{"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":"markdown","source":"# **[HuBMAP 2023] Detectron2-Inference**","metadata":{"papermill":{"duration":0.005873,"end_time":"2023-07-17T08:20:31.863335","exception":false,"start_time":"2023-07-17T08:20:31.857462","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"\n* #### [HuBMAP 2023]K-fold CV COCO Dataset Generator [here](https://www.kaggle.com/code/ammarnassanalhajali/hubmap-2023-k-fold-cv-coco-dataset-generator)\n* #### [HuBMAP 2023] Detectron2-Training [here](https://www.kaggle.com/code/ammarnassanalhajali/hubmap-2023-detectron2-training)","metadata":{"papermill":{"duration":0.005237,"end_time":"2023-07-17T08:20:31.874014","exception":false,"start_time":"2023-07-17T08:20:31.868777","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"### Install Detectron2 offline","metadata":{"papermill":{"duration":0.005074,"end_time":"2023-07-17T08:20:31.886088","exception":false,"start_time":"2023-07-17T08:20:31.881014","status":"completed"},"tags":[]}},{"cell_type":"code","source":"!pip install /kaggle/input/detectron2-wheel/detectron2/detectron2-0.6-cp310-cp310-linux_x86_64.whl --no-index --find-links=/kaggle/input/detectron2-wheel/detectron2","metadata":{"_kg_hide-output":true,"papermill":{"duration":15.651743,"end_time":"2023-07-17T08:20:47.543148","exception":false,"start_time":"2023-07-17T08:20:31.891405","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-07-22T19:47:01.843179Z","iopub.execute_input":"2023-07-22T19:47:01.843660Z","iopub.status.idle":"2023-07-22T19:47:18.044366Z","shell.execute_reply.started":"2023-07-22T19:47:01.843629Z","shell.execute_reply":"2023-07-22T19:47:18.042861Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### importing libraries","metadata":{"papermill":{"duration":0.006663,"end_time":"2023-07-17T08:20:47.557043","exception":false,"start_time":"2023-07-17T08:20:47.550380","status":"completed"},"tags":[]}},{"cell_type":"code","source":"import detectron2\nimport torch\nfrom detectron2 import model_zoo\nfrom detectron2.engine import DefaultPredictor\nfrom detectron2.config import get_cfg\nfrom PIL import Image\nimport cv2\nimport base64\nimport numpy as np\nfrom pycocotools import _mask as coco_mask\nimport typing as t\nimport zlib\n\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom fastcore.all import *\nos.environ['CUDA_VISIBLE_DEVICES'] = '0' \nif torch.cuda.is_available():\n    DEVICE = torch.device('cuda')\n    print('GPU is available')\nelse:\n    DEVICE = torch.device('cpu')\n    print('CPU is used')\nprint('detectron ver:', detectron2.__version__)\n\nfrom skimage import io\nimport matplotlib.patches as mpatches","metadata":{"papermill":{"duration":5.391932,"end_time":"2023-07-17T08:20:52.955621","exception":false,"start_time":"2023-07-17T08:20:47.563689","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-07-22T19:47:18.047844Z","iopub.execute_input":"2023-07-22T19:47:18.048467Z","iopub.status.idle":"2023-07-22T19:47:22.245260Z","shell.execute_reply.started":"2023-07-22T19:47:18.048427Z","shell.execute_reply":"2023-07-22T19:47:22.244171Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Functions","metadata":{"papermill":{"duration":0.007811,"end_time":"2023-07-17T08:20:52.971365","exception":false,"start_time":"2023-07-17T08:20:52.963554","status":"completed"},"tags":[]}},{"cell_type":"code","source":"# From https://www.kaggle.com/stainsby/fast-tested-rle\ndef rle_decode(mask_rle, shape=(520, 704)):\n    '''\n    mask_rle: run-length as string formated (start length)\n    shape: (height,width) of array to return \n    Returns numpy array, 1 - mask, 0 - background\n\n    '''\n    s = mask_rle.split()\n    starts, lengths = [np.asarray(x, dtype=int) for x in (s[0:][::2], s[1:][::2])]\n    starts -= 1\n    ends = starts + lengths\n    img = np.zeros(shape[0]*shape[1], dtype=np.uint8)\n    for lo, hi in zip(starts, ends):\n        img[lo:hi] = 1\n    return img.reshape(shape)  # Needed to align to RLE direction\n\ndef rle_encode(img):\n    '''\n    img: numpy array, 1 - mask, 0 - background\n    Returns run length as string formated\n    '''\n    pixels = img.flatten()\n    pixels = np.concatenate([[0], pixels, [0]])\n    runs = np.where(pixels[1:] != pixels[:-1])[0] + 1\n    runs[1::2] -= runs[::2]\n    return ' '.join(str(x) for x in runs)\n\n\ndef encode_binary_mask(mask: np.ndarray) -> t.Text:\n    # check input mask --\n    if mask.dtype != bool:\n        raise ValueError(\n            \"encode_binary_mask expects a binary mask, received dtype == %s\" %\n            mask.dtype)\n\n    mask = np.squeeze(mask)\n    if len(mask.shape) != 2:\n        raise ValueError(\n            \"encode_binary_mask expects a 2d mask, received shape == %s\" %\n            mask.shape)\n\n    # convert input mask to expected COCO API input --\n    mask_to_encode = mask.reshape(mask.shape[0], mask.shape[1], 1)\n    mask_to_encode = mask_to_encode.astype(np.uint8)\n    mask_to_encode = np.asfortranarray(mask_to_encode)\n\n    # RLE encode mask --\n    encoded_mask = coco_mask.encode(mask_to_encode)[0][\"counts\"]\n\n    # compress and base64 encoding --\n    binary_str = zlib.compress(encoded_mask, zlib.Z_BEST_COMPRESSION)\n    base64_str = base64.b64encode(binary_str)\n    return base64_str\n\ndef get_predicts(fn, predictor):\n    im = cv2.imread(str(fn))\n    output = predictor(im)\n    take = torch.zeros(len(output['instances'].scores), dtype=torch.bool).to(device=DEVICE)\n    # filter  based on thresholds for each class\n    for c in thresholds_classes:\n        mask = (output['instances'].pred_classes == c) & (output['instances'].scores > thresholds_classes[c])\n        take |= mask\n    pred_masks = output['instances'].pred_masks[take].cpu().numpy()\n    pred_classes = output['instances'].pred_classes[take].cpu().numpy()\n    pred_scores = output['instances'].scores[take].cpu().numpy()\n    \n    # filter masks based on minimum size thresholds for each class\n    keep = []\n    for i, mask in enumerate(pred_masks):\n        # get class of mask\n        mask_class = pred_classes[i]\n\n        # calculate area of mask\n        area = mask.sum().item()\n\n        # check if area exceeds threshold for mask class\n        if area >= min_size_thresholds.get(mask_class, 0):\n            keep.append(i)\n    # keep only masks with area greater than or equal to threshold for mask class\n    pred_masks = pred_masks[keep]\n    pred_classes = pred_classes[keep]\n    pred_scores = pred_scores[keep]\n    return pred_masks,pred_classes,pred_scores","metadata":{"papermill":{"duration":0.034261,"end_time":"2023-07-17T08:20:53.013300","exception":false,"start_time":"2023-07-17T08:20:52.979039","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-07-22T19:47:22.246777Z","iopub.execute_input":"2023-07-22T19:47:22.247570Z","iopub.status.idle":"2023-07-22T19:47:22.272328Z","shell.execute_reply.started":"2023-07-22T19:47:22.247535Z","shell.execute_reply":"2023-07-22T19:47:22.271437Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Load Model","metadata":{"papermill":{"duration":0.00687,"end_time":"2023-07-17T08:20:53.027459","exception":false,"start_time":"2023-07-17T08:20:53.020589","status":"completed"},"tags":[]}},{"cell_type":"code","source":"cfg = get_cfg()\n#config_name = \"COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml\" \nconfig_name = \"COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml\" \n\ncfg.merge_from_file(model_zoo.get_config_file(config_name))\ncfg.INPUT.MASK_FORMAT='bitmask'\ncfg.MODEL.WEIGHTS = \"/kaggle/input/hubmap-2023-detectron2-colab-models/model_best_V3.pth\"\ncfg.MODEL.ROI_HEADS.NUM_CLASSES =2 \n#cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.40\ncfg.TEST.DETECTIONS_PER_IMAGE = 1000\npredictor = DefaultPredictor(cfg)\n\nidx_classes = {0: 'blood_vessel'}\nthresholds_classes = {0: 0.05} # class:thre\nmin_size_thresholds = {0:1}  # adjust these values as needed","metadata":{"papermill":{"duration":8.588028,"end_time":"2023-07-17T08:21:01.623021","exception":false,"start_time":"2023-07-17T08:20:53.034993","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-07-22T19:47:22.276781Z","iopub.execute_input":"2023-07-22T19:47:22.277111Z","iopub.status.idle":"2023-07-22T19:47:28.476242Z","shell.execute_reply.started":"2023-07-22T19:47:22.277086Z","shell.execute_reply":"2023-07-22T19:47:28.475316Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Loading test Dataset","metadata":{"papermill":{"duration":0.007448,"end_time":"2023-07-17T08:21:01.638039","exception":false,"start_time":"2023-07-17T08:21:01.630591","status":"completed"},"tags":[]}},{"cell_type":"code","source":"Dir_testdata=Path('/kaggle/input/hubmap-hacking-the-human-vasculature/test')\ntest_image_names = (Dir_testdata).ls()\n\nids = []\nheights = []\nwidths = []\nprediction_strings = []\n","metadata":{"papermill":{"duration":0.025252,"end_time":"2023-07-17T08:21:01.670658","exception":false,"start_time":"2023-07-17T08:21:01.645406","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-07-22T19:47:28.477655Z","iopub.execute_input":"2023-07-22T19:47:28.478000Z","iopub.status.idle":"2023-07-22T19:47:28.487286Z","shell.execute_reply.started":"2023-07-22T19:47:28.477964Z","shell.execute_reply":"2023-07-22T19:47:28.486370Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Predicting","metadata":{"papermill":{"duration":0.007327,"end_time":"2023-07-17T08:21:01.685407","exception":false,"start_time":"2023-07-17T08:21:01.678080","status":"completed"},"tags":[]}},{"cell_type":"code","source":"from skimage.morphology import binary_dilation\n\nfor img in test_image_names:\n    pred_masks,pred_classes,pred_scores = get_predicts(img, predictor)\n    h, w, _ = cv2.imread(str(img)).shape\n    pred_string = \"\"\n    for i, mask in enumerate(pred_masks):\n        if int(pred_classes[i]) != 0: continue\n        #dilation\n        mask = mask.astype(np.uint8)\n        kernel = np.ones(shape=(3, 3), dtype=np.uint8)\n        mask = cv2.dilate(mask, kernel, 4)\n        mask = mask.astype(bool)  \n        #end dilation\n        encoded = encode_binary_mask(mask)\n        if i == 0:\n            pred_string += f\"{int(pred_classes[i])} {pred_scores[i]} {encoded.decode('utf-8')}\"\n        else:\n            pred_string += f\" {int(pred_classes[i])} {pred_scores[i]} {encoded.decode('utf-8')}\"\n                \n    ids.append(str(img).split('.')[0].split('/')[-1])\n    heights.append(h)\n    widths.append(w)\n    prediction_strings.append(pred_string)","metadata":{"papermill":{"duration":8.051594,"end_time":"2023-07-17T08:21:09.744425","exception":false,"start_time":"2023-07-17T08:21:01.692831","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-07-22T19:47:28.490469Z","iopub.execute_input":"2023-07-22T19:47:28.491668Z","iopub.status.idle":"2023-07-22T19:47:34.625426Z","shell.execute_reply.started":"2023-07-22T19:47:28.491633Z","shell.execute_reply":"2023-07-22T19:47:34.623207Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Visualization","metadata":{"papermill":{"duration":0.00749,"end_time":"2023-07-17T08:21:09.759993","exception":false,"start_time":"2023-07-17T08:21:09.752503","status":"completed"},"tags":[]}},{"cell_type":"code","source":"colors = [ 'Set1'] \nlegend = {0: 'blood_vessel'} \nfig, axs = plt.subplots(1, 2, figsize=(10, 5))\nI = io.imread(str(test_image_names[-1]))\naxs[0].imshow(I)\naxs[0].set_title('Image')\naxs[1].imshow(I)\nfor mk,cl in zip(pred_masks,pred_classes):\n    color = colors[cl]\n    #dilation\n    mask = mask.astype(np.uint8)\n    kernel = np.ones(shape=(3, 3), dtype=np.uint8)\n    mask = cv2.dilate(mask, kernel, 4)\n    mask = mask.astype(bool)  \n    #end dilation\n    mask = np.ma.masked_where(mk == 0, mk)\n    axs[1].imshow(mask, cmap=color, alpha=0.8)\n    axs[1].set_title('Predicted Masks')\n    handles = []\n    for cl in legend:\n        color = colors[cl]\n        handles.append(mpatches.Patch(color=plt.colormaps.get_cmap(color)(0)))\n    axs[1].legend(handles, legend.values(), bbox_to_anchor=(1.05, 1), loc='upper left')","metadata":{"papermill":{"duration":1.961226,"end_time":"2023-07-17T08:21:11.728205","exception":false,"start_time":"2023-07-17T08:21:09.766979","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-07-22T19:47:34.626826Z","iopub.execute_input":"2023-07-22T19:47:34.627592Z","iopub.status.idle":"2023-07-22T19:47:37.884846Z","shell.execute_reply.started":"2023-07-22T19:47:34.627555Z","shell.execute_reply":"2023-07-22T19:47:37.883997Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submission = pd.DataFrame()\nsubmission['id'] = ids\nsubmission['height'] = heights\nsubmission['width'] = widths\nsubmission['prediction_string'] = prediction_strings\nsubmission = submission.set_index('id')\nsubmission.to_csv(\"submission.csv\")\nsubmission","metadata":{"papermill":{"duration":0.0458,"end_time":"2023-07-17T08:21:11.786148","exception":false,"start_time":"2023-07-17T08:21:11.740348","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-07-22T19:47:37.885857Z","iopub.execute_input":"2023-07-22T19:47:37.886191Z","iopub.status.idle":"2023-07-22T19:47:37.915521Z","shell.execute_reply.started":"2023-07-22T19:47:37.886157Z","shell.execute_reply":"2023-07-22T19:47:37.914819Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![download (1).jpg](attachment:40aafe23-adb1-4f49-927b-e4c744d7daa9.jpg)","metadata":{"papermill":{"duration":0.011639,"end_time":"2023-07-17T08:21:11.809835","exception":false,"start_time":"2023-07-17T08:21:11.798196","status":"completed"},"tags":[]},"attachments":{"40aafe23-adb1-4f49-927b-e4c744d7daa9.jpg":{"image/jpeg":"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"}}}]}