{"cells":[{"metadata":{"trusted":true},"cell_type":"code","source":"import os \nimport sys\nimport random\nimport math\nimport numpy as np\nimport cv2\nimport matplotlib.pyplot as plt\nimport json\nimport pydicom\nfrom imgaug import augmenters as iaa\nfrom tqdm import tqdm\nimport pandas as pd \nimport glob\nimport keras","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"DATA_DIR = '/kaggle/input'\n\n# Directory to save logs and trained model\nROOT_DIR = '/kaggle/working'","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!git clone https://www.github.com/matterport/Mask_RCNN.git\nos.chdir('Mask_RCNN')\n#!python setup.py -q install","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Import Mask RCNN\nsys.path.append(os.path.join(ROOT_DIR, 'Mask_RCNN'))  # To find local version of the library\nfrom mrcnn.config import Config\nfrom mrcnn import utils\nimport mrcnn.model as modellib\nfrom mrcnn import visualize\nfrom mrcnn.model import log","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_dicom_dir = os.path.join(DATA_DIR, 'rsna-pneumonia-detection-challenge/stage_2_train_images')\ntest_dicom_dir = os.path.join(DATA_DIR, 'rsna-pneumonia-detection-challenge/stage_2_test_images')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"### Download COCO pre-trained weights\n!wget --quiet https://github.com/matterport/Mask_RCNN/releases/download/v2.0/mask_rcnn_coco.h5\n!ls -lh mask_rcnn_coco.h5\n\nCOCO_WEIGHTS_PATH = \"mask_rcnn_coco.h5\"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def get_dicom_fps(dicom_dir):\n    dicom_fps = glob.glob(dicom_dir+'/'+'*.dcm')\n    return list(set(dicom_fps))\n\ndef parse_dataset(dicom_dir, anns): \n    image_fps = get_dicom_fps(dicom_dir)\n    image_annotations = {fp: [] for fp in image_fps}\n    for index, row in anns.iterrows(): \n        fp = os.path.join(dicom_dir, row['patientId']+'.dcm')\n        image_annotations[fp].append(row)\n    return image_fps, image_annotations ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# The following parameters have been selected to reduce running time for demonstration purposes \n# These are not optimal \n\nclass DetectorConfig(Config):\n    \"\"\"Configuration for training pneumonia detection on the RSNA pneumonia dataset.\n    Overrides values in the base Config class.\n    \"\"\"\n    \n    # Give the configuration a recognizable name  \n    NAME = 'pneumonia'\n    \n    # Train on 1 GPU and 8 images per GPU. We can put multiple images on each\n    # GPU because the images are small. Batch size is 8 (GPUs * images/GPU).\n    GPU_COUNT = 1\n    IMAGES_PER_GPU = 8\n    \n    BACKBONE = 'resnet50'\n    \n    NUM_CLASSES = 2  # background + 1 pneumonia classes\n    \n    IMAGE_MIN_DIM = 256\n    IMAGE_MAX_DIM = 256\n    RPN_ANCHOR_SCALES = (32, 64, 128, 256)\n    TRAIN_ROIS_PER_IMAGE = 32\n    MAX_GT_INSTANCES = 3\n    DETECTION_MAX_INSTANCES = 3\n    DETECTION_MIN_CONFIDENCE = 0.7\n    DETECTION_NMS_THRESHOLD = 0.1\n\n    STEPS_PER_EPOCH = 100\n    \nconfig = DetectorConfig()\nconfig.display()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"class DetectorDataset(utils.Dataset):\n    \"\"\"Dataset class for training pneumonia detection on the RSNA pneumonia dataset.\n    \"\"\"\n\n    def __init__(self, image_fps, image_annotations, orig_height, orig_width):\n        super().__init__(self)\n        \n        # Add classes\n        self.add_class('pneumonia', 1, 'Lung Opacity')\n        \n        # add images \n        for i, fp in enumerate(image_fps):\n            annotations = image_annotations[fp]\n            self.add_image('pneumonia', image_id=i, path=fp, \n                           annotations=annotations, orig_height=orig_height, orig_width=orig_width)\n            \n    def image_reference(self, image_id):\n        info = self.image_info[image_id]\n        return info['path']\n\n    def load_image(self, image_id):\n        info = self.image_info[image_id]\n        fp = info['path']\n        ds = pydicom.read_file(fp)\n        image = ds.pixel_array\n        # If grayscale. Convert to RGB for consistency.\n        if len(image.shape) != 3 or image.shape[2] != 3:\n            image = np.stack((image,) * 3, -1)\n        return image\n\n    def load_mask(self, image_id):\n        info = self.image_info[image_id]\n        annotations = info['annotations']\n        count = len(annotations)\n        if count == 0:\n            mask = np.zeros((info['orig_height'], info['orig_width'], 1), dtype=np.uint8)\n            class_ids = np.zeros((1,), dtype=np.int32)\n        else:\n            mask = np.zeros((info['orig_height'], info['orig_width'], count), dtype=np.uint8)\n            class_ids = np.zeros((count,), dtype=np.int32)\n            for i, a in enumerate(annotations):\n                if a['Target'] == 1:\n                    x = int(a['x'])\n                    y = int(a['y'])\n                    w = int(a['width'])\n                    h = int(a['height'])\n                    mask_instance = mask[:, :, i].copy()\n                    cv2.rectangle(mask_instance, (x, y), (x+w, y+h), 255, -1)\n                    mask[:, :, i] = mask_instance\n                    class_ids[i] = 1\n        return mask.astype(np.bool), class_ids.astype(np.int32)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# training dataset\nanns = pd.read_csv(os.path.join(DATA_DIR, 'rsna-pneumonia-detection-challenge/stage_2_train_labels.csv'))\nanns.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"image_fps, image_annotations = parse_dataset(train_dicom_dir, anns=anns)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"ds = pydicom.read_file(image_fps[0]) # read dicom image from filepath \nimage = ds.pixel_array # get image array","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Original DICOM image size: 1024 x 1024\nORIG_SIZE = 1024","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Split the data into training and validation datasets**"},{"metadata":{"trusted":true},"cell_type":"code","source":"######################################################################\n# Modify this line to use more or fewer images for training/validation. \n# To use all images, do: image_fps_list = list(image_fps)\n#image_fps_list = list(image_fps[:1000]) #xhb 20190525\nimage_fps_list = list(image_fps)\n#####################################################################\n\n# split dataset into training vs. validation dataset \n# split ratio is set to 0.9 vs. 0.1 (train vs. validation, respectively)\n# 0.8 vs 0.2 #xhb 20190525\nsorted(image_fps_list)\nrandom.seed(2018)\nrandom.shuffle(image_fps_list)\n\nvalidation_split = 0.15 #xhb 20190525\n#validation_split = 0.2\nsplit_index = int((1 - validation_split) * len(image_fps_list))\n\nimage_fps_train = image_fps_list[:split_index]\nimage_fps_val = image_fps_list[split_index:]\n\nprint(len(image_fps_train), len(image_fps_val))\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# prepare the training dataset\ndataset_train = DetectorDataset(image_fps_train, image_annotations, ORIG_SIZE, ORIG_SIZE)\ndataset_train.prepare()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# prepare the validation dataset\ndataset_val = DetectorDataset(image_fps_val, image_annotations, ORIG_SIZE, ORIG_SIZE)\ndataset_val.prepare()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Display a random image with bounding boxes**"},{"metadata":{"trusted":true},"cell_type":"code","source":"# Load and display a random sample and their bounding boxes\n\nclass_ids = [0]\nwhile class_ids[0] == 0:  ## look for a mask\n    image_id = random.choice(dataset_train.image_ids)\n    image_fp = dataset_train.image_reference(image_id)\n    image = dataset_train.load_image(image_id)\n    mask, class_ids = dataset_train.load_mask(image_id)\n\nprint(image.shape)\n\nplt.figure(figsize=(10, 10))\nplt.subplot(1, 2, 1)\nplt.imshow(image)\nplt.axis('off')\n\nplt.subplot(1, 2, 2)\nmasked = np.zeros(image.shape[:2])\nfor i in range(mask.shape[2]):\n    masked += image[:, :, 0] * mask[:, :, i]\nplt.imshow(masked, cmap='gray')\nplt.axis('off')\n\n\nprint(image_fp)\nprint(class_ids)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Image augmentation (light but constant)\naugmentation = iaa.Sequential([\n    iaa.OneOf([ ## geometric transform\n        iaa.Affine(\n            scale={\"x\": (0.98, 1.02), \"y\": (0.98, 1.04)},\n            translate_percent={\"x\": (-0.02, 0.02), \"y\": (-0.04, 0.04)},\n            rotate=(-2, 2),\n            shear=(-1, 1),\n        ),\n        iaa.PiecewiseAffine(scale=(0.001, 0.025)),\n    ]),\n    iaa.OneOf([ ## brightness or contrast\n        iaa.Multiply((0.9, 1.1)),\n        iaa.ContrastNormalization((0.9, 1.1)),\n    ]),\n    iaa.OneOf([ ## blur or sharpen\n        iaa.GaussianBlur(sigma=(0.0, 0.1)),\n        iaa.Sharpen(alpha=(0.0, 0.1)),\n    ]),\n])\n\n# test on the same image as above\nimggrid = augmentation.draw_grid(image[:, :, 0], cols=5, rows=2)\nplt.figure(figsize=(30, 12))\n_ = plt.imshow(imggrid[:, :, 0], cmap='gray')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = modellib.MaskRCNN(mode='training', config=config, model_dir=ROOT_DIR)\nCOCO_WEIGHTS_PATH = \"/kaggle/input/mask-rcnn-coco/mask_rcnn_coco.h5\"\n# Exclude the last layers because they require a matching\n# number of classes\nmodel.load_weights(COCO_WEIGHTS_PATH, by_name=True, exclude=[\n    \"mrcnn_class_logits\", \"mrcnn_bbox_fc\",\n    \"mrcnn_bbox\", \"mrcnn_mask\"])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"LEARNING_RATE = 0.005\nNUM_EPOCHS = 30\n\n# Train Mask-RCNN Model \nimport warnings \nwarnings.filterwarnings(\"ignore\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"%%time\ncheckpoint_path = os.path.join(ROOT_DIR, \"mask_rcnn_{}_*epoch*.h5\".format(config.NAME.lower()))\ncheckpoint_path = checkpoint_path.replace(\"*epoch*\", \"{epoch:04d}\")\ncallbacks = [keras.callbacks.ModelCheckpoint(checkpoint_path,verbose=1, save_weights_only=True,period=1)]\n# callbacks = [keras.callbacks.TensorBoard(log_dir='logs',histogram_freq=0, write_graph=True, write_images=False)]\n\nmodel.train(dataset_train, dataset_val, \n            learning_rate=LEARNING_RATE, \n            epochs=NUM_EPOCHS, \n            custom_callbacks=callbacks,\n            layers='all',\n            augmentation=augmentation)\n\nhistory = model.keras_model.history.history","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"epochs = range(1,len(next(iter(history.values())))+1)\npd.DataFrame(history, index=epochs)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.figure(figsize=(10,10))\nplt.subplot(111)\nplt.plot(epochs, history[\"loss\"], label=\"Train loss\")\nplt.plot(epochs, history[\"val_loss\"], label=\"Valid loss\")\nplt.legend()\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.figure(figsize=(10,10))\nplt.subplot(111)\nplt.plot(epochs, history[\"mrcnn_class_loss\"], label=\"Train class ce\")\nplt.plot(epochs, history[\"val_mrcnn_class_loss\"], label=\"Valid class ce\")\nplt.legend()\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.figure(figsize=(10,10))\nplt.subplot(111)\nplt.plot(epochs, history[\"mrcnn_bbox_loss\"], label=\"Train box loss\")\nplt.plot(epochs, history[\"val_mrcnn_bbox_loss\"], label=\"Valid box loss\")\nplt.legend()\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"best_epoch = np.argmin(history[\"val_loss\"])\nprint(\"Best Epoch:\", best_epoch + 1, history[\"val_loss\"][best_epoch])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# select trained model \ndir_names = next(os.walk(model.model_dir))[1]\nkey = config.NAME.lower()\ndir_names = filter(lambda f: f.startswith(key), dir_names)\ndir_names = sorted(dir_names)\n\nif not dir_names:\n    import errno\n    raise FileNotFoundError(\n        errno.ENOENT,\n        \"Could not find model directory under {}\".format(self.model_dir))\n    \nfps = []\n# Pick last directory\nfor d in dir_names: \n    dir_name = os.path.join(model.model_dir, d)\n    # Find the last checkpoint\n    checkpoints = next(os.walk(dir_name))[2]\n    checkpoints = filter(lambda f: f.startswith(\"mask_rcnn\"), checkpoints)\n    checkpoints = sorted(checkpoints)\n    if not checkpoints:\n        print('No weight files in {}'.format(dir_name))\n    else:\n        checkpoint = os.path.join(dir_name, checkpoints[best_epoch])\n        fps.append(checkpoint)\n\nmodel_path = sorted(fps)[-1]\nprint('Found model {}'.format(model_path))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"class InferenceConfig(DetectorConfig):\n    GPU_COUNT = 1\n    IMAGES_PER_GPU = 1\n\ninference_config = InferenceConfig()\n\n# Recreate the model in inference mode\nmodel = modellib.MaskRCNN(mode='inference', \n                          config=inference_config,\n                          model_dir=ROOT_DIR)\n\n# Load trained weights (fill in path to trained weights here)\nassert model_path != \"\", \"Provide path to trained weights\"\nprint(\"Loading weights from \", model_path)\nmodel.load_weights(model_path, by_name=True)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# set color for class\ndef get_colors_for_class_ids(class_ids):\n    colors = []\n    for class_id in class_ids:\n        if class_id == 1:\n            colors.append((.941, .204, .204))\n    return colors","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**How does the predicted box compared to the expected value? Let's use the validation dataset to check.**"},{"metadata":{"trusted":true},"cell_type":"code","source":"# Show few example of ground truth vs. predictions on the validation dataset \ndataset = dataset_val\nfig = plt.figure(figsize=(10, 30))\n\nfor i in range(6):\n\n    image_id = random.choice(dataset.image_ids)\n    \n    original_image, image_meta, gt_class_id, gt_bbox, gt_mask =\\\n        modellib.load_image_gt(dataset_val, inference_config, \n                               image_id, use_mini_mask=False)\n    \n    print(original_image.shape)\n    plt.subplot(6, 2, 2*i + 1)\n    visualize.display_instances(original_image, gt_bbox, gt_mask, gt_class_id, \n                                dataset.class_names,\n                                colors=get_colors_for_class_ids(gt_class_id), ax=fig.axes[-1])\n    \n    plt.subplot(6, 2, 2*i + 2)\n    results = model.detect([original_image]) #, verbose=1)\n    r = results[0]\n    visualize.display_instances(original_image, r['rois'], r['masks'], r['class_ids'], \n                                dataset.class_names, r['scores'], \n                                colors=get_colors_for_class_ids(r['class_ids']), ax=fig.axes[-1])","execution_count":null,"outputs":[]}],"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":4,"nbformat_minor":4}