{"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"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":8089,"databundleVersionId":44321,"sourceType":"competition"},{"sourceId":8172134,"sourceType":"datasetVersion","datasetId":4836684}],"dockerImageVersionId":42,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"markdown","source":"### 2018 Data Science Bowl \n다양한 의료이미지에서 세포핵의 위치와 모양을 식별할 수 있는 컴퓨터 모델을 만들어서 의료 문제를 중심으로 다양한 문제를 해결하는 것이 목표, \n\n즉, 세포핵 탐지 자동화 알고리즘을 만드는 것이 목표  이를 통해 여러 질병 치료법 개발에 도움이 될 수 있다.","metadata":{}},{"cell_type":"code","source":"import pathlib\nimport imageio\nimport numpy as np\n\n# Glob the training data and load a single image path\ntraining_paths = pathlib.Path('../input/deeplearning/stage1_train/').glob('*/images/*.png')\ntraining_sorted = sorted([x for x in training_paths])\nim_path = training_sorted[45] # 45번째 경로를 선택합니다.\nim = imageio.imread(str(im_path))","metadata":{"_cell_guid":"2b168289-7746-4255-b933-356bd04914b7","_uuid":"25fa830192ea11807891ec2f71487dbeb18364e3","execution":{"iopub.status.busy":"2024-04-21T05:56:03.849639Z","iopub.execute_input":"2024-04-21T05:56:03.850676Z","iopub.status.idle":"2024-04-21T05:56:04.966034Z","shell.execute_reply.started":"2024-04-21T05:56:03.850613Z","shell.execute_reply":"2024-04-21T05:56:04.965087Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 21 데이터셋 내 이미지의 색상 처리\n\n- 이미지 형식:\nRGB, RGBA, Grayscale 등 여러 양식의 이미지를 편의상 sickit-image의 rgb2gray 함수를 사용하여 grayscale 양식으로 통일한은 작업 \n- 간단한 처리:\n모든 이미지를 grayscale로 변환 후 일관된 형식으로 준비 \n- 원본이미지는 RGBA 형식의 컬러이미지였으나,  grayscale로 변환되면서 색상 정보가 제거 후 단일 채널의 그레이스케일 이미지로 변환 ","metadata":{"_cell_guid":"457b5a62-435d-44c5-a7d8-30367c5212f2","_uuid":"c6854798a17802a06c246173b84848011d266fda"}},{"cell_type":"code","source":"# Print the image dimensions\nprint('Original image shape: {}'.format(im.shape)) # 원본 이미지의 차원(shape)을 출력\n\n# Coerce the image into grayscale format (if not already)\nfrom skimage.color import rgb2gray #rgb2gray 함수 사용 ㅡ  RGB 이미지를 grayscale 이미지로 변환\nim_gray = rgb2gray(im)\nprint('New image shape: {}'.format(im_gray.shape))# 이미지 크리 520(높이), 696(너비),4는 채널의 수를 의미 ","metadata":{"_cell_guid":"3f389854-b251-4723-babf-9fd0d71e3805","_uuid":"3f03d9fd9cb23494b936e2ebd4535878efc2ccfd","execution":{"iopub.status.busy":"2024-04-21T05:56:04.967185Z","iopub.execute_input":"2024-04-21T05:56:04.967444Z","iopub.status.idle":"2024-04-21T05:56:04.998356Z","shell.execute_reply.started":"2024-04-21T05:56:04.967406Z","shell.execute_reply":"2024-04-21T05:56:04.997460Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"- 세포 핵 탐지 작업 \n- 왼쪽이미지는 원본이미지/ 오른쪽 이미지는 동일한 이미지를 grayscale로 변환 \n","metadata":{}},{"cell_type":"code","source":"# Now, let's plot the data\nimport matplotlib.pyplot as plt\n\nplt.figure(figsize=(10,4))\n\nplt.subplot(1,2,1)\nplt.imshow(im)\nplt.axis('off')\nplt.title('Original Image')\n\nplt.subplot(1,2,2)\nplt.imshow(im_gray, cmap='gray')\nplt.axis('off')\nplt.title('Grayscale Image')#이미지를 그레이스케일로 변환 \n\nplt.tight_layout()\nplt.show()","metadata":{"_cell_guid":"c298bcd0-2c4b-42aa-b0d1-f5b829515841","_kg_hide-input":true,"_uuid":"b1e6a69a5ae9121ff4f2f1a4fa96698f532a9375","execution":{"iopub.status.busy":"2024-04-21T05:56:04.999619Z","iopub.execute_input":"2024-04-21T05:56:04.999975Z","iopub.status.idle":"2024-04-21T05:56:05.325827Z","shell.execute_reply.started":"2024-04-21T05:56:04.999914Z","shell.execute_reply":"2024-04-21T05:56:05.324742Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"1. # 22 이미지 배경 제거 방법 \n* 이미지의 이진화를 통해, 객체(세포핵)/ 배경(세포 핵을 제외한 나머지)  분리 ","metadata":{"_cell_guid":"5f5383ed-7c97-4c27-8ea4-ac4ae35bbd25","_uuid":"0a93026712b2a2ef54412c43873ac58197636917"}},{"cell_type":"code","source":"from skimage.filters import threshold_otsu #Otsu 임계값 계산 함수\nthresh_val = threshold_otsu(im_gray) # 그레이스케일 이미지 im_gray에 대해 Otsu 알고리즘을 적용하여 최적의 임계값 thresh_val을 계산\nmask = np.where(im_gray > thresh_val, 1, 0)# im_gray 값이 thresh_val(임계값)보다 크면 1(객체), 작으면 0(배경)으로 할당\n\n# Make sure the larger portion of the mask is considered background\nif np.sum(mask==0) < np.sum(mask==1): # 마스크에서 0과 1의 개수를 비교\n    mask = np.where(mask, 0, 1) #위 조건문이 참이면, 0과 1의 값을 서로 바꾸어 배경과 객체를 올바르게 할당","metadata":{"_cell_guid":"f145ff54-8e4d-4546-9913-e695bb7d9254","_uuid":"b76b12a4b26dc1a351d717a047bd90ea6aa3f7dd","execution":{"iopub.status.busy":"2024-04-21T05:56:05.327515Z","iopub.execute_input":"2024-04-21T05:56:05.328037Z","iopub.status.idle":"2024-04-21T05:56:05.350326Z","shell.execute_reply.started":"2024-04-21T05:56:05.327923Z","shell.execute_reply":"2024-04-21T05:56:05.349377Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#임계값 시각화. \nplt.figure(figsize=(10,4))\n\nplt.subplot(1,2,1)\nim_pixels = im_gray.flatten()\nplt.hist(im_pixels,bins=50) # im_pixels 배열의 히스토그램을 50개의 bin으로 그립니다.\nplt.vlines(thresh_val, 0, 100000, linestyle='--') # Otsu 임계값을 수직선으로 표시\nplt.ylim([0,50000]) # y축의 범위를 0에서 50000으로 설정\nplt.title('Grayscale Histogram')\n\nplt.subplot(1,2,2) \nmask_for_display = np.where(mask, mask, np.nan) #마스크 값이 1인 경우에는 그대로 두고, 0인 경우에는 NaN으로 설정하여 투명\nplt.imshow(im_gray, cmap='gray') # 그레이스케일 이미지를 그립니다. \nplt.imshow(mask_for_display, cmap='rainbow', alpha=0.5) #마스크를 레인보우 컬러맵으로 그림 \nplt.axis('off')\nplt.title('Image w/ Mask')\n\nplt.show()","metadata":{"_cell_guid":"65033fba-1681-46fd-aa6d-c3e3fec09c1f","_kg_hide-input":true,"_uuid":"14792479e2019e59f6cd0388b40798debb9e34dc","execution":{"iopub.status.busy":"2024-04-21T05:56:05.352092Z","iopub.execute_input":"2024-04-21T05:56:05.352480Z","iopub.status.idle":"2024-04-21T05:56:05.740746Z","shell.execute_reply.started":"2024-04-21T05:56:05.352409Z","shell.execute_reply":"2024-04-21T05:56:05.739732Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#23 Deriving individual masks for each object\n\n이미지 내에서 배경을 제외한 모든 개체를 찾아 ndimage.label()함수를 사용하여 각 개체에 번호를 라벨링한다.\n\n해당 번호를 for문에 활용하여 각 개체 번호를 list 자료형에 담는다.","metadata":{"_cell_guid":"6b6afa37-f217-4c0b-a9d4-8e244d5384b9","_uuid":"47cc36785c751676f70dec7b25d2a4c56fe4cf0c"}},{"cell_type":"code","source":"from scipy import ndimage\nlabels, nlabels = ndimage.label(mask) # 연속된 개체끼리 라벨링, 전체 개체 개수 저장\n\n\nlabel_arrays = []\nfor label_num in range(1, nlabels+1):\n    label_mask = np.where(labels == label_num, 1, 0)# 각 인덱스에 맞는 개체만 따로 뽑아 저장\n    label_arrays.append(label_mask)\n\nprint('There are {} separate components / objects detected.'.format(nlabels))","metadata":{"_cell_guid":"5c8b868f-1b2d-4939-914f-836d0aa7b50d","_uuid":"2475893dbeabff7c165283970eebea2fd2de9bd7","execution":{"iopub.status.busy":"2024-04-21T05:56:05.742301Z","iopub.execute_input":"2024-04-21T05:56:05.742620Z","iopub.status.idle":"2024-04-21T05:56:05.819327Z","shell.execute_reply.started":"2024-04-21T05:56:05.742559Z","shell.execute_reply":"2024-04-21T05:56:05.818424Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#24 Create a random colormap\nfrom matplotlib.colors import ListedColormap\nrand_cmap = ListedColormap(np.random.rand(256,3))\n\nlabels_for_display = np.where(labels > 0, labels, np.nan)\nplt.imshow(im_gray, cmap='gray')\nplt.imshow(labels_for_display, cmap=rand_cmap)\nplt.axis('off')\nplt.title('Labeled Cells ({} Nuclei)'.format(nlabels))\nplt.show()","metadata":{"_cell_guid":"14e0c916-d8c9-42df-acd0-3a89f4d0d072","_kg_hide-input":true,"_uuid":"53cb03a6457b538ad803ffe4fc1159b085f2a72e","execution":{"iopub.status.busy":"2024-04-21T05:56:05.820921Z","iopub.execute_input":"2024-04-21T05:56:05.821286Z","iopub.status.idle":"2024-04-21T05:56:05.990458Z","shell.execute_reply.started":"2024-04-21T05:56:05.821215Z","shell.execute_reply":"2024-04-21T05:56:05.989619Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![image.png](attachment:ed945084-ddd5-4a07-8442-2151e45b7e13.png)","metadata":{},"attachments":{"ed945084-ddd5-4a07-8442-2151e45b7e13.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"위와 같이 76개의 세포핵으로 구분된 사진에서 두가지 문제점이 확인된다.\n","metadata":{}},{"cell_type":"markdown","source":"세포핵으로 구분된 1 픽셀짜리 점들 (우측 상단  원 참고)\n둘 이상의 세포핵이 겹쳐 하나로 인식된 경우 (상단 중앙 빨간 원 참고)\n1번 문제를 해결하기 위해 ndimage.find_objects 메서드가 라벨링 된 개체의 좌표 정보를 리스트로 반환하기 때문에 이를 활용하여 각각 추가 작업을 진행할 수 있다.\n\n이 작업을 통해 76개 중 16개의 너무 작은 개체값을 제외하여 총 60개의 개체값(세포핵)으로 분리했다","metadata":{"_uuid":"a510167c5bc06a6843a4255d8e3505df23cb95f1"}},{"cell_type":"code","source":"# 25 60개의 개체값(세포핵)으로 분리\nfor label_ind, label_coords in enumerate(ndimage.find_objects(labels)):\n    cell = im_gray[label_coords]\n    \n    # Check if the label size is too small\n    if np.product(cell.shape) < 10: \n        print('Label {} is too small! Setting to 0.'.format(label_ind))\n        mask = np.where(labels==label_ind+1, 0, mask)\n\n# Regenerate the labels\nlabels, nlabels = ndimage.label(mask)\nprint('There are now {} separate components / objects detected.'.format(nlabels))","metadata":{"_uuid":"34ab91ec5564752381fe300645aa4e4732288e14","execution":{"iopub.status.busy":"2024-04-21T05:56:05.992057Z","iopub.execute_input":"2024-04-21T05:56:05.992485Z","iopub.status.idle":"2024-04-21T05:56:06.034631Z","shell.execute_reply.started":"2024-04-21T05:56:05.992411Z","shell.execute_reply":"2024-04-21T05:56:06.033352Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"#26\nfig, axes = plt.subplots(1,6, figsize=(10,6))\n\nfor ii, obj_indices in enumerate(ndimage.find_objects(labels)[0:6]):\n    cell = im_gray[obj_indices]\n    axes[ii].imshow(cell, cmap='gray')\n    axes[ii].axis('off')\n    axes[ii].set_title('Label #{}\\nSize: {}'.format(ii+1, cell.shape))\n\nplt.tight_layout()\nplt.show()","metadata":{"_kg_hide-input":true,"_uuid":"28cc9fda0ca6b5e0060591ca9ed7f41d52919a20","execution":{"iopub.status.busy":"2024-04-21T05:56:06.036646Z","iopub.execute_input":"2024-04-21T05:56:06.037020Z","iopub.status.idle":"2024-04-21T05:56:06.463944Z","shell.execute_reply.started":"2024-04-21T05:56:06.036953Z","shell.execute_reply":"2024-04-21T05:56:06.463113Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Label #2와 같이 두 개의 세포핵이 겹쳐지며 하나로 인식되는 문제를 해결하기 위해 마스크를 축소하여 세포 간의 차이를 벌릴 수 있는지 확인한다. 이를 mask erosion(마스크 침식)이라고 부른다. 확인 후에는 원래 비율을 회복하기 위해 다시 확장시킬 수 있다.","metadata":{"_uuid":"c44a9f56a96eb0b65464f1b20a3738dc4051826d"}},{"cell_type":"code","source":"# 27 Get the object indices, and perform a binary opening procedure\ntwo_cell_indices = ndimage.find_objects(labels)[1]\ncell_mask = mask[two_cell_indices]\ncell_mask_opened = ndimage.binary_opening(cell_mask, iterations=8) #이진 오프닝(Binary Opening) 수행","metadata":{"_uuid":"aea9549db2f19d7551fb2a021bd877572c8555fb","execution":{"iopub.status.busy":"2024-04-21T05:56:06.465429Z","iopub.execute_input":"2024-04-21T05:56:06.465807Z","iopub.status.idle":"2024-04-21T05:56:06.476752Z","shell.execute_reply.started":"2024-04-21T05:56:06.465746Z","shell.execute_reply":"2024-04-21T05:56:06.475748Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#28 결과물 수행 \nfig, axes = plt.subplots(1,4, figsize=(12,4))\n\naxes[0].imshow(im_gray[two_cell_indices], cmap='gray')\naxes[0].set_title('Original object')\naxes[1].imshow(mask[two_cell_indices], cmap='gray')\naxes[1].set_title('Original mask')\naxes[2].imshow(cell_mask_opened, cmap='gray')\naxes[2].set_title('Opened mask')\naxes[3].imshow(im_gray[two_cell_indices]*cell_mask_opened, cmap='gray')\naxes[3].set_title('Opened object')\n\n\nfor ax in axes:\n    ax.axis('off')\nplt.tight_layout()\nplt.show()","metadata":{"_kg_hide-input":true,"_uuid":"cc2b1241e08673952bc6da9f4d05b95608a2c1e8","execution":{"iopub.status.busy":"2024-04-21T05:56:06.478367Z","iopub.execute_input":"2024-04-21T05:56:06.478769Z","iopub.status.idle":"2024-04-21T05:56:06.822972Z","shell.execute_reply.started":"2024-04-21T05:56:06.478705Z","shell.execute_reply":"2024-04-21T05:56:06.822124Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 29 Convert each labeled object to Run Line Encoding\n마지막으로 각 label_mask를 \"Run Line Encoded\" 문자열로 인코딩해야 한다. 기본적으로 array를 순회하며 mask의 일부인 픽셀을 찾으면 인덱싱하고, 해당 mask가 몇 개의 픽셀로 이루어져 있는지 세어본다. 새로운 mask의 시작점 픽셀을 찾을 때마다 이것을 반복한다.","metadata":{"_cell_guid":"ad2b865b-678c-41de-9db1-133462f20377","_uuid":"9f417451b0234f5f714be676cd5a217873b8fe98"}},{"cell_type":"code","source":"def rle_encoding(x): # x 인자로 주어진 바이너리 배열(마스크)을 Run Line Encoding(RLE)으로 변환\n    '''\n    x: numpy array of shape (height, width), 1 - mask, 0 - background\n    Returns run length as list\n    '''\n    dots = np.where(x.T.flatten()==1)[0] # .T sets Fortran order down-then-right\n    run_lengths = []\n    prev = -2\n    for b in dots:\n        if (b>prev+1): run_lengths.extend((b+1, 0))\n        run_lengths[-1] += 1\n        prev = b\n    return \" \".join([str(i) for i in run_lengths])\n\nprint('RLE Encoding for the current mask is: {}'.format(rle_encoding(label_mask)))","metadata":{"_cell_guid":"29ea6b76-766c-487d-8f88-d6b0e288e94f","_uuid":"4d64b21aa0f26d177863b3cae7fb91e80bc39180","execution":{"iopub.status.busy":"2024-04-21T05:56:06.824262Z","iopub.execute_input":"2024-04-21T05:56:06.824526Z","iopub.status.idle":"2024-04-21T05:56:06.844110Z","shell.execute_reply.started":"2024-04-21T05:56:06.824481Z","shell.execute_reply":"2024-04-21T05:56:06.843155Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 30 Combine it into a single function\n하나의 이미지를 처리하는 기본적인 단계들을 살펴보았으니 이것을 하나의 함수로 통합할 수 있을 것이다.\n\n이 함수는 하나의 이미지 경로를 입력받아 위에서 살펴본 프로세스를 수행하며 발견되는 각 mask의 RLE 문자열을 데이터 프레임으로 반환할 것이다.\n\n또한, 데이터셋에 포함된 모든 이미지에 대해 위의 처리과정을 거쳐 하나의 데이터프레임으로 반환하는 함수도 만든다.","metadata":{"_cell_guid":"42577f67-935c-4269-b6ef-d7c166723779","_uuid":"dfcd51eaaa5fdf144ce7e9de6397e8abea0b50b9"}},{"cell_type":"code","source":"import pandas as pd\n\ndef analyze_image(im_path): #analyze_list_of_images 함수(여러이미지 경로를 받아 호출하고 그 결과물을 하나의 데이터프레임으로 반환)\n    '''\n    Take an image_path (pathlib.Path object), preprocess and label it, extract the RLE strings \n    and dump it into a Pandas DataFrame.\n    '''\n    # Read in data and convert to grayscale\n    im_id = im_path.parts[-3]\n    im = imageio.imread(str(im_path))\n    im_gray = rgb2gray(im)\n    \n    # Mask out background and extract connected objects\n    thresh_val = threshold_otsu(im_gray)\n    mask = np.where(im_gray > thresh_val, 1, 0)\n    if np.sum(mask==0) < np.sum(mask==1):\n        mask = np.where(mask, 0, 1)    \n        labels, nlabels = ndimage.label(mask)\n    labels, nlabels = ndimage.label(mask)\n    \n    # Loop through labels and add each to a DataFrame\n    im_df = pd.DataFrame()\n    for label_num in range(1, nlabels+1):\n        label_mask = np.where(labels == label_num, 1, 0)\n        if label_mask.flatten().sum() > 10:\n            rle = rle_encoding(label_mask)\n            s = pd.Series({'ImageId': im_id, 'EncodedPixels': rle})\n            im_df = im_df.append(s, ignore_index=True)\n    \n    return im_df\n\n\ndef analyze_list_of_images(im_path_list):\n    '''\n    Takes a list of image paths (pathlib.Path objects), analyzes each,\n    and returns a submission-ready DataFrame.'''\n    all_df = pd.DataFrame()\n    for im_path in im_path_list:\n        im_df = analyze_image(im_path)\n        all_df = all_df.append(im_df, ignore_index=True)\n    \n    return all_df","metadata":{"_cell_guid":"0d32b900-8f40-4eff-8465-0f715f24faf6","_uuid":"944f216004fa635201470b8eaa28a446d9679516","execution":{"iopub.status.busy":"2024-04-21T05:56:06.845631Z","iopub.execute_input":"2024-04-21T05:56:06.845993Z","iopub.status.idle":"2024-04-21T05:56:06.927430Z","shell.execute_reply.started":"2024-04-21T05:56:06.845927Z","shell.execute_reply":"2024-04-21T05:56:06.926545Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"testing = pathlib.Path('../input/stage1_test/').glob('*/images/*.png')\ndf = analyze_list_of_images(list(testing))\ndf.to_csv('submission.csv', index=None)","metadata":{"_cell_guid":"58b07c91-855f-4e6d-a9d8-457750d9ad4a","_uuid":"b7e21d3b95d7936904bfed088a880a5c78f0fa18","execution":{"iopub.status.busy":"2024-04-21T05:56:06.928732Z","iopub.execute_input":"2024-04-21T05:56:06.929048Z","iopub.status.idle":"2024-04-21T05:56:06.940700Z","shell.execute_reply.started":"2024-04-21T05:56:06.928991Z","shell.execute_reply":"2024-04-21T05:56:06.939918Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"_uuid":"51fdaa6a207574dc67d497eca81eb8d757021fe4","collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":null,"outputs":[]}]}