{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import numpy as np \nimport pandas as pd\nimport tensorflow as tf\nimport os\nimport matplotlib.pyplot as plt\nfrom PIL import Image, ImageDraw, ImageEnhance\nimport albumentations as albu\nfrom tqdm.notebook import tqdm","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-03-19T20:08:57.179655Z","iopub.execute_input":"2022-03-19T20:08:57.179905Z","iopub.status.idle":"2022-03-19T20:09:02.295213Z","shell.execute_reply.started":"2022-03-19T20:08:57.179875Z","shell.execute_reply":"2022-03-19T20:09:02.294445Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"labels = pd.read_csv('../input/global-wheat-detection/train.csv')\nlabels.head()","metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","execution":{"iopub.status.busy":"2022-03-19T20:09:02.296932Z","iopub.execute_input":"2022-03-19T20:09:02.297338Z","iopub.status.idle":"2022-03-19T20:09:02.567094Z","shell.execute_reply.started":"2022-03-19T20:09:02.297292Z","shell.execute_reply":"2022-03-19T20:09:02.566235Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def group_boxes(group):\n    boundaries = group['bbox'].str.split(',', expand=True)\n    boundaries[0] = boundaries[0].str.slice(start=1)\n    boundaries[3] = boundaries[3].str.slice(stop=-1)\n    \n    return boundaries.values.astype(float)\n\nlabels = labels.groupby('image_id').apply(group_boxes)","metadata":{"execution":{"iopub.status.busy":"2022-03-19T20:09:02.568267Z","iopub.execute_input":"2022-03-19T20:09:02.568659Z","iopub.status.idle":"2022-03-19T20:09:08.622233Z","shell.execute_reply.started":"2022-03-19T20:09:02.56863Z","shell.execute_reply":"2022-03-19T20:09:08.621414Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"labels['b6ab77fd7'][0:5]","metadata":{"execution":{"iopub.status.busy":"2022-03-19T20:09:08.623527Z","iopub.execute_input":"2022-03-19T20:09:08.623887Z","iopub.status.idle":"2022-03-19T20:09:08.633677Z","shell.execute_reply.started":"2022-03-19T20:09:08.623852Z","shell.execute_reply":"2022-03-19T20:09:08.632751Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Vì data khá là nhỏ nên chủ yếu mình chia phần lớn cho training, val chỉ 30 images","metadata":{}},{"cell_type":"code","source":"train_image_ids = np.unique(labels.index.values)[0:3343]\nval_image_ids = np.unique(labels.index.values)[3343:3373]","metadata":{"execution":{"iopub.status.busy":"2022-03-19T20:09:08.637539Z","iopub.execute_input":"2022-03-19T20:09:08.638219Z","iopub.status.idle":"2022-03-19T20:09:08.647533Z","shell.execute_reply.started":"2022-03-19T20:09:08.638175Z","shell.execute_reply":"2022-03-19T20:09:08.64681Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Tác giả có bảo dựa theo kinh nghiệm khi để 1024 thì độ chính xác không quá nhiều để đánh đổi lại thời gian chạy khá là lâu, nên resize về 256 để chạy nhanh hơn","metadata":{}},{"cell_type":"code","source":"def load_image(image_id):\n    image = Image.open('../input/global-wheat-detection/train/' + image_id + \".jpg\")\n    image = image.resize((256, 256))\n    \n    return np.asarray(image)","metadata":{"execution":{"iopub.status.busy":"2022-03-19T20:09:08.650887Z","iopub.execute_input":"2022-03-19T20:09:08.65141Z","iopub.status.idle":"2022-03-19T20:09:08.657378Z","shell.execute_reply.started":"2022-03-19T20:09:08.651381Z","shell.execute_reply":"2022-03-19T20:09:08.656404Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_pixels = {}\ntrain_labels = {}\n\nfor image_id in tqdm(train_image_ids):\n    train_pixels[image_id] = load_image(image_id)\n    train_labels[image_id] = labels[image_id].copy() / 4","metadata":{"execution":{"iopub.status.busy":"2022-03-19T20:09:08.659562Z","iopub.execute_input":"2022-03-19T20:09:08.659989Z","iopub.status.idle":"2022-03-19T20:11:21.544145Z","shell.execute_reply.started":"2022-03-19T20:09:08.659952Z","shell.execute_reply":"2022-03-19T20:11:21.543284Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"val_pixels = {}\nval_labels = {}\n\nfor image_id in tqdm(val_image_ids):\n    val_pixels[image_id] = load_image(image_id)    \n    val_labels[image_id] = labels[image_id].copy() / 4","metadata":{"execution":{"iopub.status.busy":"2022-03-19T20:11:21.545645Z","iopub.execute_input":"2022-03-19T20:11:21.546232Z","iopub.status.idle":"2022-03-19T20:11:21.998943Z","shell.execute_reply.started":"2022-03-19T20:11:21.546194Z","shell.execute_reply":"2022-03-19T20:11:21.997967Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Visualise images\n\nVisualize ảnh cùng với boundingbox của nó","metadata":{}},{"cell_type":"code","source":"def draw_bboxes(image_id, bboxes, source='train'):  \n    image = Image.open('../input/global-wheat-detection/' + source +'/' + image_id + \".jpg\")\n    image = image.resize((256,256))\n    \n    draw = ImageDraw.Draw(image)\n            \n    for bbox in bboxes:\n        draw_bbox(draw, bbox)\n    \n    return np.asarray(image)\n\n\ndef draw_bbox(draw, bbox):\n    x, y, width, height = bbox\n    draw.rectangle([x, y, x + width, y + height], width=2, outline='red')","metadata":{"execution":{"iopub.status.busy":"2022-03-19T20:11:22.000542Z","iopub.execute_input":"2022-03-19T20:11:22.000904Z","iopub.status.idle":"2022-03-19T20:11:22.011373Z","shell.execute_reply.started":"2022-03-19T20:11:22.000865Z","shell.execute_reply":"2022-03-19T20:11:22.010509Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"I'll also add a wrapper function to call this function multiple times for multiple images.","metadata":{}},{"cell_type":"code","source":"def show_images(image_ids, bboxes, source='train'):\n    pixels = []\n    \n    for image_id in image_ids:\n        pixels.append(\n            draw_bboxes(image_id, bboxes[image_id], source)\n        )\n    \n    num_of_images = len(image_ids)\n    fig, axes = plt.subplots(\n        1, \n        num_of_images, \n        figsize=(5 * num_of_images, 5 * num_of_images)\n    )\n    \n    for i, image_pixels in enumerate(pixels):\n        axes[i].imshow(image_pixels)","metadata":{"execution":{"iopub.status.busy":"2022-03-19T20:11:22.012663Z","iopub.execute_input":"2022-03-19T20:11:22.013224Z","iopub.status.idle":"2022-03-19T20:11:22.021665Z","shell.execute_reply.started":"2022-03-19T20:11:22.013187Z","shell.execute_reply":"2022-03-19T20:11:22.020644Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"show_images(train_image_ids[0:4], train_labels)","metadata":{"execution":{"iopub.status.busy":"2022-03-19T20:11:22.023008Z","iopub.execute_input":"2022-03-19T20:11:22.023618Z","iopub.status.idle":"2022-03-19T20:11:22.731459Z","shell.execute_reply.started":"2022-03-19T20:11:22.023582Z","shell.execute_reply":"2022-03-19T20:11:22.730424Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Clean bounding boxes\n\nCó một số lượng nhỏ các bbox không bound a head of wheat. Các bbox này đủ nhỏ để model học được cách detect nhưng cũng gây ra 1 vấn đề nhỏ là gây ra sai lệch, giảm accuracy. Do đó xóa một vài bb quá nhỏ hoặc quá lớn","metadata":{}},{"cell_type":"code","source":"tiny_bboxes = []\n\nfor i, image_id in enumerate(train_image_ids):\n    for label in train_labels[image_id]:\n        if label[2] * label[3] <= 10 and label[2] * label[3] != 0:\n            tiny_bboxes.append(i)\n\n            \nprint(str(len(tiny_bboxes)) + ' tiny bounding boxes found')","metadata":{"execution":{"iopub.status.busy":"2022-03-19T20:11:22.732593Z","iopub.execute_input":"2022-03-19T20:11:22.732905Z","iopub.status.idle":"2022-03-19T20:11:22.952696Z","shell.execute_reply.started":"2022-03-19T20:11:22.732874Z","shell.execute_reply":"2022-03-19T20:11:22.95201Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"huge_bboxes = []\n\nfor i, image_id in enumerate(train_image_ids):\n    for label in train_labels[image_id]:\n        if label[2] * label[3] > 8000:\n            huge_bboxes.append(i)\n\n            \nprint(str(len(huge_bboxes)) + ' huge bounding boxes found')","metadata":{"execution":{"iopub.status.busy":"2022-03-19T20:11:22.953875Z","iopub.execute_input":"2022-03-19T20:11:22.954259Z","iopub.status.idle":"2022-03-19T20:11:23.176259Z","shell.execute_reply.started":"2022-03-19T20:11:22.954219Z","shell.execute_reply":"2022-03-19T20:11:23.175336Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Visualize để thấy rỏ các tiny và big. Các big bbox thì dễ nhìn nhưng tiny thì khó phát hiện","metadata":{}},{"cell_type":"code","source":"show_images(train_image_ids[562:564], train_labels)","metadata":{"execution":{"iopub.status.busy":"2022-03-19T20:11:23.178082Z","iopub.execute_input":"2022-03-19T20:11:23.178456Z","iopub.status.idle":"2022-03-19T20:11:23.558236Z","shell.execute_reply.started":"2022-03-19T20:11:23.178419Z","shell.execute_reply":"2022-03-19T20:11:23.557411Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def clean_labels(train_image_ids, train_labels):\n    good_labels = {}\n    \n    for i, image_id in enumerate(train_image_ids):\n        good_labels[image_id] = []\n        \n        for j, label in enumerate(train_labels[image_id]):\n\n            # remove huge bbox\n            if label[2] * label[3] > 8000 and i not in [1079, 1371, 2020]:\n                continue\n\n            # remove tiny bbox\n            elif label[2] < 5 or label[3] < 5:\n                continue\n                \n            else:\n                good_labels[image_id].append(\n                    train_labels[image_id][j]\n                )\n                \n    return good_labels\n\ntrain_labels = clean_labels(train_image_ids, train_labels)","metadata":{"execution":{"iopub.status.busy":"2022-03-19T20:11:23.55988Z","iopub.execute_input":"2022-03-19T20:11:23.560234Z","iopub.status.idle":"2022-03-19T20:11:24.042263Z","shell.execute_reply.started":"2022-03-19T20:11:23.560197Z","shell.execute_reply":"2022-03-19T20:11:24.041481Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Data pipeline\n\n\n- define the size of the dataset. Keras needs this to work out how long an epoch is.\n- shuffle the dataset.\n- get an image and augment it to add variety to the dataset. This includes amending bounding boxes when a head of wheat has changed in the image.\n- reshape the bounding boxes to a label grid.","metadata":{}},{"cell_type":"code","source":"class DataGenerator(tf.keras.utils.Sequence):\n\n    def __init__(self, image_ids, image_pixels, labels=None, batch_size=1, shuffle=False, augment=False):\n        self.image_ids = image_ids\n        self.image_pixels = image_pixels\n        self.labels = labels\n        self.batch_size = batch_size\n        self.shuffle = shuffle\n        self.augment = augment\n        self.on_epoch_end()\n        \n        self.image_grid = self.form_image_grid()\n        \n        \n    def form_image_grid(self):    \n        image_grid = np.zeros((32, 32, 4))\n\n        # x, y, width, height\n        cell = [0, 0, 256 / 32, 256 / 32] \n\n        for i in range(0, 32):\n            for j in range(0, 32):\n                image_grid[i,j] = cell\n\n                cell[0] = cell[0] + cell[2]\n\n            cell[0] = 0\n            cell[1] = cell[1] + cell[3]\n\n        return image_grid","metadata":{"execution":{"iopub.status.busy":"2022-03-19T20:11:24.043764Z","iopub.execute_input":"2022-03-19T20:11:24.044114Z","iopub.status.idle":"2022-03-19T20:11:24.054765Z","shell.execute_reply.started":"2022-03-19T20:11:24.044077Z","shell.execute_reply":"2022-03-19T20:11:24.053667Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def __len__(self):\n    return int(np.floor(len(self.image_ids) / self.batch_size))\n\n\ndef on_epoch_end(self):\n    self.indexes = np.arange(len(self.image_ids))\n\n    if self.shuffle == True:\n        np.random.shuffle(self.indexes)\n\n\nDataGenerator.__len__ = __len__\nDataGenerator.on_epoch_end = on_epoch_end","metadata":{"execution":{"iopub.status.busy":"2022-03-19T20:11:24.056574Z","iopub.execute_input":"2022-03-19T20:11:24.057087Z","iopub.status.idle":"2022-03-19T20:11:24.067206Z","shell.execute_reply.started":"2022-03-19T20:11:24.057048Z","shell.execute_reply":"2022-03-19T20:11:24.066508Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Data augmentation:\n\n- **random sized crop:** The model needs to be able to detect a wheat head regardless of how close or far away the head is to the camera. To produce more zoom levels in the dataset the crop method will take a portion of the image and zoom in to create a new image with larger wheat heads.\n- **flip amd rotate**: The wheat heads can point in any direction. To create more examples of wheat heads pointing in different directions the image will randomly be flipped both horizontally and vertically or rotated.\n- **hue saturation and brightness:** these are various methods that will alter the lighting of the image which will help to create different lighting scenarios. This helps as the test pictures are from various countries each with their own lighting levels.\n- **noise:** Some wheat heads aren't quite in focus. Adding some noise to the images helps to catch these wheat heads while also forcing the model to learn more abstract wheat head shapes. This helps a lot with over-fitting.\n- **cutout**: randomly remove small squares of pixels in the image. This prevents the model simply memorizing certain wheat heads and instead forces it to learn the patterns that represent a wheat head.\n- **clahe:** this is a must have. In many images the wheat heads are a similar colour to the grass in the background making it tricky for the model to differentiate between them. CLAHE helps to exemplify the colour difference between the two.\n- **grey scale:** I found that there were a few images with a yellow/gold tint. My model was learning to detect wheat heads without a tint (as most images do not contain a tint) and was really struggling to detect anything on the yellow images. By converting all images to grey scale the model is forced to ignore these tints making it much more effective at identifying wheat heads regardless of tint.\n\nNgoài ra sử dụng CLAHE để làm nổi bật chi tiết hơn, cụ thể là nổi các wheat lên","metadata":{}},{"cell_type":"markdown","source":"![image.png](attachment:34605914-bbb2-497d-919e-ee863431ebbd.png)","metadata":{},"attachments":{"34605914-bbb2-497d-919e-ee863431ebbd.png":{"image/png":"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"}}},{"cell_type":"code","source":"DataGenerator.train_augmentations = albu.Compose([\n        albu.RandomSizedCrop(\n            min_max_height=(200, 200), \n            height=256, \n            width=256, \n            p=0.8\n        ),\n        albu.OneOf([\n            albu.Flip(),\n            albu.RandomRotate90(),\n        ], p=1),\n        albu.OneOf([\n            albu.HueSaturationValue(),\n            albu.RandomBrightnessContrast()\n        ], p=1),\n        albu.OneOf([\n            albu.GaussNoise(),\n            albu.GlassBlur(),\n            albu.ISONoise(),\n            albu.MultiplicativeNoise(),\n        ], p=0.5),\n        albu.Cutout(\n            num_holes=8, \n            max_h_size=16, \n            max_w_size=16, \n            fill_value=0, \n            p=0.5\n        ),\n        albu.CLAHE(p=1),\n        albu.ToGray(p=1),\n    ], \n    bbox_params={'format': 'coco', 'label_fields': ['labels']})\n\nDataGenerator.val_augmentations = albu.Compose([\n    albu.CLAHE(p=1),\n    albu.ToGray(p=1),\n])","metadata":{"execution":{"iopub.status.busy":"2022-03-19T20:11:24.068258Z","iopub.execute_input":"2022-03-19T20:11:24.068563Z","iopub.status.idle":"2022-03-19T20:11:24.081107Z","shell.execute_reply.started":"2022-03-19T20:11:24.068538Z","shell.execute_reply":"2022-03-19T20:11:24.080169Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The next functions load an image and the corresponding bounding boxes depending on randomly picked image ids. As well as loading the images the above augmentations are added to an image as it is loaded. As the albumentaitons library was used to apply these augmentations I get the bounding boxes re-sized for free.","metadata":{}},{"cell_type":"code","source":"def __getitem__(self, index):\n    indexes = self.indexes[index * self.batch_size:(index + 1) * self.batch_size]\n\n    batch_ids = [self.image_ids[i] for i in indexes]\n\n    X, y = self.__data_generation(batch_ids)\n\n    return X, y\n\n\ndef __data_generation(self, batch_ids):\n    X, y = [], []\n\n    # Generate data\n    for i, image_id in enumerate(batch_ids):\n        pixels = self.image_pixels[image_id]\n        bboxes = self.labels[image_id]\n\n        if self.augment:     \n            pixels, bboxes = self.augment_image(pixels, bboxes)\n        else:\n            pixels = self.contrast_image(pixels)\n            bboxes = self.form_label_grid(bboxes)\n\n        X.append(pixels)\n        y.append(bboxes)\n\n    return np.array(X), np.array(y)\n\n\ndef augment_image(self, pixels, bboxes):\n    bbox_labels = np.ones(len(bboxes))\n\n    aug_result = self.train_augmentations(image=pixels, bboxes=bboxes, labels=bbox_labels)\n\n    bboxes = self.form_label_grid(aug_result['bboxes'])\n\n    return np.array(aug_result['image']) / 255, bboxes\n\n\ndef contrast_image(self, pixels):        \n    aug_result = self.val_augmentations(image=pixels)\n    return np.array(aug_result['image']) / 255\n\n\nDataGenerator.__getitem__ = __getitem__\nDataGenerator.__data_generation = __data_generation\nDataGenerator.augment_image = augment_image\nDataGenerator.contrast_image = contrast_image","metadata":{"execution":{"iopub.status.busy":"2022-03-20T08:50:44.119274Z","iopub.execute_input":"2022-03-20T08:50:44.119546Z","iopub.status.idle":"2022-03-20T08:50:44.158543Z","shell.execute_reply.started":"2022-03-20T08:50:44.119518Z","shell.execute_reply":"2022-03-20T08:50:44.157382Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The final part of the data generator class re-shapes the bounding box labels. It's worth mentioning here that there are a number of ways to represent a bounding box with four numbers. Some common ways are coco (the shape the boxes are in the raw data), voc-pascal and yolo.\n\n![Screenshot%202020-07-13%20at%2010.29.09.png](attachment:Screenshot%202020-07-13%20at%2010.29.09.png)\n*Figure 1, some common ways to represent bounding boxes*\n\nI'll be using the yolo shape for this model. In addition to the above shape, yolo detects objects by placing a grid over the image and asking if an object (such as a wheat head) is present in any of the cells of the grid. I've decided to use a 32x32 grid for this challenge which I'll refer to as a label grid. The bounding boxes are reshaped to be offset within the relevant cells of the image. Then all four variables (x, y, width and height) are scaled down to a 0-1 scale using the width and height of the image.\n\n![Screenshot%202020-06-12%20at%2016.29.19.png](attachment:Screenshot%202020-06-12%20at%2016.29.19.png)\n*Figure 2, a bounding box is put in the cell most at the centre of an object. It's x and y are offset to the cell while width and height remain the same*\n\nAny cells in the grid that have no objects within them contain a bounding box with the dimensions [0, 0, 0, 0].\n\nEach bounding box gets a confidence score where a value of 1 tells us that an object (wheat head) is present in the cell and a value of 0 tells us that no object is present. So a cell with an object present could contain a value like this: [1, 0.5, 0.5, 0.2, 0.2] telling us that there is an object present (due to the confidence score of 1, the centre of the bounding box is exactly in the middle of the cell and the box is 20% of the images total width and height.\n\nAs a cell could contain two overlapping heads of wheat I have configured the grid to contain up to two bounding boxes. These are known as anchor boxes.\n\nThe below code takes the list of bounding boxes for an image and puts them into the yolo label grid shape.","metadata":{},"attachments":{"Screenshot%202020-07-13%20at%2010.29.09.png":{"image/png":"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"},"Screenshot%202020-06-12%20at%2016.29.19.png":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAWQAAAFqCAYAAAAtESBQAAAYWWlDQ1BJQ0MgUHJvZmlsZQAAWIWVeQVUVF3397mTzDAM3d0l3c3Q3Q0CAwzdHSopEiqCgCCgggqCChYhYiGIKCKogIFIGJQKKigC8r+Evs//eb/1fes7a517f3efffbZcWrPAMCZRo6MDEXQARAWHhttZ6zH5+Lqxod9ByCAAABwAYjsGxNJsrGxgL/An/f/LsvDMDdcnkltyvrv9v9rofejxPgCANnA2McvxjcMxtcAQGX4RkbHAoBRhemCCbGRm9gDxkzRsIIwjtzEAds4axP7bOOyLR4HO30YXwCAikAmRwcAQGyD6XzxvgGwHOILuI0h3C8oHGadh7G2byDZDwDOXTDPrrCwiE3sAmMxn3/ICfhfMn3+yiSTA/7ibVu2CpVBUExkKDnp/9Md/+8SFhr3ZwwRuBICo03sNm2G/fYiJMJ8ExNgPB/uY2UNYwYY/wzy2+KHMQIfGGfiuM2P4PKN0Yd9BlhgLOtHNjAHmxEHCKPwUCuLHbqPf5CRKYzhGYJIDIo1ddjpm0OJMbTfkVkZHWFn/Qf7R+uTdvo2kqO3xt3k74oLcSTtyH8RSDH9I/97cqCDM4zxACDx8UFOVjAmwpgpJsTefJsHKZAcqG/1hyc6zm5TfyEYq1LCjfW25SM9/KON7Hb4I8Ni/tiLzA4MMrXawWWxgQ4m2/5BNviSt/Rng3ELJZzk+EcOJcbF4o8tfhQDw23bkf2UcMcde5FjkbF6djt9FyJDbXb4UVSUUONNugCMOWLi7Xf6ojRj4Qm5LR9lERlr47CtJ8o7mGxms60PKh5YAH1gAPhAHFx9QAQIBkH9863z8Nd2ixEgg2gQAChAaofyp4fzVks4/LQHyeATjCgg5m8/va1WCoiH6et/qdtPKeC/1Rq/1SMEfIBxGDAHofB33Fav8L+jOYF3MCXov0b3hXUNhetm23/TSDDFYocS90cuH+0fTowhxgBjgjHCiKM4UNooDZQF/NSFqzxKFaX2R9v/8KM/oAfRk+gh9Dj6pVdQRvS/dLEE47B8ox2Lff5pMUoElqmE0kNpwdJhySgWFAeQQinC45BQOvDISjBVf0fvTdv5/g92/rXgHz7f4cPJ4hA4VpwuTuzfPYkSRKW/UjY9+k//bOvq89er+n9b/j2+/j/87Ae/zf/NicxBXkX2IO8ie5EdyFbAh7yNbEP2IW9u4r9z6N3WHPozmt2WPiGwnKD/Go+8M+amJ2Nkz8vOyK7ttIFYSmLs5gLTj4hMig4KCIzlI8E7P4XPNNxXehefvKy8LACb58j2NvXNbut8gFie/IdGmQZAE14buIH/0IKPAtDQDQBr3n9oIu4AsMP78uWnvnHR8ds01OYDDe8GtPCKYgc8QBCIwRbJA2WgAXSBITAD1sABuAJP2M+B8HyOBglgD0gH2SAfHAEl4Dg4CU6Dc+AiuAJaQQe4C+6DR2AADIHX8Px5D+bAAlgGqxAEYSEaiBFih3ghYUgSkodUIW3IELKA7CBXyBsKgMKhOGgPlAnlQ0XQcagaqocuQ9ehu1AvNAi9hCagGegr9AuBRBAQTAhuhAhCBqGKICHMEQ6I3YgARBQiGZGFOIwoQ9QgLiBaEHcRjxBDiHHEHGIJCZDUSBYkP1IKqYrUR1oj3ZD+yGjkPmQeshRZg2xEtsORfoYcR84jV1AYFCOKDyUFz2ETlCPKFxWF2oc6iDqOOodqQXWhnqEmUAuo32gaNBdaEq2ONkW7oAPQCehsdCm6Ft2M7oZX03v0MgaDYcGIYlTg1eiKCcakYA5iqjBNmDuYQcwUZgmLxbJjJbFaWGssGRuLzcaWYy9gb2OfYt9jf1JRU/FSyVMZUblRhVNlUJVSNVDdonpK9ZFqFUeHE8ap46xxfrgkXAHuDK4d9wT3HreKp8eL4rXwDvhgfDq+DN+I78aP4r9RU1MLUKtR21IHUadRl1Ffon5APUG9QmAgSBD0CR6EOMJhQh3hDuEl4RsNDY0IjS6NG00szWGaepp7NGM0P4mMRGmiKdGPmEqsILYQnxI/0+JohWlJtJ60ybSltFdpn9DO0+HoROj06ch0++gq6K7TjdAt0TPSy9Fb04fRH6RvoO+ln2bAMogwGDL4MWQxnGa4xzDFiGQUZNRn9GXMZDzD2M34ngnDJMpkyhTMlM90kamfaYGZgVmR2Yk5kbmC+SbzOAuSRYTFlCWUpYDlCsswyy9WblYSK4U1l7WR9SnrDzZONl02ClseWxPbENsvdj52Q/YQ9kL2VvY3HCgOCQ5bjgSOExzdHPOcTJwanL6ceZxXOF9xIbgkuOy4UrhOc/VxLXHzcBtzR3KXc9/jnudh4dHlCeYp5rnFM8PLyKvNG8RbzHubd5aPmY/EF8pXxtfFt8DPxW/CH8dfzd/PvyogKuAokCHQJPBGEC+oKugvWCzYKbggxCtkKbRH6LzQK2GcsKpwoPAx4R7hHyKiIs4iB0RaRaZF2URNRZNFz4uOitGI6YhFidWIPRfHiKuKh4hXiQ9IICSUJAIlKiSeSCIklSWDJKskB3ehd6ntCt9Vs2tEiiBFkoqXOi81Ic0ibSGdId0q/VlGSMZNplCmR+a3rJJsqOwZ2ddyDHJmchly7XJf5SXkfeUr5J8r0CgYKaQqtCksKkoqUhRPKL5QYlSyVDqg1Km0rqyiHK3cqDyjIqTirVKpMqLKpGqjelD1gRpaTU8tVa1DbUVdWT1W/Yr6Fw0pjRCNBo1pTVFNiuYZzSktAS2yVrXWuDaftrf2Ke1xHX4dsk6NzqSuoK6fbq3uR5I4KZh0gfRZT1YvWq9Z74e+uv5e/TsGSANjgzyDfkMGQ0fD44ZjRgJGAUbnjRaMlYxTjO+YoE3MTQpNRky5TX1N600XzFTM9pp1mRPM7c2Pm09aSFhEW7RbIizNLI9ajloJW4VbtVoDa1Pro9ZvbERtomxu2GJsbWwrbD/YydntseuxZ7T3sm+wX3bQcyhweO0o5hjn2OlE6+ThVO/0w9nAuch53EXGZa/LI1cO1yDXNjesm5NbrduSu6F7ift7DyWPbI/h3aK7E3f3enJ4hnre9KL1Intd9UZ7O3s3eK+Rrck15CUfU59KnwVffd9jvnN+un7FfjMULUoR5aO/ln+R/3SAVsDRgJlAncDSwPkg/aDjQYvBJsEng3+EWIfUhWyEOoc2hVGFeYddD2cIDwnviuCJSIwYjJSMzI4cj1KPKolaiDaPro2BYnbHtMUywRf2vjixuP1xE/Ha8RXxPxOcEq4m0ieGJ/YlSSTlJn1MNko+m4JK8U3p3MO/J33PxF7S3up90D6ffZ2pgqlZqe/TjNPOpePTQ9IfZ8hmFGV8z3TObM/izkrLmtpvvP98NjE7OnvkgMaBkzmonKCc/lyF3PLc33l+eQ/zZfNL89cO+h58eEjuUNmhjcP+h/sLlAtOHMEcCT8yXKhTeK6Ivii5aOqo5dGWYr7ivOLvJV4lvaWKpSeP4Y/FHRsvsyhrKxcqP1K+djzw+FCFXkVTJVdlbuWPKr+qpyd0TzSe5D6Zf/LXqaBTL6qNq1tqRGpKT2NOx5/+cMbpTM9Z1bP1tRy1+bXrdeF14+fsznXVq9TXN3A1FJxHnI87P3PB48LARYOLbY1SjdVNLE35l8CluEuzl70vD18xv9J5VfVq4zXha5XNjM15LVBLUstCa2DreJtr2+B1s+ud7RrtzTekb9R18HdU3GS+WXALfyvr1sbt5NtLdyLvzN8NuDvV6dX5+p7Lveddtl393ebdD+4b3b/XQ+q5/UDrQUeveu/1h6oPWx8pP2rpU+prfqz0uLlfub/licqTtgG1gfZBzcFbT3We3n1m8Oz+c9Pnj4ashgaHHYdfjHiMjL/wezH9MvTl4qv4V6uv00bRo3lv6N6UjnGN1bwVf9s0rjx+c8Jgom/SfvL1lO/U3LuYd2vvsz7QfCj9yPuxflp+umPGaGZg1n32/Vzk3Op89if6T5WfxT5f+6L7pW/BZeH9YvTixteD39i/1X1X/N65ZLM0thy2vPoj7yf7z3Mrqis9v5x/fVxNWMOula2Lr7f/Nv89uhG2sRFJjiZvXQWQcEX4+wPwtQ4AGlcAGAfgNMF9O8/bKUj48rGZyzpBhggSUhXFhsZjqLCyVK64TPxtAoaGTGylw9OHMjxkUmKuZAVsIez9nMpcR7jneHX5CvgHBfFCasKuIiGiYWIe4noS3BKLkvd3lUuFSGvJ0Mi8lW2SS5O3VeBX+KR4XWm/sq0Kl8p71Ua1RHWSBl7jmWallp/2Lu2vOq26e0h6egS9t/q3DBoMq4wKjfeZkE11zNjMFs37LBotq6yqrTtspuzQ9uwOHI50TkinNedVV+CGcyd60OxG7V7ynPQa8L5DvupT61vul0dJ8g8IcAjUC1IMlgjhD2UPow1Hhn+PmIwciLoRfSbmcGxqXHZ8cyIqiZJ8Zw/YK7JPPdU0zT09LuNwZklWyn7F/VPZBQdscoRzqfNAPuIg/SGxw9oFVkecC92K3I66FDuVOJTaHrMqMy83Pq5XoV2pVqVwQuqkxCnZavOazNPjZ01rL9TN1dM3CJ+Xu6Bx0aDRssn5ktflwCuRVxOu7WvOaNnfmtOWf72gveRGZUftzWu3um+P3Bm/O9zZdM+/i63rQXfp/YQe/we7e50f2j4y7zN+bNLv8CRq4NTgy2fUz2WG9IdNRwxfqL4UfkV8tfJ6evTFm7tjp99mjgdMOE5aTVm+s35v/cHso9o06/T4TN6s4uz43Ln55E8mn6k+138x/jK1cHox8avnN+vvlkvBy50/D/xqXTfY2NiJvxwShZxBjaOnMAtUSJwyPpC6kjBOlKBNoLvPwM6YxPScRZ41g+0NhxJnNtcADwevC18hf4fAqOCS0LLwrMhj0dNi0eLaElQSzyVP7gqWUpL6LX1f5rCssxyv3Ef5RoV4RS0lSKlbOU/FWpVRdVitXN1dg1tjFJ4FHtrs2iM6x3TdSSKkVb0h/csGBw0pRprG9MYfTDpMS8zizSkWPpaBVhHWYTY+ttZ2GvYSDpyORCeE07LzR5dh13tuje4VHnm7kz2DvFy8DcgyPmy+kO+s3xCly785oDawNCgrOCLENVQ3TDScBp4JE5FjUd9j+GO94srj7ya8SJxKmk9e2UO9l2efWCpfGibtbXpzRkFmdJbnfsdslwNBOZm5VXkX85sPthy6dvhywcUj9YVni04drSguKSkozT2WUZZUHnE8oCKoMq3q9knxU+dqRE8XnXl2dqWOeI6jXrBBAp4HKhe1Gw2aLC+5Xg69kn319LVbzYMtY63Tbd/akTdYOyRvatzSva1yh/8u4u5kZ8+95q667or7R3r2P0jujX4Y+yi3r6Of5cnegTdPOZ7pPHcY8h9OGzn74snL768ZRqXeWIxFvj02fmPi6eTY1OS7uQ9oOPrpM4Nz9POyn5Q+i3yh/fJz4cPiyNeH365/r15KXXb6Ifpj+WfHSvIvjVXCmsH6zE78paE5RBXSEyWOxqIXMTPYWapJ3CI1niBMQyK60abTXaAfZNhgEmY2ZAlm3c92kv0aRzfnA6773Dd4qnkT+fT4fvGfETAXmBPMERIV6hT2FF4RKRaVFX0oFiCOFa+TMJH4KJm9S2xXt5SvNJCuktGUeSEbB99umuQt5KcVMhV5FNuU7JTmlfer8Kq0wreWabVUdRb18xokjaeavpqftVK0sdoVOoo6w7rJJB5Sm5613kv9QP0NgxpDGyOc0T3jPSaKJrOmNWYe5mzmwxYllvZWtFa91pk2GjbfbZvsQuxF7d85VDvudmJ3eu5c4GLisuHa7BbqLuT+xqN0t9XuZc9iL2Gva94k71fkRB8BnxfwPhJIMfZXCVALNA0iB4eFkEN1wujCRsPPRoRFKkWuRd2LzouxiWWOfR13Mt4vQSThQ+KJJMOk0eTQFKaUZ3tu7L21ryv1Xtr19PqM0szMrIj97tmGByRy0DnPc8vz3PKF8lcPjh96fPh6wakj+wrdi9SPchxdKR4uuVJ67NihsqLy6uNXK+5XvqiaPbF6iqaar0bhtMkZj7MRtfvqcs8drE9rIJ9XuUC88PXip8aVS4TLPFfkr9pcS2m+1vKzTe16ZHv5jUsdbTdv3Oq9vXTXuPN6l333Uk9pr8LD532H+r0HTJ+SnusNh74kjs5N9s8ufV/ZjP/2732bBaMMwNF0OEPNBsBRB4DCLjjPHILzTjwANjQAOKgBhIg/QBD6AKQ+8ff8gODTBgOoAT1gA7xAFMgCdTgXtgZuwB/EwNllATgBGsEt8ARMgO9w5sgFyUHGkBeUABVCF6AH0AcEBiGGsEDEIKrgPG8DzuvikdeRv1HGqKOoSbQCOgf9FqOOKceswhnWQyoVqjocJ64QT43PpcZTHyFwEOpoFGk6iFrEdlpV2ht0JnSv6WMZ6BguMhowDjI5MA0yWzM/ZfFi+clazqbFNsa+l4OTo53TkwvH1cEdz6PI8433Cl80vxL/mkCPYKlQoLCmCFFkXPSqWI64jwRJUmQXcdeq1GfpdzJDss1yKfJy8mMKOYpKil+U2pSLVJJU/dQs1GU1WDWJWtLaFbqSpEN6vfpfDKmMmI3ZTbhMhcwUza0soizLrLqsv9oK2jnbH3bocUI5G7hku/a5s3j47G7wfOeNIdP7YHyWfN/7jVJmA2gDzYNKgj+GaoYVh3+ONItqiCHERsW9SjBKbEuWSqndy7evIo0lvTATn5W+f+lAcM5cXv7BsMPNhfRHOYo/ldaXeR1nqRioOnTS+NRSTcEZprM5tcvnQuq/nj9y0bCJ/tLilQ/Xplvm2j62T3Us3ma9q3/Ps9u7x75X55HMY/EnyoPhz36OoF7hRk++ZZy49Z44vWeO9Knpy+pX5e9Gy/gfh34+XJn+9X715dq19SO/fTZkt/aPzfhjAQEwAHbADySAAtACJsABeIMwkAJyQTmoB9fBI/AGLEBoiAOS3Yp+ElQMXYL6oU8IWoQCwg2RibiCeI/kRXohzyDnUcqoLNQQWhydjh6FY1+BBdhA7BCVIVUbTgbXgBfHX6BWpL5NsCFM0SQSccQSWn7aS3D++po+gYGFoZXRifET015mPHMZixTLQ9YINla2O+xBHEwcdzgjuIS4RrnLeVx42Xhf8lXx+wnICgLB50LnhbNEPEQV4VxuVrxP4ip8ihVIZUrvkYmV9ZXTlSfI9yvkKZorsSotKr9U6VFtUatRP6iRrBmvlavdpvODpKDnp59vUGvYYnTD+IbJTdNeswkLhKWElZP1fptW23l7IQcvxyqnMRcB12C3Fg/sbmfP417d3oPkTp963xy/IIqdv0mAa2BG0J0QmlCfsI4IjsjkqDcxerH18bQJkYmPkvlT4vcM7FNKPZPOmVGchd+fkj2fQ86dzE8+JFuAOPKm6HJxfKnisa/llyviqtRP/DpVWyN/uurMx1rRusBzlxpYz1de1Gr8dKn8itrV/mZyy2pbTbttB7hZf9vizmLnyS6f++oP+B+iHj1+HP8EM5D3lPCsZshrxPJl6Ou6Nx/HeSdt3qV/uDXDOnfks8jC42/FywdXTFfl106sv/u9uBN/FMABOnj18wNJoAz0gQ3whGO/F1751eAaeADG4HVPgEQgXWg3lAJVQDehCQQOjjoZUYIYQDIjKcibKC5UGmoW7Yp+jNHH3MRqYe9SWVC9wcXgafGXqJ0ISEIrTRRRjviTtpuunD6OwZXRlMmM2ZbFjFWFTZxdicOLM4krltuHx4HXis+S31LAQtBSyE7YSyRG9JBYg/gDiZldNFIq0v4yx2WH5TkU/BSblFZVbFQfq+dqumqjdY7orumZ62fCEWw16jC+ZdJvumpubtFiJW19wVbarsVB33HYOcwV73bBw8mT3pvax8vPnfIuQCMwP+hDiF1oX7hlxNMo9+jp2JR4noSxpPspd/ZWpTqm/cqoznLK5j2wkHsz/+Ah/wLjQvaiR8X+JcvHMsvpj9dUKlc9PulfDdVUnlE9O1QXV8/Z8OBCaqPxJZkrRtdSW2raCtpdO1hvjtyuuOt6D9t19r5iz41ew4cjfYn9MgPIwYVn00ODI4UvRV9Vvf79xnAs7+2jCdpJx6lT72Y+yH0MmT4182B2dh79ieuz7BeDBedF8le/bzbfBb4vLR1a5lpu+KH24/iPlZ/OP1tWWFaiV1pWVn/p/sr61btKXLVfPbY6sEa1pruWuHZ5bWadf911vWj94fr6b7nffr+P/X70+/eG3AZlo2yjbzP+Mf4K8lvHB0TQAwA9trHxTQQAbBEA64UbG6s1Gxvrp+FkYxSAO6Hb/yFtnTV0AFQubKJHbb/S/v1fzv8AeEDZYozmsIQAAABWZVhJZk1NACoAAAAIAAGHaQAEAAAAAQAAABoAAAAAAAOShgAHAAAAEgAAAESgAgAEAAAAAQAAAWSgAwAEAAAAAQAAAWoAAAAAQVNDSUkAAABTY3JlZW5zaG90dCJqagAAAdZpVFh0WE1MOmNvbS5hZG9iZS54bXAAAAAAADx4OnhtcG1ldGEgeG1sbnM6eD0iYWRvYmU6bnM6bWV0YS8iIHg6eG1wdGs9IlhNUCBDb3JlIDUuNC4wIj4KICAgPHJkZjpSREYgeG1sbnM6cmRmPSJodHRwOi8vd3d3LnczLm9yZy8xOTk5LzAyLzIyLXJkZi1zeW50YXgtbnMjIj4KICAgICAgPHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9IiIKICAgICAgICAgICAgeG1sbnM6ZXhpZj0iaHR0cDovL25zLmFkb2JlLmNvbS9leGlmLzEuMC8iPgogICAgICAgICA8ZXhpZjpQaXhlbFhEaW1lbnNpb24+MzU2PC9leGlmOlBpeGVsWERpbWVuc2lvbj4KICAgICAgICAgPGV4aWY6VXNlckNvbW1lbnQ+U2NyZWVuc2hvdDwvZXhpZjpVc2VyQ29tbWVudD4KICAgICAgICAgPGV4aWY6UGl4ZWxZRGltZW5zaW9uPjM2MjwvZXhpZjpQaXhlbFlEaW1lbnNpb24+CiAgICAgIDwvcmRmOkRlc2NyaXB0aW9uPgogICA8L3JkZjpSREY+CjwveDp4bXBtZXRhPgpBln5cAABAAElEQVR4Ae2dCWBVxb3/vzc7gbCEVRYDyCKbAopREcWtda1LfU9bbYu10vX/au32Xn19tu9V+2rr0k1tn0Xq1lLrBiiLiggoEgHZg4Q9ELaEhJB9/f/mnJzcm9wluck9Z8659ztwc8+ZM8tvPjP3e+fOmTPjaxYHOhIgARIgAe0EkrRbQANIgARIgAQMAhRkNgQSIAEScAkBCrJLKoJmkAAJkAAFmW2ABEiABFxCgILskoqgGSRAAiRAQWYbIAESIAGXEEhpb0dDQwOWL19ueA8cOLD9ZUfPT5w4YeSXnZ2N5ORkR/MOzKy6uhoVFRWGl1uY6LajtLQUqq2kpaWhT58+gbgcP7baiW4mlh2Kh+Kiy9XV1eHUqVNG9m5hotuO8vJy1NbWGjqi9ESnU+0kNTUVV199dZAZvvbzkNWHbN68eUEB6UECJEACJBA7AnPnzg1KLKiHbIXIzMwMqeDWdSfeN2/ejP379+O6664zvlGcyDNUHqq3sXLlSkycOBFjx44NFcQxvzVr1qCqqgqf+cxnHMszVEaHDh3Chg0bkJubiyFDhoQK4pjfW2+9hX79+uGiiy5yLM9QGe3YsQMFBQXG50Z9fnQ59YtO/codM2YMJk2apMsMI99169ahuLgY119/vVY7jh8/jrVr12L69OkYMWKEVltef/11JCWFHi0OEmQroBoiGDx4sFbDe/ToYeQ/aNAgrT8BU1JMTL169dLORP3UcUPdnD592qibvn37amfi8/mQnp6u3Y4DBw4YTAYMGICsrCxtnx1reK1nz57amah6UfWjW0vUMI5yvXv31m5LpIYRWqYjxeA1EiABEiABWwhQkG3BykRJgARIIHoCFOTomTEGCZAACdhCgIJsC1YmSgIkQALRE6AgR8+MMUiABEjAFgIUZFuwMlESIAESiJ4ABTl6ZoxBAiRAArYQoCDbgpWJkgAJkED0BCjI0TNjDBIgARKwhQAF2RasTJQESIAEoidAQY6eGWOQAAmQgC0EKMi2YGWiJEACJBA9AQpy9MwYgwRIgARsIUBBtgUrEyUBEiCB6AlQkKNnxhgkQAIkYAsBCrItWJkoCZAACURPgIIcPTPGIAESIAFbCFCQbcHKREmABEggegJBWzhFnwRjkAAJJCSB8r3AqX1AfTlQJ1t6qZc6hg9I622+qhqBJnmVFQC9hgEp+vYa9EIdUZC9UEu0kQR0Eqg5CZzYCBz5ACjNl9dO4KS8mmo7tirl20DS2cAL48yw6f2AniLM/cYDI68Dhl4K9BnTcToJEoKCnCAVzWKSQFQEdv8TKHzHFOLjH0cVNWLg2lJAvU5uA/a8YgYdNAM44xIg5xrgTL27qUe03YGLFGQHIDMLEvAEgVMyBFGwwHyVbHbOZCX46rX5caD/FOk53wCMuhEYcpFzNrgkJwqySyqCZpCANgIHl/qFuKFamxlGxiVbAfXa8EsRZhHlSXNFnEWgE8RRkBOkollMEmhDoPq4X4TV2LAb3f5FgHolkDBTkN3YEGkTCdhJIH8+kPdz4PR+O3OJXdqWMJ91GzD1Phlvnhm7tF2WEuchu6xCaA4J2EagfD/wzt3Au/LyihgHwtgjNxpfkZt/q/5NptvJeHccOgpyHFYqi0QCQQQ+fQF47XJg5/ygS57z2PJ74J9yw2/rk54zvSODKcgdEeJ1EvA6gY9+Crz9JW/2isOxV2Pg78sc5xX3AjUl4UJ5zp+C7Lkqo8Ek0EkC9RXA0n8F1v+ikxE8GGzHM8AbMndZzZmOA0dBjoNKZBFIICQB9XDH7pdDXoorT/UU4RtXA9v/7PliUZA9X4UsAAm0I2A9AVd9ot2FOD997+vm7BEPF9PXLC7Q/oaGBsybN8/w8vl8gZccP7ZM022HKrhbbHGLHQIEquH45J/6r9O5hYkr7Gj5OFsfas1VY7QR1TYct6O9dgkQs8WKLe2vOdx4rXYyd6489NLOhZ2HnJqaijPPPLNdcGdPT5w4gfLycowcORJJSfo687W1tTh06BD6Z/dH3359nYXQLrfDhw9DfWnm5OS0u+LsaUVFBY4dO4bBQwajZ8+ezmbeLrd9+/YhIyMDZ5xxRrsrzp6WlJSgrKwMI0aMgPr8OO7Uoj/Fm9GANBxIOgd9m4+if/Mhx80IzPCobyyqfVkY1STDCk66AdNkAaNxrTlWVVXhyJEjGDRoELKyslr9dRzs2bMn7BdUkCBbwqca+JVXXqnD3tY8V69ebQjyZZddhrS0tFZ/pw/UB00J8lljzsLUqVOdzr5NfgsXLoQSQ911s3v3bkOQzz33XO1fDvPnz0f//v21M8nLy8OmTZswc+ZM5z/0aqw4/ztGW6nw9TMEOadpC3IbX2nTfpw+WSarvRX5zsaVDf/nbNZHJbvct4ERVxn5FhYWGoI8ceJEjBvnF2pnjTJzU58dX5gOpr5upw4SzJME4pGAEmM1m4KuLQF1o+/oR239XH5GQXZ5BdE8EohIYO9rFONIgN7+sixWtC1SCFddoyC7qjpoDAlEQeD0AeCDH0cRIQGDniowH7VuqvdE4YPGkD1hNY0kARIAPhQxVoJDF5nA4fdkOyk1R3l05HAuuMoesgsqgSaQQNQENj5iLp8ZdcQEjXBgsScKTkH2RDXRSBIIIKDWL1a9Y7roCZTtij6OgzEoyA7CZlYkEBMCqndM1zUC+93dU6Ygd61aGYsE9BDYIU/R7luoJ+94yLV4C6CW73SpoyC7tGJoFgkEEagpBtg7DsIStcf6h+Vm6O6oozkRgYLsBGXmQQKxILDlj0DZp7FIKbHTqJLH+DbJDtcudBRkF1YKTSKBIAKVRcA27y8vGVQuXR4FC4DKI7pyD5svBTksGl4gARcR2P5/QJWIMl1sCKhdRpQou8xRkF1WITSHBIIIsHcchCQmHhTkmGBkIiSQWATUgvPsHce+zo/JwkP734x9ut1IkT3kbsBjVBJwhMCBJY5kk5CZuKyXTEFOyFbIQnuGgJqeRUG2r7qUIJe5Zz0QCrJ9Vc2USaD7BCjG3WcYKYWmOlfd3KMgR6osXiMB3QQoyPbXgIuGLSjI9lc3cyCBrhGolifzCld0LS5jdZ7ASVnA/lhe58PbGJKCbCNcJk0C3SJw6F2gqbZbSTByJwkc+7iTAe0NRkG2ly9TJ4GuEygUQaZzhsBx9pCdAc1cSMCrBFQPmc4ZAhyycIYzcyEBTxKoPgGU7/Wk6Z40unQnUF+p3XQOWWivAhpAAiEInNwewpNethI4ucPW5DuTOAW5M5QYhgScJuACcXC6yNrzc8GXIAVZeyugASQQgoALxCGEVfHt5YIvQQpyfDcxls6rBFwgDl5F12W7XfAlSEHucu0xIgnYSKDutI2JM+mQBNSDOJodBVlzBTB7EghJoJ6CHJKLnZ715Xam3qm0KcidwsRAJOAwgboKhzNkdnDBrxIKMtshCbiRAHvIztdKnf4eckr7Ujc1NRletbW1WLt2bfvLjp4fO3bMyC8vLw/JycmO5h2YWXV1tXF68OBBWMeB1508Li8vR319vfa6KSsrM4q9c+dOFBXp3eutsbERyh7d7fXoUdnNWNzGjRuRlpZmHHf5T9O1QBebfJ0v3ci2KGk81uJfu2xCLCKW+oaiUQqyNlmvHad9/Y3i7E66ACW+EaGLpqTPIc1rbm4OaYNPLrS50tDQgHnz5oUMTE8SIAESIIHYEJg7d25QQkE9ZCtEr169cPPNN1unWt7XrVuHgoIC3H777UhNTdVig8q0tLQUb775JqZNm4ZJkyZps0NlvHz5clRWVuKWW27Rasf+/fuxZs0azJ49G8OHD9dqy4IFCzBw4EBcccUVWu3YtGkTtm3bZtRNz549u2fLgumyj17XtqmvQl+8mvoAJjW+j2lNi7tnRzdjr0y+G0eTxuCO+ge6mVL3ohf5xmNFytdwccMCjG5eHzqxzDOA2zeGvhZD3+eeey7sL/4gQU5KMoeVfT4fMjMzY2hG9EmlpJjm9ejRo/s/AaPPvjWGNUyhvhR0M1H144a6sX6Sp6ena2eieKghLd11Y7XXjIyM7tuS5gMquzam2eQzxzpSUIvM5q6l0dr4u3mQjAZJoVm7Hem+KqMkaagOb0vaUEjFdbPE3YvOm3rd48fYJGAPgdTe9qTLVMMTcAFzCnL46uEVEtBHoMcAfXknas4uYE5BTtTGx3K7m0C23nsV7oZjk3UuYE5BtqlumSwJdItA9sRuRWfkLhBwAXMKchfqjVFIwHYCLuit2V5Gt2XgAuYUZLc1CtpDAoqAC3prCVcRLmBOQU64VscCe4JAqsxjHnKhJ0yNCyMVa8Vcs6Mga64AZk8CYQnkXB/2Ei/EmIBLWFOQY1yvTI4EYkZg2KUxS4oJdUDAJawpyB3UEy+TgDYCZ8wEesrTY3T2ElCMFWsXOAqyCyqBJpBASALqEehhl4e8RM8YElCMWx43j2GqXUqKgtwlbIxEAg4RyJFlOOnsJeAixhRke6uaqZNA9wgosUjP7l4ajB2egGJLQQ7Ph1dIgAQCCGS4SzACLIuPQyXGirFLHHvILqkImkECYQm4qAcX1kavXnAZWwqyVxsS7U4cAuPvBAaelzjldaqkiqli6yJHQXZRZdAUEghLYHLwdj9hw/JC5wi4kCkFuXNVx1AkoJfAJBFk9pJjVweKpWLqMkdBdlmF0BwSCEvAhT26sLa6/YJLWVKQ3d5waB8JWARUj27E1dYZ37tKQDF0Ye9YFYeC3NVKZTwS0EFg+g915BpfebqYIQU5vpqae0pTUQGsW2e+GtTOw2FcUZEZZtu2tgGam/3ngWkF+vtDJM6R6t1N+U7ilDfWJR1xlat/ZVCQY13hTM8kkJ8PXChrzKpXZWV4Ks8/b4a5915/mHfeAS67zH++Y4c/LSXOie6m/wjoPTrRKXSt/DnXdS2eQ7EoyA6BTrhs0tOBQYPMV7IsktNZ98YbwNXSC9y0qbMxEi9c1gjg4l8lXrljUWIXPZUXqjgpoTzpRwJtCKge7r59siKWT26GmLshJ50+jWwZbkjds0fWAsgxg5eUAEeOyJKRsvPCmDHAu++a/j16+JOrqwM++QQoLgbOP9/vr45OnQJ27vT7qWGM4cP95+pIDVmoHvPu3cBE2QhU5ZOIbsxtwHn/AWz4ZSKWPvoyj7kd2B99NKdjUJCdJu7F/JRQTpliWr5rFzB2LPq88AJue+wx0+/kSaBfP+C//gt48kngjjuA++8HLrjAvF5WBvTpAxw6BNx0E7Bxo5/CjBn+YzVU8e//bp6L4Bt5/v3vwKhR/jB33QUsWuQ///nPgTPP9J8n0tFFDwMn5Mvt4NJEKnX0ZT3zGmDCHBHkJdHHdTgGhywcBu7J7IbKAt4zWxbwfvttowiZH3zgL8qHH5o913/+0/S79Vb/tcAjJdKWGN9zj5nmxx/7Q6jesBpzttwXv9hWjJW/ulGoRHjWLDPUgw8iPZHHlWc+AmS1/EKxuPHdT0CxUYw84ijIHqko7WbeJj+RlXvrLeMmXQ8ljJZ7/31g61bg+HHT57Ofta7432tqgJdfNs8fegh45hlgzRpgdMDNqdxc4LvfNcNkZQEvvujvZVspzZ9v9sSfe87yQabqwSeq6y+/XK6a75oF1l1VDWrRecVGMfKIoyB7pKK0m3nDDaYJb74JLF9uHJ+0xneXLQPUS7kbb5QZAL3N48C/hYX+s9mz/cef+Yz/uDNH55xjhurbtzV0cm1t63FCHgybDdwswz10bQkoJoqNhxwF2UOVpdVUdfNM3URT7gc/MN62XXEFGtT47ZYtwNNPG36wetLmmf9vRob/OHCIIfDYHyL8kSX20czcCJ9a/FxRwnPLe/FTnu6WRLHwmBirIgfd1GtqajJQqPdTmn8K1qk78uLKy8uRmppqHOv4U9EiGjXys1s3kwZ5yEJX3aTLDbkMNcNh716jGork5l55YyOy1dBCi1/5JZegWdpNsjDr1VJZqv6aZeaF3NYzXK1MbauR4QmfzMrIev11+MS3UdKpkHip0tvNlPNmmU1R3tL+gtKStumT9AP74YqL7rqpbempn5YbktbnyCyxQ397TQNu3Y6qt+YA8jFWJE/5BjuUeehs6pGOZqlhx+xIl5vL170iM32GmbN2WsyqbJkLX11drb2dhCZl+gYJshVYFWDBggXWqdb3V199VWv+VuZbpCeoXm5wOupmgPRKrdt1lTKrolzmGa+XaW7WoMOhCRPwlpopIW7g/v24pQWUqr86mfp2ngxnnCczJNJlJsappUsxqEXEVbASEefXpb0NF8G/Ts6V4DZJepuuvRanBg/GzS1pvfLKK6iX3naqfDne3eKnxPi42KGDSYsJbd4WL17c5tz5k68ZWe5MvgTq5Qa3IPW/nTFD9ScXrwmb1zq596FeOl1SUpLR4QhlQ1hBTktLw+TJk0PFcczv4MGDMl21GOeeey6SNf5EVd+q+fLk2bBhwzBYxEGn2yXTztQvBy11M20a6v78Z6TJzbvSiy4yMKSrhzjUVDdxjTffjOnTpxvHmQFzj8+VeI2ZmfBJezouD4wMktkYSoxrZLij5PrrMeypp5ApN/FU3CQR4Wq5cdijoAD9RGTHyFhxhfhZburUqUZaSVVVlpdsGJwsw9a9ZUrymFY/HQdFMi/76NGjRt2oz48up9rHNpnDPTi9HMOqVusyw8h3T9L5OO0bgKmNNk/NUzfuRsn9izC7R6tfabtl7vrIkSORnZ2tlcmGDRtkSr/6XRjsggRZqbdy6fLBOb/9xP3g+Lb6KCFUgjxNPtA6G7jqvVmCrARBp1MfejWEoq1ujh0zil+jHsxYsQIjpW6MhzXEV02+Ui/DqbbzpS8ZhxLC7y6+2HyUWuYmZ8gXnPywNARdDW9IDNN9+imgbgKKGI+wxoxDpdWyrkWpzLwYLPOctTFpMTsvL88Q5CkyZztLzRLR5FT7UII85OxZOD9JZres+6kmS+SXj28EKn19cX7jQvtsyP0fYMZ/Rky/UNqTJcjjxo2LGNbui+vXr4els+3zChLk9gF4TgIxJ6Ce5FOvcE71HhL1YY9wTLrqr4Sq33hg9X3yRVjU1VTcGa/nUJmP/oQ8rfkv7rSvC1ZxlkUXoDEKCXiKgBKsz8lTaqOskXhPWR/aWKtMcSTGqqAU5NDVTV8SiC8C/WX+9vWvSY/ycSCtr3fL1kfuE1w5D7jmH/LAR8ucdO+WJsjyxB6yUOsiqClbHbj+cn1uB2Gcuvw5K6M777SOtLzLxwLq5QY3xw1GiA0XtLzw9a9rtUiNx4dtr2pxqJUyR3fdz2XBqNe12hlV5knpgNp26fyfAJlDoorqpcCJK8hqPYROiLGXKpO2kkCHBLZvB/74BvCg9JaPfgQUyNRW9ao60mFULQGyRgHjpfMxXjpPaiw8zl3iCnKcVyyLRwIdEhhyIaBe6safJcxFqzqM5kiAoZcC4+6Qlwhxmr4ZK46UNSATCrKCMXu29BgeDMDS9rBa5jCuWrUKZ599NnI0z3X9WFZWU9MBL73yyrZGOnx2/PBhWdb4E8yQ5TOzo5ib/cqvgLylprHTpQi3ixZ0170rD5n0kwdVpqvFiTS6PbKWs5padYXUTWrAPGynTWqQ9vGurEV91llnYYw1h1v9Ily5MrQpGTIoN+Vb5qvwnRZxlmVP6ytCh7fLN1UGW8aKCI+9XbZZusquXFydLgXZqh4lymFclcxDPiAT/gfL3NoczfOQj8iXg/EodwR7wxQjpt7lIjwH5GnOs2W5zGxrgfoOcpgn4vtiixiroOtl/frSqcA3ftNBxA4uF+7fjzr1paCZSYk8/HJA5vHXyOPjqRrnIdfIPOQDMu+2rzxQNcb6klKC3Bln7DknYqh6zYdWAMfyzNeJjZ2JHX2YgfIg0WAZfVev4Vck/FKiFOTomxBjdIHAwidFjB8Kjvjyo8AAeTrktu8FX6OPRgJqHeEJd5svZUZDlSnMpTvlW1S98oGT8l5xsHNG9joTyD5bxoEnyEu9y0uJcEpm5+InSCgKcoJUtM5ifiA383/77fAWPHW/zGCSOf6Xyy9VOpcSUMKpVk9rv4JacyNQVy6v0+Z7vbwrlyrjvmm9Zc3rLbKt13FgzgHTn38jEqAgR8TDi90l8OnHwC++2HEqD31BesoiylNmdRyWIVxEQK0doVZYU69QLkl60nSdJpDU6ZAMSAJREig+LGIsQltX3XFEtSyFClu0p+OwDEEC8UqAghyvNau7XF0QWCXgD0lvuq5Gt/HMnwT0EKAg6+Ee97mqYYqtq6Mv5k65qa96ynQkkIgEKMiJWOs2l/mp7wPvyTTWrjp1E/B3EW4CdjVdxiMBtxOgILu9hjxmn5rG9s/Hum/0G2qa3MPdT4cpkICXCFCQvVRbLrd1hfSKn/5B7Iyc94BsZj0/dukxJRJwOwEKsttryCP2bVklN+RsGPt9RJ5NWL/cIxBoJgl0kwAFuZsAGR04vNveG3FK6PdvI2kSiH8CFOT4r2NbS1hfa/aMS4rsy6b8pAi+zNo4XWpfHkyZBNxAgILshlrwsA3/kIWBPl1vfwH2bbVnSMR+y5kDCXSeAAW586wYMgSB/HUhPG3y+ngZ8Ouv2pQ4kyUBFxCgILugErxowhpNu/8sfRZ49qdeJEabSaBjAhTkjhkxRDsCS2WPyVUvt/N08PSFXwALn3IwQ2ZFAg4RoCA7BDpesjGGDe7RX5rffgv4ULaGoyOBeCJAQY6n2rS5LG67saZmXjhxQ9FmrEyeBFoJUJBbUfAgEgFj6pnMB3bT1LNa2cRCzVFubopkOa+RgHcIUJC9U1daLTUezpAd5N3m1EMptZ1Yb9ltdtMeEghFgIIcigr92hBw++PLjQ3Ank1tTOYJCXiSAAXZk9XmnNFeWeCn5EhsFzZyjjBzIgE/AQqynwWP2hHw2hKYsVr6sx0GnpKAYwSCNjltajLvkFRVVeHNN990zJBQGZWVlRney5YtQ1JSbL87pu7aBdl93nCNJSVYGqGs9fX1RridO3fi8GHZZ0ijO3nyJBobG22vm7ITwN4CYPZ9oQub0cccuJ1y03qMu0Lvyj8p6Q3IHnlCbH0T+YXACzJPut/g0Hbb6VteLrsvi3vvvfeQnCybf2pyqn0ot2fPHhQXFxvH10gbtyw6LG1/U4T2bkSI0Z8SydeJ9tqRuTU15r5gmzdvRkGBNGyNzufzoVltIhnCBQmyFUYJs4Kp01lCqERIFSKWrqrafydI5ROprIFfUnV1dbE0I+q0GhoajMqMZG/UibaLoMZkK08BfUe0uxBwmpxifuh7DjiNHn0rA644f+jzNSG1R53Ya7bXUvker5P2npLqrC2qbpQrLS2NeXuNpiTWh12JkGWTauOWIKu2b2f7CbRVfV6UPU7lF5h34LH1Ga6oqEB1wGc/MIxTx5G0LEiQrZ5or1698IUvyJwijW716tXIz8837EhLS4utJfv2Aa+9ZqSZMWQIvvzlL4dNXzWmV155BdOnT8fUqVPDhnPiwsKFC6Ea1Re/KJNwbXBqo9HvXSq7P++NnHjOBbtx4T0r8NFfLkfRlpzIgW2+eusT81G8ZzBW/f7a1pwGyM+fx98Hhp7V6mX7QV5eHjZt2oRbb70VWVlZtucXLgPVPl566SVMmjQJubm5ZrBnnwXkF55yY889F2MjtHczQmz+ql+36ldlpM9XbHKKnEphYSGWLFmCmTNnYty4cZED23z16aefDvsLKrbjADYXhMnbS0D9ilIbjHYkxvZaEZvU1ReLKgunxMWGJ1NxhgAF2RnOnshFCVhXdop2a+E+/ZhLdrq1bmhXaAIU5NBcEs73qfuBlQvir9gfyHoXv+UO1vFXsXFaIgpynFZsNMX6h9op+vFoYngr7EK1g/VD3rKZ1iYmAQpyYtZ7a6lX/A34Uwx3im5N2GUH8/4TUGsp05GAmwlQkN1cOzbbtllmITxkz2QNmy3vWvJqtxHuYN01dozlDAEKsjOcXZeLWpQnkcTYqgB143Kf3udYLFP4TgJBBCjIQUji30MtW6mEyc6dot1K8bTsYK1WrlPvdCTgNgIUZLfViAP2KDHe5cBO0Q4UpUtZqB6yWtyejgTcRoCC7LYasdmeJ74pWx8ttDkTDyS/fhl3sPZANSWciRTkBKpytTnooqcTqMAdFFXNulCzL+hIwC0EKMhuqQmb7VgiK6A9+1ObM/Fg8mp+spqnTEcCbiBAQXZDLdhsw8dLgd/cY3MmHk5ePcmnnuijIwHdBCjIumvA5vz3buUNrM4gVjMv1NoXdCSgkwAFWSd9m/Mul+WBldBUlNqcURwkr1aFU7NP1CpxdCSgiwAFWRd5B/JVArPfhTtFO1D0LmVRtCcxH5bpEixGsoUABdkWrPoTfWQOsOFt/XZ4zYItq8yestfspr3xQYCCHB/12KYUf3kAWPbXNl48iYLAe3+XHay/H0UEBiWBGBGgIMcIpFuSeeOPwEsPu8Ua79rx8mOA2sWajgScJEBBdpK2zXmtkS0Cf/cdmzNJoOSflmVJVW+ZjgScIkBBdoq0zfnszOMNKTsQqxujalyZjgScIEBBdoKyzXmcOGTeiKqrsTmjBE1eTR0skuVK6UjAbgIUZLsJ25y+2ilaCcaRvTZnlMDJFxeZD9dwB+sEbgQOFZ2C7BBou7JRP6m3rrErdaZrEeAO1hYJvttJgIJsJ12b047XnaJtxtbl5I0drL/V5eiMSAIdEqAgd4jInQH+8Zv43inandRlZbingBe4g7Vbq8fzdlGQPViFNZWyU/QPPWh4nJj8LHewjpOadF8xKMjuq5OIFlWeAtSiQXR6CagdrD9eptcG5h5/BCjIHqrTwwVA4S4PGRznpqrZLftkeVM6EogVAQpyrEjanI4aplAzKhrqbM6IyXeawGlZ1lRtllrOHaw7zYwBIxOgIEfm45qr6oO/a4NrzKEhLQT2yw7WqqdMRwKxIJDSPpGmpibDq6GhAfv27Wt/2dHz06dPG/kdOHAAKSlBpnbLlmGlpUizUqipiVhWy45SiaODyeI/y1BFITB8GpDWswbJaQ1yrLduskeeMOgNGH0MSclmm7FwOv2u8s/oXa2NyVFB8bisDjf5GhngF1coldWjRw+nMbTmVyPtWblTp061ttdRLX7Kv07a8WGHPttVVVVolqeXdHxuVFktV1Ji3ngpLi5Gamqq5a3l3efzhc3XJ7DkWS+/U0I8b57siBnnbtbixZiwaJFRyqpx4/DC9+UTRUcCcUrgrkcfReauXUbp8m+8EatvuCFOS+qdYs2dOzfI2LDdTvUNP3PmzKAITnrk5+fj8OHDmD17dux7yDt2tBYls18/XHXVVa3n7Q9UD3ndunUYM2YMRo4c2f6ybeefrAAWPd02+Sk3rTd6yRteuqTtBYfPBo45irFXbEP+snNRemCgw7m3zS53zkqUH+uD/CXyE0Kjy7lgN4ZN3W98bnT3kNesWYOcnByMHTvWIJIZ0MkaPXo00iO091gi3Lx5M06ePInLL788lslGnZbqGW/atAmTJk3CGWecEXX8WEZ4++23kZQUerQ4SJCtgGqIQFWcTqfEWL2UCKaltQ4wxMYkEeFWJ18+kcqqfu4oQc7Ozo4YrjW9GBzkyU7RT94bnNC4K7chObUBhRv01o0aJlAf9eLdQ1C0JSfYUAd9ZnxpFWpOZWpn0u/MYgyTch/feSYu/3yWgwTaZlVRUWF49O3b199eA4ZQ0qXtR2rvbVPr3llBQQHUUJ9T+YWzVg1TKEEeOHCgdlvaDUq0MTm0TLcJwhOnCezdwhtFTjOPZX5/uA9Qy6HSkUC0BLwtyHnS6qXnKncvoi23a8OfKjant1WUudZEGtYBgXq5p/aQzIpRy6LSkUA0BLwtyLm5wIUXAmvXRlNmV4dVH+QD/uFtV9tK48ITsHawbnvLPHx4XiEBRcDbghxndfirOdwpOp6qdOtqDj3FU306UZb4EeSiIkCmsmHjRnmcrcEJdjHN4y8/AZb/NaZJMjEXEHhvAfAUZ1S6oCa8YUJ8CPITTwDD5P62zK/EeecB110H1Nd7owbEytf/KDtF/9Iz5tLQKAn8kztYR0kscYPHhyAvWwbcfz8wZ45ZkzLPD8uXe6JW1U7Rv+dO0Z6oq+4YqXawXvG37qTAuIlAIGgesicL/eMfA//7v6bp77wDHJLb2+pZY5e7fJkgohYMoksMAuqG7QD5IXfOpYlRXpYyegLx0UOeFvCE1qBBJoVKWR7Nxe6EfF+oRWnqa11sJE2LOQH1BayWUaUjgVAE4kOQ+/Txly052X/s0iO1fpNave2I3vWBXEonvs0qkXvPqqdcUxXf5WTpukYgPgS5a2XXFkv1jLet0ZY9M9ZM4NP1nA6nuQpcmz0F2eGqefJ7wMp/OJwps3MdgQ8XAr/9luvMokGaCcSfIFtDFmFWU9LJW+0U/YrM0KMjAUXA2MH6F2RBAn4C3p5lEeq5VJc+Rv3uS9wp2t/seGQRePanMvNiKHDNVy0fvicygfjrIbuwNjevBB6+04WG0SRXEPj1PdzB2hUV4QIjKMg2V8KhXZxrbDPiuEhe3ejduzUuisJCdIMABbkb8DqKqnaKVlOcTh7tKCSvJzoBtYO1EuVyc+u3RMeRsOWnINtY9eohAO4UbSPgOEt6/3bzCzzOisXiREGAghwFrGiCPvENWaZ5UTQxGJYEgPXLgUfuJolEJUBBtqHmn/8f2Zz0TzYkzCQTgsCy+cC8BxKiqCxkOwIU5HZAunu65C/A/P/qbiqMn+gEXnwYeEOWZaVLLAIU5BjWd94S4Ddfi2GCTCqhCfxOlmVd83pCI0i4wntfkNXuIJ98It2JN4DtcleksVFLJRo7RcuMCjoSiCUBNfOCO1jHkqi70/K2IKt1j2fNAqZPB26+GZg8GbjqKllG7Yij1LlTtKO4EyqzOtnBWs3W4Q7WiVHt3hbkuXOBjz4C1BrIP/whMHq0rNyzEviBbM/goFMfGO4U7SDwBMvqyF5zOlyzLNtKF98EvCvI+2Qx4SUyaKvc88/LXKFHgBdfNM9fkoUjTpwwj23++6uvyL6qskkJHQnYSUDtYK3W0KaLbwLeXVxozx5/zXzpS0CvXv5zdaSGMwYObOsX47NnfiJb9z0X40SZHAmEIbBSdrBWCxF9UzZNpYtPAt4V5Oxsf41cey0QuGuIutJeoP2hY3L0+h+Av3Gn6JiwZCKdJ/DPx4H+Isr/6uyoXOcNZMhuEfDukMXUqUBWlln4q6+W1b5/a+46XV4ODB8ODB7cLTCRIq9+VXaK/n+RQvAaCdhH4E9yu4Q7WNvHV2fK3hVktQD9nS1rWt51lywoe435mj9fnj9dD/TubQtXtVO0WjCIjgR0ElBtcPP7Oi1g3nYQ8K4gKxqPPgrce6/JZdkyoLoauOMO4Omn7WCFylPmFCTuFG0LXiYaJQE1R5k7WEcJzeXBfc3iAm1skAct5s2bB5/Phx49egRecvy4rq4Oyh5lh7InnPPJwyA9S0tR2a8fmq0tnMIFbvG/8NVXMea114yz2gkT8PJPfhI2RpNsE11TU4Om+lTUVaeGDefEhbSeNcKiGbUVeusmObUBqT3qUFeVjqaGZCeKHjaPjKxqNDUmGbaEDeTAhZT0OqSkN0jdZKC5yZm+TrI0x95yOyXw42G115SUFKSlpRkl/5eHH0Z6fr5xvPuWW/DRrbc6QASora2VZ7UakZmZ6Uh+4TJRNihbFA/FRaerqjK3HJ+rpu22c2EtS5Ihgb59+7YL7uxpWVmZIch95Iadsiei698ffSIGaHsxIyOj1UNVUKSy1tfXG4JcdaoHqk62m83RmoozB/1GFCMppRHlR/XWTUZWlSHI1aU9UVvpZ+kMhba5pPeqRkNtinYmvQaUiyBXoOJEbzTWh/1otTU+BmdN8vkeNcWfkOrEqA6EauO9W4buAkVI+Udq7/6Uun908uRJqC8Ip/ILZ7HioQRZfTHo/nKorKwM28EMajWW8Cmjb7jhhnDlc8R/9erVyJdv9WtkfNj6po9Zxhs2tCaVLGIeqaxP/UcJkke9gr2rz0b+UrmZqNFd+aOFyOxXgfce1Vs3ORfsxoX3rMCW12egaEtOTIj8DF/DOViHO/ERqtGz02ne+sR8lOwbhFW/l9k2Gt05t+RhwjWbsPaZK1FZnOWoJTd+A7jvKTPLiooKvCRz8c866yzk5uaanmp4r8UNHz8ewx36bC+TocTDhw9H/HxZdtn5XlhYKI8tLMFUmQwwbtw4O7PqMO2nZUjV0tn2gTvodrYPnnjnC34NvPNi4pVbR4mbkCS/crbJXz3rkegoc6zyXCS3TV74RaxSYzq6CFCQI5B/V4T4zz+KEICXYkqgCeZYdBL4jHBXwKodrJfM60pMxnELgaAhC7cYptuOTStlp2iZTUfnHAG/ILOH3FXqv7lHbvIN7mpsxtNNID56yEVFwDqZILw1Ntv2qp2i1ZQiOmcJqCEL5ZLZQ+4WeLWOMp03CcSHIP/978CFF5pP6nWzHqorzLnG3Cm6myC7EJ095C5ACxGlSubLK1dbbb7zr3cIxIcgq3Us1BKcw4Z1m7x6AqpgY7eTYQJdIGD1kDmG3AV4IaJ8tDiEJ71cTSA+xpBvugkyvwcy8bJbsB+XqUPcKbpbCLsVmT3kbuELinx0v6xKOwf40fygS/RwKYH46CE/9xwwRWbG3357lzE//9/A4j91OTojxoCAv4fMm3oxwGkkseyvwF+4g3WscNqeTnwIcjcxNcqOT/Mf7GYijN5tAv4ecuRpb31QjFEyX3kktrfJ0/IfhoI2/ol+8tLDQMPhRKfgjfJTkKWeCj/1RmXFu5Wd7SGPxhY8gyn4CyYjBztasXwT9xv+38F3W/14YBI4xO8oTzSF+BhD7ibqkdOBP/wxfCKVcrd6ozToz30T+HZAT1oJ+YLfhI9nx5V0WaMlRRaUGTnZjtQ7n+aAEWbYwfLUdFrkDm2nE806Lg+GHAdGjBVpTg8f7VTzZSjdNRz96g/hxsGv4mjycGTJQkdX43kj0icjvoyRcp/XaddngJnjcHkyt3aIPbnfIlPazjo3ctq19UBevuz3e6cshtiykP3Ib0sc3qyODM4FVynIqhJk6eQJMmsunCspMQV50JkSLmApi5Q0YP+2cLHs8T/rOpmnK2LldL7tS9MsXwxnieexA0BRjBicbJmHfKSgEfvbZ9jmPBlv4F7MwYOYcex5vNH4HxiSv6k1xMLCG1FT2Hrq2EHvscBQyU3NY68stifbkZMit1WVqyxlYQiy2llkgtzrNpw9y4O3JM63WBHgkEWsSDKdbhOwxpA782DIu7IEkXLDsQvZRw5j4u4PjPMluAc1USxMZETiHxJwCQEKsksqgmZAns8zm2NnFhcqkv75Zsw2sI3fsBYT96wxjt8Gn3dnW/IugfgQZGtReuvdu/WR0JZbPeTOCLIC9Zb0hpWb/s6bSKurRhkGYStmGX78QwJeJBAfgvxduauuNj7Jy/NiHdDmFgJ+Qe7cXcIPcHMbdovwDellJ7fx4wkJeIlAfAiyl4jT1rAEohmyUIlUoxfUmLHlVoArQlks+O5NAhRkb9ZbXFpt9W6jWctiEA4aLIoGj5Ojs+OSCwuVOAQ47S1x6tr1JY2mh3wfvinbPa1qfTBk3bmfA5a7vog0kAQiEmAPOSIeXnSSQDQ95GlY0SrGu6ddgG1jL3PSVOZFArYQYA/ZFqxMNFoCZ8kj0Be1dHEvw2KZLZGLOoR/XO8HeAfZOIqTGIJZX3kXTXt4My9a5gzvPgLsIbuvThLOov+U4Yc/Y5KsTvG6Ufbr8Wu5WZeBz+P/wrI4gRH4FDOg3ulIIF4IUJDjpSY9Wo5v4me4HLJlcgj3LczFRGwIcYVeJBCfBCjI8VmvniiVD824DT+PaOsX8IeI13mRBOKJgPfHkE/JBmIbpBelntKbMQMoKwNOngQGDgQGD46nunJNWb6KX2HMge0446WDmHbofVk7ooc89KxuyTXKe2Pre+Cxdc3v1xRxjNgq7MV4VQ6ftU75TgJxTcDbgrxmDXCdLH92+rRZScOHy2oz8vroI+DBB4Gf/SyuK09X4ZTk5soqazgGyAJ4YZy6yWa91A8x69h8b5bzA5B1Kjt03D2kQ0QMEDcEvCvI1bJI8RfkySwlxllZwNe+Brz8sinGcVM97izIYlnA55Ppl2L6XR/gg79cjcPbRxr9YtVHNvvJSnQ7dhmowpsdrMy2Edd3nBBDkECcEPDuGLLqBR86ZFbDkiXAY48BH38cJ9Xi7mKUyFSzUynZqO7ZE5XJWSKrvVArwxb1SDOEubPW1yATS/G9iMFfxtcjXudFEognAt4V5P37/fWgxo6VGyLbNEycaB7zrycIPIZHZAbyDSFt/Qf+G3m4IuQ1epJAPBLw7pDFsGH++jhxAlDnDQ2yQV6h359HrifQiBT8PyzChXgbV8o85BHYjU9wiQxl3IlDGO16+2kgCcSSgHcF+ZJL/Bz+KBvi/fu/A88957/B57/KIw8Q+Eh2xFMvOhJIZAJBgtzUZK5FW1dXh02b/PuU6YBUojazE7d161aZ1RZ8o2jk1Vej79tvA7/8pfkKMPLo0aM4GsH+0XK9txVeNiGLVNaqqiojZFFRkRXDeC8+InuWXdPGy/aTHv0qkZpRJ/nqrZu+I8y6GXHeXvQZWmp7uSNlkJTSiF4Dy7Uz6T9app2IG3PZDtRVpkcyucvXDstefQ0dVH1tba2R/vHjx1vb9VS10V6LK5e2vzfCZ8MKF4v3UzItVWlKpM9XLPLpKI0yNR1W3MGDB2F9njuKY9d1n88XNmlfs7jAqw3ys3/evHmBXq49TpEvjQtlZsWoTz5BU0oKtl1xBUZs346hO3ci79Zbsemznw1r+6zFizFh0SLjetW4cXjh+98PG5YXSMDrBO569FFk7pLdV8Xl33gjVt8Qetze6+X0kv1z584NMjeoh2yF6Cl30K+5xuHun5V5y/vGjRuxb98+fO5zn0Nqamqbq0ny7d/j979Ho4hu7UMPoXHCBIyRb+K+LTf4Jkrv+azPf75NnMCT/nv2AC2CnCkPkXw+Qlj1Lf/OO+9g8uTJGD9+fGsyB/OBXzi8JnrunPeR0bsK7//u2lY7dBycMbkQ59ySh40LLsaJXWfoMKE1zyt/tBBlhf2x4W8zW/10HIy7YhtGzfwUq35/LarLZFtuG9yP5BmZMdMiJ6x6gEtk5tE46WhMmTLFCJyphvNaBHmCfFYGRWjvkVOP7uqHH34I1VO/+eabo4sY49DHjh3DGnlu4fzzz0dOTk6MU48uuZelExnqF79KJUiQk5KSjNTVe//+/aPLKcahMzIyjBSzs7ORlpbWNvUePYCnnjL9Bg0CpEcsYxvAAdmXXlyvyy9Hr0j2ZwZ8YETsO1PWTIkTGO6kJFF2yDTBqb8NdSloakySfPXWjTVMUVmcpd2W5iYf6mtStdtRc1rapLjyo32huNjhespHIlKzVnmmp5vDJT3kM9LaXgM7NO3asR12Wmmqz636id5qh3XB4XdrmKJXr17abYlUdFN9I4Vw6zUlqKqHO3o05CsYeFoWqPnA3AoeTz4J6cq61XLaRQIkQAIhCQT1kEOGcqunGge7Vn66790LlMqNJdV1UI9Ot/QQ3Go27SIBEiCBUAS8LciqRGr2xdixocpGPxIgARLwFAHvC7KncMe/sb1QhoHySIdyJTgD5TDHus9Evqx00YhKmWx4PMKSRPFPiCUkgfAEvDuGHL5MvKKRQIqsaPEbeebuGdn/439xrYhwgzx395osoDnR8DtPnsijIwESCE3A24KclwesWweoNZHpXEFA9Y9/iecNW8bjY9yDB/BjfMU4XyWbMi0RHzoSIIHQBLwtyLm5wIUXAmvXhi4dfbUQWI/P4GXcb+R9uywelInTMpAxCI/Jznl0JEAC4Ql4W5DDl4tXNBOYh4dwJGBxoMdl37zTsk80HQmQQHgC8SPIap0JeRwa8nSfsepb+DLzigMEhqFAbunJdMQWdyVetA75TgIkEIZAfAjyE0+Yy2/KM/o47zxzW6f6+jBFprfdBNJkl72fyK4iylXBfGLtUryCz2K+4cc/JEACoQnEhyAvWwbcL2OWc+aYpVQrwC1fHrrE9LWdwBw8KIMVW4x8vo11WAv5ohT3I9yNoZA1ROhIgARCEogPQf7xjwFZzQrPysor6kk95bhQvcnB4b+T8QHUjTzl/izvBzEBT+Cp1p6yNePCYbOYHQl4gkB8PBgyLWD5K7XQkNprr7LSExUQb0Zuw0yZhdzcpljFGCZ95PI2fjwhARIIJhAfPeQ+ffwlU49S05EACZCABwnEhyB7EDxNJgESIIH2BCjI7YnwnARIgAQ0EYg/QbaGLFoW2tfEldmSAAmQQNQEvH1Tr+12gGbh+Rh11I2AEUiABNxBIP56yO7gSitIgARIIGoCFOSokTECCZAACdhDgIJsD1emSgIkQAJRE6AgR42MEUiABEjAHgIUZHu4MlUSIAESiJpAfAlyqFkXUSNhBBIgARLQQyA+BLm0FPj+94G//lUPReZKAiRAAjEgEB+CfMklwGOPAY2NMUDCJEiABEhADwHvC3JBAVBcbNJTq7x9+qkeksyVBEiABLpJwPuCfMMNwPHjJoaf/czcLaSbUBidBEiABHQQ8L4g33STn9s55wC33eY/5xEJkAAJeIiA9wX5EdmdYvRoE/m//Rvwq195CD9NJQESIAE/gaDFhZqamoyrzTKFrK6uzh9Sw5FlS0d2pIqtPrGvoaEBTZ20OS3wBqCUOVIe9S0bpjZKnMBwDXIPMbWHs2B8SVJWX7Pkq7duktMajIKnyLtuW1Tl+5KbtNuRlGLeVE7JqLfNlkb5eHbUxEO117SWz7VRae3asZ0tuLOfYTttUGkrbbDeAz/DhqeL/vhEeNvst6MMnzdvnotM7NiUOx54AL3lxt6qL38ZO2fO7DiChJi1eDEmLFpkhK0aNw4vqGlzdCQQpwTukj0nM3ftMkqXL7uzr1b3Xui0Epg7d25Q/kE9ZCtEamoqxowZY51qeT9y5AjKysowfvx4JEVY3zi1h9lNHdq/P3wTJnTK1oEffdQaLrNnT0yIEK+mpgb79u3DINmvr7/kYblTMrlj9SvWmTPvw6YeQEp6PQ6s01s3vQaVY/DZh3Fk+whUlfRypvBhchl18aeoKc/EkW0jwoRwxrv/qOPoO6IEB/LGoKEm1ZZML5ZbJtlDIietesi7d+822qpqs8qpNm65gQMGRGzvVrhYvBfKZsNVVVXGZzgW6XU1jYqKCtn3uBBDhw5Fn8At37qaYDfi7dixQ37lqt/0wS5IkC3hy8jIwKxZs4JjOOizevVqQ5AvuugipKWlhc952DBjl+kxzzyDMe+/D2zYED6sdWXFCusIUkMRy1pSUmII8siRIzF16tTWeAUbgcdfbD115KDPsFJk9qvA+hf11k3OBbsNQd69ciKKtuQ4UvZwmZw5Yw9OFfXTzuScW/IMQd76xgxUFmeFM7db/l/6BjBZpt1Hckp8lCAPlx3Yc3NzzaABIjQgJydie4+UdrTXli1bBtWh0a0lSozVa5z8GlYvnW779u1hO5jev6mnyH772ybf06eBjaKSLeNFOqEzbxIgARKIlkB8CPJddwFKjPfvN5/WSwnq+EfLheFJgARIwHEC8aNcvWQcU73oSIAESMCjBOKjh+xR+DSbBEiABAIJUJADafCYBEiABDQSoCBrhM+sSYAESCCQAAU5kAaPSYAESEAjAQqyRvjMmgRIgAQCCVCQA2nwmARIgAQ0EqAga4TPrEmABEggkAAFOZAGj0mABEhAIwEKskb4zJoESIAEAglQkANp8JgESIAENBKgIGuEz6xJgARIIJAABTmQBo9JgARIQCMBCrJG+MyaBEiABAIJUJADafCYBEiABDQSoCBrhM+sSYAESCCQAAU5kAaPSYAESEAjAQqyRvjMmgRIgAQCCVCQA2nwmARIgAQ0EqAga4TPrEmABEggkAAFOZAGj0mABEhAIwEKskb4zJoESIAEAglQkANp8JgESIAENBKgIGuEz6xJgARIIJAABTmQBo9JgARIQCMBCrJG+MyaBEiABAIJUJADafCYBEiABDQSoCBrhM+sSYAESCCQAAU5kAaPSYAESEAjgZT2eTc1NRleNTU1eO+999pfdvT8+PHjRn6rVq1CcnJyTPOetG8fBrWk2FxaipURylpbW2uE3LNnD0olrOXKTwIXftU6c+Y9a9ApJKfXS75666Zn/9NGgcdfvRVnnr/XmcKHySU5rQF9h5doZ9J3RLFh4bR/WYuG2tQw1nbPu+AwcKKDqq+vrzcyOXjwIKqqqozj2dJufS1ZH5e2vz1Ce++ehW1jFxcXo7GxUbuWVFZWGobl5+fj8GGBqNH5fD40NzeHtCBIkK1QqlILCgqsU63ve/fG/gM/5OTJVkGurq7uVFlLSkqgXoEuJzfwzLnjnFx31M2gcUXOFTpCTj36VsEtTIZN3R/B0u5dOnFKBFlenXGq82B1IHKljWe2RCqRtu/0Z9vp/MLxOXbsGNRLp1OCHM4FCbIVOCsrC3fccUe4eI74r1mzBuob7Stf+QrS0tJimqevSIRk0SIjzcyhQ3HvvfeGTV+J8KuvvooZM2Zg6tSpreEKNgLfuqD11JGDK36wCJn9KrD4gS84kl+4THJm7Eau9NLXPPkZFG3NCRfMEf9bHvsrivcOxuo/XONIfuEymXJzHiZ8djPe/OkdqCzOChesW/6Pvw9Mnhk5iYqKCvztb3/DOeecg9xcs8fgk3Ps2mVEnDB9Os6O0N4jpx7d1eXLlxs90rvvvju6iDEOXVhYiKVLl+Kyyy7DuHHjYpx6dMn96U9/CvuLP6wgqywscY4uu9iHVnbE3JZ231KR0reutbdDJdFsjvDEvtAdpNjcFP5btoOoMbnc3Gzmr95122IUSH4BarfDYiJ1Y5ctinq7phtUn+Haa2tAOz5PrYmHPrBsCn3Vfl8r//afYftzji4H3tSLjhdDkwAJkIBtBCjItqFlwiRAAiQQHQEKcnS8GJoESIAEbCNAQbYNLRMmARIggegIUJCj48XQJEACJGAbAQqybWiZMAmQAAlER4CCHB0vhiYBEiAB2whQkG1Dy4RJgARIIDoCFOToeDE0CZAACdhGgIJsG1om7CgBvQ8uOlpUr2VmPSXnNbvtsjcSDwqyXdSZLgmQAAlESYCCHCUwBicBEiABuwhQkO0iy3RJgARIIEoCFOQogTE4CZAACdhFgIJsF1mmSwIkQAJREqAgRwmMwUmABEjALgIUZLvIMl0SIAESiJIABTlKYAxOAiRAAnYRoCDbRZbpkgAJkECUBCjIUQJjcBIgARKwiwAF2S6yTJcESIAEoiRAQY4SGIOTAAmQgF0EKMh2kWW6JEACJBAlAQpylMAYnARIgATsIkBBtoss0yUBEiCBKAlQkKMExuAkQAIkYBcBCrJdZJkuCZAACURJgIIcJTAGJwESIAG7CFCQ7SLLdEmABEggSgIU5CiBMTgJkAAJ2EUgpX3CTU1NhldjYyOOHTvW/rKj59XV1UZ+x48fR2pqakzzHlRRgdZ9MVeuBHytZ0H59BefuUG+wFjxezeEv61ej5ip34c7bc2mw8TzJIS8QnHpMG6sA9wX6wS7mN5rEk9ec/H1LibQiWizOg7TS4JEqpdmafvHHfps19bWorm5WbuWlJWVGeDKy8u12xKpBn0CqzkwQENDA+bNmxfoFZfHsxYvxoRFi+KybCwUCUQikH/jjVh9ww2RgvCaAwTmzg3+2gzqISs7hg8fbpgzcOBAB8wKn8WJEyeMi9nZ2UhOTg4fsCtX+vQBNm8GDh7sSmzGIQHPEmieMwfTRoxwxH7rM6xbS1TPWPXWlY4oPdHpFJNwv/iDFsUyGgAAAHxJREFUesg6DXU8bzUksm6d49kyQxLQRmD2bG1ZM+OOCSS2IHfMhyFIgARIwDECnGXhGGpmRAIkQAKRCVCQI/PhVRIgARJwjAAF2THUzIgESIAEIhOgIEfmw6skQAIk4BgBCrJjqJkRCZAACUQmQEGOzIdXSYAESMAxAv8fwJTYsV9i7dYAAAAASUVORK5CYII="}}},{"cell_type":"code","source":"def form_label_grid(self, bboxes):\n    label_grid = np.zeros((32, 32, 10))\n\n    for i in range(0, 32):\n        for j in range(0, 32):\n            cell = self.image_grid[i,j]\n            label_grid[i,j] = self.rect_intersect(cell, bboxes)\n\n    return label_grid\n\n\ndef rect_intersect(self, cell, bboxes): \n    cell_x, cell_y, cell_width, cell_height = cell\n    cell_x_max = cell_x + cell_width \n    cell_y_max = cell_y + cell_height\n    \n    anchor_one = np.array([0, 0, 0, 0, 0])\n    anchor_two = np.array([0, 0, 0, 0, 0])\n\n    # check all boxes\n    for bbox in bboxes:\n        box_x, box_y, box_width, box_height = bbox\n        box_x_centre = box_x + (box_width / 2)\n        box_y_centre = box_y + (box_height / 2)\n\n        if(box_x_centre >= cell_x and box_x_centre < cell_x_max and box_y_centre >= cell_y and box_y_centre < cell_y_max):\n            \n            if anchor_one[0] == 0:\n                anchor_one = self.yolo_shape(\n                    [box_x, box_y, box_width, box_height], \n                    [cell_x, cell_y, cell_width, cell_height]\n                )\n            \n            elif anchor_two[0] == 0:\n                anchor_two = self.yolo_shape(\n                    [box_x, box_y, box_width, box_height], \n                    [cell_x, cell_y, cell_width, cell_height]\n                )\n                \n            else:\n                break\n\n    return np.concatenate((anchor_one, anchor_two), axis=None)\n\n\ndef yolo_shape(self, box, cell):\n    box_x, box_y, box_width, box_height = box\n    cell_x, cell_y, cell_width, cell_height = cell\n\n    # top left x,y to centre x,y\n    box_x = box_x + (box_width / 2)\n    box_y = box_y + (box_height / 2)\n\n    # offset bbox x,y to cell x,y\n    box_x = (box_x - cell_x) / cell_width\n    box_y = (box_y - cell_y) / cell_height\n\n    # bbox width,height relative to cell width,height\n    box_width = box_width / 256\n    box_height = box_height / 256\n\n    return [1, box_x, box_y, box_width, box_height]\n\n\nDataGenerator.form_label_grid = form_label_grid\nDataGenerator.rect_intersect = rect_intersect\nDataGenerator.yolo_shape = yolo_shape","metadata":{"execution":{"iopub.status.busy":"2022-03-19T20:11:24.098916Z","iopub.execute_input":"2022-03-19T20:11:24.099535Z","iopub.status.idle":"2022-03-19T20:11:24.118185Z","shell.execute_reply.started":"2022-03-19T20:11:24.099496Z","shell.execute_reply":"2022-03-19T20:11:24.117407Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_generator = DataGenerator(\n    train_image_ids,\n    train_pixels,\n    train_labels, \n    batch_size=6, \n    shuffle=True,\n    augment=True\n)\n\nval_generator = DataGenerator(\n    val_image_ids, \n    val_pixels,\n    val_labels, \n    batch_size=10,\n    shuffle=False,\n    augment=False\n)\n\nimage_grid = train_generator.image_grid","metadata":{"execution":{"iopub.status.busy":"2022-03-19T20:11:24.119611Z","iopub.execute_input":"2022-03-19T20:11:24.120113Z","iopub.status.idle":"2022-03-19T20:11:24.13409Z","shell.execute_reply.started":"2022-03-19T20:11:24.119986Z","shell.execute_reply":"2022-03-19T20:11:24.133334Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Model\n\n\n![Screenshot%202020-07-13%20at%2010.42.35.png](attachment:Screenshot%202020-07-13%20at%2010.42.35.png)\n*figure 3, An outline of the model taken from the yolov3 [paper](https://pjreddie.com/media/files/papers/YOLOv3.pdf).*\n\nBelow is my keras implementation of the model. The model is mostly in line with the yolov3 architecture though I have removed a few layers and altered some others. \n\nVề phần model thì inspired từ yolo, cụ thể yolo-v3 nhưng modify nhỏ hơn . Cụ thể bắt đầu với convolutional layer with 32 filters, sau đó giảm 1 nưza trước khi upto 128 layers, lặp lại vài lần cho tới đạt được size of 1024. Một vài resnet skip layers được thêm vào để ổn định số lớn của layer và giảm vanishing gradients","metadata":{},"attachments":{"Screenshot%202020-07-13%20at%2010.42.35.png":{"image/png":"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"}}},{"cell_type":"code","source":"x_input = tf.keras.Input(shape=(256,256,3))\n\nx = tf.keras.layers.Conv2D(32, (3, 3), strides=(1, 1), padding='same')(x_input)\nx = tf.keras.layers.BatchNormalization()(x)\nx = tf.keras.layers.LeakyReLU(alpha=0.1)(x)\n\n########## block 1 ##########\nx = tf.keras.layers.Conv2D(64, (3, 3), strides=(1, 1), padding='same')(x)\nx = tf.keras.layers.BatchNormalization()(x)\nx = tf.keras.layers.LeakyReLU(alpha=0.1)(x)\n\nx_shortcut = x\n\nfor i in range(2):\n    x = tf.keras.layers.Conv2D(32, (3, 3), strides=(1, 1), padding='same')(x)\n    x = tf.keras.layers.BatchNormalization()(x)\n    x = tf.keras.layers.LeakyReLU(alpha=0.1)(x)\n\n    x = tf.keras.layers.Conv2D(64, (3, 3), strides=(1, 1), padding='same')(x)\n    x = tf.keras.layers.BatchNormalization()(x)\n    x = tf.keras.layers.LeakyReLU(alpha=0.1)(x)\n\n    x = tf.keras.layers.Add()([x_shortcut, x])\n    x = tf.keras.layers.LeakyReLU(alpha=0.1)(x)\n\n    x_shortcut = x\n\n\n########## block 2 ##########\nx = tf.keras.layers.Conv2D(128, (3, 3), strides=(1, 1), padding='same')(x)\nx = tf.keras.layers.BatchNormalization()(x)\nx = tf.keras.layers.LeakyReLU(alpha=0.1)(x)\n\nx_shortcut = x\n\nfor i in range(2):\n    x = tf.keras.layers.Conv2D(64, (3, 3), strides=(1, 1), padding='same')(x)\n    x = tf.keras.layers.BatchNormalization()(x)\n    x = tf.keras.layers.LeakyReLU(alpha=0.1)(x)\n\n    x = tf.keras.layers.Conv2D(128, (3, 3), strides=(1, 1), padding='same')(x)\n    x = tf.keras.layers.BatchNormalization()(x)\n    x = tf.keras.layers.LeakyReLU(alpha=0.1)(x)\n\n    x = tf.keras.layers.Add()([x_shortcut, x])\n    x = tf.keras.layers.LeakyReLU(alpha=0.1)(x)\n\n    x_shortcut = x\n\n########## block 3 ##########\nx = tf.keras.layers.Conv2D(256, (3, 3), strides=(2, 2), padding='same')(x)\nx = tf.keras.layers.BatchNormalization()(x)\nx = tf.keras.layers.LeakyReLU(alpha=0.1)(x)\n\nx_shortcut = x\n\nfor i in range(8):\n    x = tf.keras.layers.Conv2D(128, (3, 3), strides=(1, 1), padding='same')(x)\n    x = tf.keras.layers.BatchNormalization()(x)\n    x = tf.keras.layers.LeakyReLU(alpha=0.1)(x)\n\n    x = tf.keras.layers.Conv2D(256, (3, 3), strides=(1, 1), padding='same')(x)\n    x = tf.keras.layers.BatchNormalization()(x)\n    x = tf.keras.layers.LeakyReLU(alpha=0.1)(x)\n\n    x = tf.keras.layers.Add()([x_shortcut, x])\n    x = tf.keras.layers.LeakyReLU(alpha=0.1)(x)\n\n    x_shortcut = x\n\n    \n########## block 4 ##########\nx = tf.keras.layers.Conv2D(512, (3, 3), strides=(2, 2), padding='same')(x)\nx = tf.keras.layers.BatchNormalization()(x)\nx = tf.keras.layers.LeakyReLU(alpha=0.1)(x)\n\nx_shortcut = x\n\nfor i in range(8):\n    x = tf.keras.layers.Conv2D(256, (3, 3), strides=(1, 1), padding='same')(x)\n    x = tf.keras.layers.BatchNormalization()(x)\n    x = tf.keras.layers.LeakyReLU(alpha=0.1)(x)\n\n    x = tf.keras.layers.Conv2D(512, (3, 3), strides=(1, 1), padding='same')(x)\n    x = tf.keras.layers.BatchNormalization()(x)\n    x = tf.keras.layers.LeakyReLU(alpha=0.1)(x)\n\n    x = tf.keras.layers.Add()([x_shortcut, x])\n    x = tf.keras.layers.LeakyReLU(alpha=0.1)(x)\n\n    x_shortcut = x\n\n########## block 5 ##########\nx = tf.keras.layers.Conv2D(1024, (3, 3), strides=(2, 2), padding='same')(x)\nx = tf.keras.layers.BatchNormalization()(x)\nx = tf.keras.layers.LeakyReLU(alpha=0.1)(x)\n\nx_shortcut = x\n\nfor i in range(4):\n    x = tf.keras.layers.Conv2D(512, (3, 3), strides=(1, 1), padding='same')(x)\n    x = tf.keras.layers.BatchNormalization()(x)\n    x = tf.keras.layers.LeakyReLU(alpha=0.1)(x)\n\n    x = tf.keras.layers.Conv2D(1024, (3, 3), strides=(1, 1), padding='same')(x)\n    x = tf.keras.layers.BatchNormalization()(x)\n    x = tf.keras.layers.LeakyReLU(alpha=0.1)(x)\n\n    x = tf.keras.layers.Add()([x_shortcut, x])\n    x = tf.keras.layers.LeakyReLU(alpha=0.1)(x)\n\n    x_shortcut = x\n\n########## output layers ##########\nx = tf.keras.layers.Conv2D(512, (3, 3), strides=(1, 1), padding='same')(x)\nx = tf.keras.layers.BatchNormalization()(x)\nx = tf.keras.layers.LeakyReLU(alpha=0.1)(x)\n\nx = tf.keras.layers.Conv2D(256, (3, 3), strides=(1, 1), padding='same')(x)\nx = tf.keras.layers.BatchNormalization()(x)\nx = tf.keras.layers.LeakyReLU(alpha=0.1)(x)\n\nx = tf.keras.layers.Conv2D(128, (3, 3), strides=(1, 1), padding='same')(x)\nx = tf.keras.layers.BatchNormalization()(x)\nx = tf.keras.layers.LeakyReLU(alpha=0.1)(x)\n\npredictions = tf.keras.layers.Conv2D(10, (1, 1), strides=(1, 1), activation='sigmoid')(x)\n\nmodel = tf.keras.Model(inputs=x_input, outputs=predictions)","metadata":{"execution":{"iopub.status.busy":"2022-03-19T20:11:24.137104Z","iopub.execute_input":"2022-03-19T20:11:24.137435Z","iopub.status.idle":"2022-03-19T20:11:28.559689Z","shell.execute_reply.started":"2022-03-19T20:11:24.137403Z","shell.execute_reply":"2022-03-19T20:11:28.55893Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n\nVề hàm loss thì chia làm 3 phần\n- confidence score, trying to work out if a label grid cell contains a head of wheat or not,binary crossentropy sử dụng cho binary clasification task\n- x,y position of bbox , MSE\n- width, height , MSE\n\nMột vấn đề là nhiều cells nó không có object, do đó tăng weights 5 lần nếu có bbox, giảm 1/2 nếu không có bbox","metadata":{}},{"cell_type":"code","source":"def custom_loss(y_true, y_pred):\n    binary_crossentropy = prob_loss = tf.keras.losses.BinaryCrossentropy(\n        reduction=tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE\n    )\n    \n    prob_loss = binary_crossentropy(\n        tf.concat([y_true[:,:,:,0], y_true[:,:,:,5]], axis=0), \n        tf.concat([y_pred[:,:,:,0], y_pred[:,:,:,5]], axis=0)\n    )\n    \n    xy_loss = tf.keras.losses.MSE(\n        tf.concat([y_true[:,:,:,1:3], y_true[:,:,:,6:8]], axis=0), \n        tf.concat([y_pred[:,:,:,1:3], y_pred[:,:,:,6:8]], axis=0)\n    )\n    \n    wh_loss = tf.keras.losses.MSE(\n        tf.concat([y_true[:,:,:,3:5], y_true[:,:,:,8:10]], axis=0), \n        tf.concat([y_pred[:,:,:,3:5], y_pred[:,:,:,8:10]], axis=0)\n    )\n    \n    bboxes_mask = get_mask(y_true)\n    \n    xy_loss = xy_loss * bboxes_mask\n    wh_loss = wh_loss * bboxes_mask\n    \n    return prob_loss + xy_loss + wh_loss\n\n\ndef get_mask(y_true):\n    anchor_one_mask = tf.where(\n        y_true[:,:,:,0] == 0, \n        0.5, \n        5.0\n    )\n    \n    anchor_two_mask = tf.where(\n        y_true[:,:,:,5] == 0, \n        0.5, \n        5.0\n    )\n    \n    bboxes_mask = tf.concat(\n        [anchor_one_mask,anchor_two_mask],\n        axis=0\n    )\n    \n    return bboxes_mask","metadata":{"execution":{"iopub.status.busy":"2022-03-19T20:11:28.560924Z","iopub.execute_input":"2022-03-19T20:11:28.561257Z","iopub.status.idle":"2022-03-19T20:11:28.579922Z","shell.execute_reply.started":"2022-03-19T20:11:28.561222Z","shell.execute_reply":"2022-03-19T20:11:28.57914Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Optmization sử dụng Adam","metadata":{}},{"cell_type":"code","source":"optimiser = tf.keras.optimizers.Adam(learning_rate=0.0001)\n\nmodel.compile(\n    optimizer=optimiser, \n    loss=custom_loss\n)","metadata":{"execution":{"iopub.status.busy":"2022-03-19T20:11:28.581469Z","iopub.execute_input":"2022-03-19T20:11:28.581976Z","iopub.status.idle":"2022-03-19T20:11:28.677181Z","shell.execute_reply.started":"2022-03-19T20:11:28.581933Z","shell.execute_reply":"2022-03-19T20:11:28.676521Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Sử dụng leaarningrate decay và early stopping","metadata":{}},{"cell_type":"code","source":"callbacks = [\n    tf.keras.callbacks.ReduceLROnPlateau(monitor='loss', patience=2, verbose=1),\n    tf.keras.callbacks.EarlyStopping(monitor='loss', patience=5, verbose=1, restore_best_weights=True),\n]","metadata":{"execution":{"iopub.status.busy":"2022-03-19T20:11:28.678578Z","iopub.execute_input":"2022-03-19T20:11:28.678933Z","iopub.status.idle":"2022-03-19T20:11:28.686822Z","shell.execute_reply.started":"2022-03-19T20:11:28.678896Z","shell.execute_reply":"2022-03-19T20:11:28.68608Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"history = model.fit_generator(\n    train_generator,\n    validation_data=val_generator,\n    epochs=10,\n    callbacks=callbacks\n)","metadata":{"execution":{"iopub.status.busy":"2022-03-19T20:11:28.690335Z","iopub.execute_input":"2022-03-19T20:11:28.690616Z","iopub.status.idle":"2022-03-19T21:37:26.692199Z","shell.execute_reply.started":"2022-03-19T20:11:28.690589Z","shell.execute_reply":"2022-03-19T21:37:26.691446Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Prediction post processing\n\n\nĐể visualize bbox trên image và submit thì cần reshape from (11,16,10) to (m,4) , m là số lượng bbox có high confidence\n\nCần transform từ yolo format to côco format \n- return the scale of the boxes from 0-1 to 0-256\n- change the x,y from the centre of the box to the top left corner\n- change width and height to x_max, y_max i.e. change to voc shape","metadata":{}},{"cell_type":"code","source":"def prediction_to_bbox(bboxes, image_grid):    \n    bboxes = bboxes.copy()\n    \n    im_width = (image_grid[:,:,2] * 32)\n    im_height = (image_grid[:,:,3] * 32)\n    \n    # descale x,y\n    bboxes[:,:,1] = (bboxes[:,:,1] * image_grid[:,:,2]) + image_grid[:,:,0]\n    bboxes[:,:,2] = (bboxes[:,:,2] * image_grid[:,:,3]) + image_grid[:,:,1]\n    bboxes[:,:,6] = (bboxes[:,:,6] * image_grid[:,:,2]) + image_grid[:,:,0]\n    bboxes[:,:,7] = (bboxes[:,:,7] * image_grid[:,:,3]) + image_grid[:,:,1]\n    \n    # descale width,height\n    bboxes[:,:,3] = bboxes[:,:,3] * im_width \n    bboxes[:,:,4] = bboxes[:,:,4] * im_height\n    bboxes[:,:,8] = bboxes[:,:,8] * im_width \n    bboxes[:,:,9] = bboxes[:,:,9] * im_height\n    \n    # centre x,y to top left x,y\n    bboxes[:,:,1] = bboxes[:,:,1] - (bboxes[:,:,3] / 2)\n    bboxes[:,:,2] = bboxes[:,:,2] - (bboxes[:,:,4] / 2)\n    bboxes[:,:,6] = bboxes[:,:,6] - (bboxes[:,:,8] / 2)\n    bboxes[:,:,7] = bboxes[:,:,7] - (bboxes[:,:,9] / 2)\n    \n    # width,heigth to x_max,y_max\n    bboxes[:,:,3] = bboxes[:,:,1] + bboxes[:,:,3]\n    bboxes[:,:,4] = bboxes[:,:,2] + bboxes[:,:,4]\n    bboxes[:,:,8] = bboxes[:,:,6] + bboxes[:,:,8]\n    bboxes[:,:,9] = bboxes[:,:,7] + bboxes[:,:,9]\n    \n    return bboxes","metadata":{"execution":{"iopub.status.busy":"2022-03-19T21:37:26.693992Z","iopub.execute_input":"2022-03-19T21:37:26.69439Z","iopub.status.idle":"2022-03-19T21:37:26.713268Z","shell.execute_reply.started":"2022-03-19T21:37:26.694348Z","shell.execute_reply":"2022-03-19T21:37:26.712414Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Next the bounding boxes with low confidence need removing","metadata":{}},{"cell_type":"code","source":"def non_max_suppression(predictions, top_n):\n    probabilities = np.concatenate((predictions[:,:,0].flatten(), predictions[:,:,5].flatten()), axis=None)\n    \n    first_anchors = predictions[:,:,1:5].reshape((32*32, 4))\n    second_anchors = predictions[:,:,6:10].reshape((32*32, 4))\n    \n    bboxes = np.concatenate(\n        (first_anchors,second_anchors),\n        axis=0\n    )\n    \n    bboxes = switch_x_y(bboxes)\n    bboxes, probabilities = select_top(probabilities, bboxes, top_n=top_n)\n    bboxes = switch_x_y(bboxes)\n    \n    return bboxes\n\n\ndef switch_x_y(bboxes):\n    x1 = bboxes[:,0].copy()\n    y1 = bboxes[:,1].copy()\n    x2 = bboxes[:,2].copy()\n    y2 = bboxes[:,3].copy()\n    \n    bboxes[:,0] = y1\n    bboxes[:,1] = x1\n    bboxes[:,2] = y2\n    bboxes[:,3] = x2\n    \n    return bboxes\n\n\ndef select_top(probabilities, boxes, top_n=10):\n    top_indices = tf.image.non_max_suppression(\n        boxes = boxes, \n        scores = probabilities, \n        max_output_size = top_n, \n        iou_threshold = 0.3,\n        score_threshold = 0.3\n    )\n    \n    top_indices = top_indices.numpy()\n    \n    return boxes[top_indices], probabilities[top_indices]","metadata":{"execution":{"iopub.status.busy":"2022-03-19T21:37:26.714815Z","iopub.execute_input":"2022-03-19T21:37:26.715234Z","iopub.status.idle":"2022-03-19T21:37:26.731326Z","shell.execute_reply.started":"2022-03-19T21:37:26.715196Z","shell.execute_reply":"2022-03-19T21:37:26.730659Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Wrap these post-processing functions into one and output the predicted bounding boxes as a dictionary where the image id is the key.","metadata":{}},{"cell_type":"code","source":"def process_predictions(predictions, image_ids, image_grid):\n    bboxes = {}\n    \n    for i, image_id in enumerate(image_ids):\n        predictions[i] = prediction_to_bbox(predictions[i], image_grid)\n        bboxes[image_id] = non_max_suppression(predictions[i], top_n=100)\n        \n        # back to coco shape\n        bboxes[image_id][:,2:4] = bboxes[image_id][:,2:4] - bboxes[image_id][:,0:2]\n    \n    return bboxes","metadata":{"execution":{"iopub.status.busy":"2022-03-19T21:37:26.732918Z","iopub.execute_input":"2022-03-19T21:37:26.733383Z","iopub.status.idle":"2022-03-19T21:37:26.74224Z","shell.execute_reply.started":"2022-03-19T21:37:26.733346Z","shell.execute_reply":"2022-03-19T21:37:26.741566Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"val_predictions = model.predict(val_generator)\nval_predictions = process_predictions(val_predictions, val_image_ids, image_grid)","metadata":{"execution":{"iopub.status.busy":"2022-03-19T21:37:26.745385Z","iopub.execute_input":"2022-03-19T21:37:26.745631Z","iopub.status.idle":"2022-03-19T21:37:31.631682Z","shell.execute_reply.started":"2022-03-19T21:37:26.7456Z","shell.execute_reply":"2022-03-19T21:37:31.630782Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"show_images(val_image_ids[0:4], val_predictions)","metadata":{"execution":{"iopub.status.busy":"2022-03-19T21:37:31.633143Z","iopub.execute_input":"2022-03-19T21:37:31.633531Z","iopub.status.idle":"2022-03-19T21:37:32.389851Z","shell.execute_reply.started":"2022-03-19T21:37:31.633494Z","shell.execute_reply":"2022-03-19T21:37:32.389078Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"show_images(val_image_ids[4:8], val_predictions)","metadata":{"execution":{"iopub.status.busy":"2022-03-19T21:37:32.394862Z","iopub.execute_input":"2022-03-19T21:37:32.395418Z","iopub.status.idle":"2022-03-19T21:37:33.071758Z","shell.execute_reply.started":"2022-03-19T21:37:32.395373Z","shell.execute_reply":"2022-03-19T21:37:33.067892Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Evaluate Model\n\nWith the model trained it's time to look at the quality of the model. Begin by plotting the loss curve.","metadata":{}},{"cell_type":"code","source":"print('Epochs: ' + str(len(history.history['loss'])))\nprint('Final training loss: ' + str(history.history['loss'][-1]))\nprint('Final validation loss: ' + str(history.history['val_loss'][-1]))","metadata":{"execution":{"iopub.status.busy":"2022-03-19T21:37:33.073252Z","iopub.execute_input":"2022-03-19T21:37:33.073788Z","iopub.status.idle":"2022-03-19T21:37:33.08288Z","shell.execute_reply.started":"2022-03-19T21:37:33.073745Z","shell.execute_reply":"2022-03-19T21:37:33.081902Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig, ax = plt.subplots(1, 2, figsize=(15, 5))\n\nax[0].set_title('Training Loss')\nax[0].plot(history.history['loss'])\n\nax[1].set_title('Validation Loss')\nax[1].plot(history.history['val_loss'])","metadata":{"execution":{"iopub.status.busy":"2022-03-19T21:37:33.08443Z","iopub.execute_input":"2022-03-19T21:37:33.085168Z","iopub.status.idle":"2022-03-19T21:37:33.330694Z","shell.execute_reply.started":"2022-03-19T21:37:33.085085Z","shell.execute_reply":"2022-03-19T21:37:33.330028Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Then visualise the first few layers of the model to see how each layer influences the bounding boxes. Start by copying the model and configuring the new one to return each layers output when a prediction is made.","metadata":{}},{"cell_type":"code","source":"layer_outputs = [layer.output for layer in model.layers]\nevaluation_model = tf.keras.Model(inputs=model.input, outputs=layer_outputs)","metadata":{"execution":{"iopub.status.busy":"2022-03-19T21:37:33.3321Z","iopub.execute_input":"2022-03-19T21:37:33.332501Z","iopub.status.idle":"2022-03-19T21:37:33.367799Z","shell.execute_reply.started":"2022-03-19T21:37:33.332462Z","shell.execute_reply":"2022-03-19T21:37:33.367169Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Then pick an image and cycle through the layers making a prediction and visualising the features outputted by the layer. The warm colours represent where the features lie in this image.\n\nNote that I have not visualised all the layers here due to the large number of them.","metadata":{}},{"cell_type":"code","source":"image = Image.open('../input/global-wheat-detection/train/' + train_image_ids[1] + \".jpg\")\nimage = image.resize((256, 256))\n\npixels = np.asarray(image) / 255\npixels = np.expand_dims(pixels, axis=0)","metadata":{"execution":{"iopub.status.busy":"2022-03-19T21:37:33.370685Z","iopub.execute_input":"2022-03-19T21:37:33.370937Z","iopub.status.idle":"2022-03-19T21:37:33.406011Z","shell.execute_reply.started":"2022-03-19T21:37:33.370914Z","shell.execute_reply":"2022-03-19T21:37:33.405388Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"num_of_layers = len(layer_outputs)\n\nfig, axes = plt.subplots(2, 6, figsize=(20, 10))\n\nlayer = 0\nfor i in range(0, 2):\n    for j in range(0, 6):\n        layer_output = evaluation_model.predict(pixels)[layer]\n        axes[i, j].imshow(layer_output[0, :, :, 1], cmap='inferno')\n        \n        layer = layer + 1","metadata":{"execution":{"iopub.status.busy":"2022-03-19T21:37:33.407287Z","iopub.execute_input":"2022-03-19T21:37:33.407658Z","iopub.status.idle":"2022-03-19T21:38:03.401988Z","shell.execute_reply.started":"2022-03-19T21:37:33.407621Z","shell.execute_reply":"2022-03-19T21:38:03.401203Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Test Images\n\nFinally predict bounding boxes for the test set. I wanted to see how easy/difficult it would be to use the model without a data generator class so I have inputted the images into the model as numpy arrays.\n\nFirst, load the test image ids.","metadata":{}},{"cell_type":"code","source":"test_image_ids = os.listdir('/kaggle/input/global-wheat-detection/test/')\ntest_image_ids = [image_id[:-4] for image_id in test_image_ids]","metadata":{"execution":{"iopub.status.busy":"2022-03-19T21:38:03.403405Z","iopub.execute_input":"2022-03-19T21:38:03.403928Z","iopub.status.idle":"2022-03-19T21:38:03.416615Z","shell.execute_reply.started":"2022-03-19T21:38:03.403892Z","shell.execute_reply":"2022-03-19T21:38:03.415702Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Now loop through the images, loading each one as a numpy array, applying augmentations to it and feeding it into the model. Save the bounding box predictions as a numpy array.","metadata":{}},{"cell_type":"code","source":"test_predictions = []\n\nfor i, image_id in enumerate(test_image_ids):\n    image = Image.open('/kaggle/input/global-wheat-detection/test/' + image_id + \".jpg\")\n    image = image.resize((256, 256))            \n\n    pixels = np.asarray(image)\n\n    val_augmentations = albu.Compose([\n        albu.CLAHE(p=1),\n        albu.ToGray(p=1)\n    ])\n\n    aug_result = val_augmentations(image=pixels)\n    pixels = np.array(aug_result['image']) / 255\n    \n    pixels = np.expand_dims(pixels, axis=0)\n    \n    bboxes = model.predict(pixels)\n\n    test_predictions.append(bboxes)\n    \ntest_predictions = np.concatenate(test_predictions)","metadata":{"execution":{"iopub.status.busy":"2022-03-19T21:38:03.417997Z","iopub.execute_input":"2022-03-19T21:38:03.418556Z","iopub.status.idle":"2022-03-19T21:38:05.608536Z","shell.execute_reply.started":"2022-03-19T21:38:03.418517Z","shell.execute_reply":"2022-03-19T21:38:05.607752Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Then apply the typical post processing functions to the predictions.","metadata":{}},{"cell_type":"code","source":"test_predictions = process_predictions(test_predictions, test_image_ids, image_grid)","metadata":{"execution":{"iopub.status.busy":"2022-03-19T21:38:05.609713Z","iopub.execute_input":"2022-03-19T21:38:05.610047Z","iopub.status.idle":"2022-03-19T21:38:05.620393Z","shell.execute_reply.started":"2022-03-19T21:38:05.610014Z","shell.execute_reply":"2022-03-19T21:38:05.619712Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Although only a submission to the competition will provide a final score on how good the model is, I'll visualise each test image with their predicted boxes to get an idea of the models quality. This involves a slightly different visualisation function just to accomodate the larger images.","metadata":{}},{"cell_type":"code","source":"show_images(test_image_ids[0:4], test_predictions, source='test')","metadata":{"execution":{"iopub.status.busy":"2022-03-19T21:38:05.622028Z","iopub.execute_input":"2022-03-19T21:38:05.622435Z","iopub.status.idle":"2022-03-19T21:38:06.319471Z","shell.execute_reply.started":"2022-03-19T21:38:05.6224Z","shell.execute_reply":"2022-03-19T21:38:06.318481Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"show_images(test_image_ids[4:8], test_predictions, source='test')","metadata":{"execution":{"iopub.status.busy":"2022-03-19T21:38:06.320938Z","iopub.execute_input":"2022-03-19T21:38:06.321282Z","iopub.status.idle":"2022-03-19T21:38:07.019968Z","shell.execute_reply.started":"2022-03-19T21:38:06.321247Z","shell.execute_reply":"2022-03-19T21:38:07.019071Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"show_images(test_image_ids[8:10], test_predictions, source='test')","metadata":{"execution":{"iopub.status.busy":"2022-03-19T21:38:07.021222Z","iopub.execute_input":"2022-03-19T21:38:07.021722Z","iopub.status.idle":"2022-03-19T21:38:07.38687Z","shell.execute_reply.started":"2022-03-19T21:38:07.021683Z","shell.execute_reply":"2022-03-19T21:38:07.386116Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Finally save the weights so that the model can be used in an inference notebook.","metadata":{}},{"cell_type":"code","source":"model.save_weights('wheat_detection_model')","metadata":{"execution":{"iopub.status.busy":"2022-03-19T21:38:07.388011Z","iopub.execute_input":"2022-03-19T21:38:07.388461Z","iopub.status.idle":"2022-03-19T21:38:08.650108Z","shell.execute_reply.started":"2022-03-19T21:38:07.388428Z","shell.execute_reply":"2022-03-19T21:38:08.649372Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]}]}