{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nimport os\nprint(os.listdir(\"../input\"))\n\n# Any results you write to the current directory are saved as output.","execution_count":170,"outputs":[{"output_type":"stream","text":"['test', 'train', 'sample_submission_file.csv', 'extraimages']\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"%matplotlib inline\nfrom matplotlib import pyplot as plt\nimport cv2\nimage=cv2.imread('../input/train/train/cmd/train-cmd-1992.jpg')\nplt.imshow(image)\nplt.show()","execution_count":171,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"gray=cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)\nfirst_gray = cv2.GaussianBlur(gray, (5, 5), 0)\nret, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)\n# noise removal\nkernel = np.ones((3,3),np.uint8)\nopening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel, iterations = 2)\n\n# sure background area\nsure_bg = cv2.dilate(opening,kernel,iterations=3)\n\n# Finding sure foreground area\ndist_transform = cv2.distanceTransform(opening,cv2.DIST_L2,5)\nret, sure_fg = cv2.threshold(dist_transform,0.7*dist_transform.max(),255,0)\n\n# Finding unknown region\nsure_fg = np.uint8(sure_fg)\nunknown = cv2.subtract(sure_bg,sure_fg)\nplt.imshow(sure_bg)\n#plt.imshow(first_gray)\nplt.show()\n","execution_count":172,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Marker labelling\nret, markers = cv2.connectedComponents(sure_fg)\n# Add one to all labels so that sure background is not 0, but 1\nmarkers = markers+1\n# Now, mark the region of unknown with zero\nmarkers[unknown==255] = 0\nplt.imshow(markers)\nplt.show()\nmarkers = cv2.watershed(image,markers)\nimage[markers == -1] = [255,0,0]\nplt.imshow(image)\nplt.show()\n","execution_count":173,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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size 432x288 with 1 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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#edges = cv2.Sobel(image,cv2.CV_64F,1,0, ksize=1)\nedges=cv2.Canny(image,200,300)\nplt.subplot(121),plt.imshow(first_gray,cmap = 'gray')\nplt.title('Original Image'), plt.xticks([]), plt.yticks([])\nplt.subplot(122),plt.imshow(edges,cmap = 'gray')\nplt.title('Edge Image'), plt.xticks([]), plt.yticks([])\nplt.show()","execution_count":174,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 2 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"np.mean(edges)\nnp.mean(image)","execution_count":175,"outputs":[{"output_type":"execute_result","execution_count":175,"data":{"text/plain":"111.69728133333334"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"second submission"},{"metadata":{"trusted":true},"cell_type":"code","source":"import cv2 \nimport os \nimport numpy as np \nfrom random import shuffle \nfrom tqdm import tqdm ","execution_count":177,"outputs":[]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"TRAIN_DIR = '../input/train/train/cgm'\nTEST_DIR = '../input/test/test/0'\n#IMG_SIZE = 96\nLR = 1e-3\nMODEL_NAME = 'cassava-{}-{}.model'.format(LR, '6conv-basic') \nclass_c=''\n\n","execution_count":178,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import numpy.ma as ma\ndef create_train_data(): \n training_data = [] \n for img in tqdm(os.listdir(TRAIN_DIR)): \n             # labeling the images \n            #label = label_img(img) \n   # grayc=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n    #edgedet=cv2.Canny(np.array(grayc),200,300,10)\n    #m=np.ma.mean(img)\n    path = os.path.join(TRAIN_DIR, img) \n              # loading the image from the path and then converting  into gray\n    img = cv2.imread(path, cv2.IMREAD_GRAYSCALE) \n              # resizing the image \n    img = cv2.resize(img, (100,100)) \n              # forming the training data list with numpy array of the images \n            #training_data.append([np.array(img), np.array(label)]) \n    label=os.path.basename(TRAIN_DIR)\n    training_data.append([np.array(np.mean(img)), np.array(label)])\n            \n               # shuffling of the training data to preserve the random state of our data \nshuffle(training_data) \n         # saving our trained data for further uses if required \nnp.save('train_data.npy', training_data) \nprint(*training_data, sep=\",\")\nreturn training_data \n","execution_count":179,"outputs":[{"output_type":"stream","text":"[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', 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0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 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 0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   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      ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 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0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   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0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 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 0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   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0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   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 0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   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      ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 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 0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   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0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 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dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 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 0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   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      ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 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 0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   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0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 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 0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   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      ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 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0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   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0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 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  0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 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0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   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 0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   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      ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 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 0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   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0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 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dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 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 0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   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      ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 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 0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   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dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')],[array([[  0,   0,   0, ...,   0,   0,   0],\n       [  0, 255, 255, ..., 255, 255,   0],\n       [  0, 255,   0, ...,   0, 255,   0],\n       ...,\n       [  0, 255,   0, ...,   0, 255,   0],\n       [  0, 255,   0, ..., 255, 255,   0],\n       [  0,   0, 255, ...,   0,   0,   0]], dtype=uint8), array('cgm', dtype='<U3')]\n","name":"stdout"},{"output_type":"error","ename":"SyntaxError","evalue":"'return' outside function (<ipython-input-179-847d48151267>, line 25)","traceback":["\u001b[0;36m  File \u001b[0;32m\"<ipython-input-179-847d48151267>\"\u001b[0;36m, line \u001b[0;32m25\u001b[0m\n\u001b[0;31m    return training_data\u001b[0m\n\u001b[0m                         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m 'return' outside function\n"]}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(os.path.basename(TRAIN_DIR))","execution_count":181,"outputs":[{"output_type":"stream","text":"cgm\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"import numpy.ma as ma\ndef process_test_data(): \n    testing_data = [] \n    for img in tqdm(os.listdir(TEST_DIR)): \n         #edge_test=cv2.Canny(np.asarray(img),200,300)\n         path = os.path.join(TEST_DIR, img) \n         img_num = img.split('.')[0] \n         img = cv2.imread(path, cv2.IMREAD_GRAYSCALE) \n         img = cv2.resize(img, (666, 500)) \n         testing_data.append([np.array(np.ma.mean(img)), np.array(img_num)]) \n         \n    shuffle(testing_data) \n    np.save('test_data.npy', testing_data) \n    return testing_data ","execution_count":182,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#executing model\ntrain_data = create_train_data() \ntest_data = process_test_data() ","execution_count":183,"outputs":[{"output_type":"stream","text":"100%|██████████| 773/773 [00:05<00:00, 152.71it/s]\n100%|██████████| 3774/3774 [00:31<00:00, 117.95it/s]\n","name":"stderr"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"import tflearn \nfrom tflearn.layers.conv import conv_2d, max_pool_2d \nfrom tflearn.layers.core import input_data, dropout, fully_connected \nfrom tflearn.layers.estimator import regression \n  \nimport tensorflow as tf \ntf.reset_default_graph() \nconvnet = input_data(shape =[None, 666, 500, 1], name ='input') \n  \nconvnet = conv_2d(convnet, 32, 5, activation ='relu') \nconvnet = max_pool_2d(convnet, 5) \n  \nconvnet = conv_2d(convnet, 64, 5, activation ='relu') \nconvnet = max_pool_2d(convnet, 5) ","execution_count":184,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"convnet = conv_2d(convnet, 128, 5, activation ='relu') \nconvnet = max_pool_2d(convnet, 5) \n  \nconvnet = conv_2d(convnet, 64, 5, activation ='relu') \nconvnet = max_pool_2d(convnet, 5) \n  \nconvnet = conv_2d(convnet, 32, 5, activation ='relu') \nconvnet = max_pool_2d(convnet, 5) ","execution_count":185,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"convnet = fully_connected(convnet, 1024, activation ='relu') \nconvnet = dropout(convnet, 0.8) ","execution_count":187,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"convnet = fully_connected(convnet, 2, activation ='softmax') \nconvnet = regression(convnet, optimizer ='adam', learning_rate = LR, \n      loss ='categorical_crossentropy', name ='targets') ","execution_count":188,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = tflearn.DNN(convnet, tensorboard_dir ='log')","execution_count":189,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.4","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}