{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport cv2\nimport os\nimport re\n\nfrom PIL import Image\n\nimport albumentations as A\nfrom albumentations.pytorch.transforms import ToTensorV2\n\nimport torch\nimport torchvision\n\nfrom torchvision.models.detection.faster_rcnn import FastRCNNPredictor, FasterRCNN\nfrom torchvision.models.detection.backbone_utils import resnet_fpn_backbone\nfrom torchvision.models.detection.rpn import AnchorGenerator\n\nfrom torch.utils.data import DataLoader, Dataset\nfrom torch.utils.data.sampler import SequentialSampler\n\nfrom matplotlib import pyplot as plt\n\nDIR_INPUT = '/kaggle/input/global-wheat-detection'\nDIR_TRAIN = f'{DIR_INPUT}/train'\nDIR_TEST = f'{DIR_INPUT}/test'\n\nDIR_WEIGHTS = '/kaggle/input/bbbbbb'\n\nWEIGHTS_FILE = f'{DIR_WEIGHTS}/FRCNN_best(2).pth'","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"test_df = pd.read_csv(f'{DIR_INPUT}/sample_submission.csv')\ntest_df.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"class WheatTestDataset(Dataset):\n\n    def __init__(self, dataframe, image_dir, transforms=None):\n        super().__init__()\n\n        self.image_ids = dataframe['image_id'].unique()\n        self.df = dataframe\n        self.image_dir = image_dir\n        self.transforms = transforms\n\n    def __getitem__(self, index: int):\n\n        image_id = self.image_ids[index]\n        records = self.df[self.df['image_id'] == image_id]\n\n        image = cv2.imread(f'{self.image_dir}/{image_id}.jpg', cv2.IMREAD_COLOR)\n        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB).astype(np.float32)\n        image /= 255.0\n\n        if self.transforms:\n            sample = {\n                'image': image,\n            }\n            sample = self.transforms(**sample)\n            image = sample['image']\n\n        return image, image_id\n\n    def __len__(self) -> int:\n        return self.image_ids.shape[0]\n\n# Albumentations\ndef get_test_transform():\n    return A.Compose([\n        # A.Resize(512, 512),\n        ToTensorV2(p=1.0)\n    ])\n\n\n\ndef get_model():\n    \"\"\"\n    https://stackoverflow.com/questions/58362892/resnet-18-as-backbone-in-faster-r-cnn\n    \"\"\"\n    backbone = resnet_fpn_backbone('resnet101', pretrained=False)\n    model = FasterRCNN(backbone, num_classes=2)\n    return model\n\n\ndevice = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\nmodel = get_model()\nmodel.load_state_dict(torch.load(WEIGHTS_FILE))\nmodel.eval()\n\nmodel.to(device)\n\n\n\ndef collate_fn(batch):\n    return tuple(zip(*batch))\n\ntest_dataset = WheatTestDataset(test_df, DIR_TEST, get_test_transform())\n\ntest_data_loader = DataLoader(\n    test_dataset,\n    batch_size=4,\n    shuffle=False,\n    num_workers=4,\n    drop_last=False,\n    collate_fn=collate_fn\n)\n\ndef format_prediction_string(boxes, scores):\n    pred_strings = []\n    for j in zip(scores, boxes):\n        pred_strings.append(\"{0:.4f} {1} {2} {3} {4}\".format(j[0], j[1][0], j[1][1], j[1][2], j[1][3]))\n\n    return \" \".join(pred_strings)\n\ndetection_threshold = 0.5\nresults = []\n\nfor images, image_ids in test_data_loader:\n\n    images = list(image.to(device) for image in images)\n    outputs = model(images)\n\n    for i, image in enumerate(images):\n\n        boxes = outputs[i]['boxes'].data.cpu().numpy()\n        scores = outputs[i]['scores'].data.cpu().numpy()\n        \n        boxes = boxes[scores >= detection_threshold].astype(np.int32)\n        scores = scores[scores >= detection_threshold]\n        image_id = image_ids[i]\n        \n        boxes[:, 2] = boxes[:, 2] - boxes[:, 0]\n        boxes[:, 3] = boxes[:, 3] - boxes[:, 1]\n        \n        result = {\n            'image_id': image_id,\n            'PredictionString': format_prediction_string(boxes, scores)\n        }\n\n        \n        results.append(result)\n\nresults[0:2]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\n\ntest_df = pd.DataFrame(results, columns=['image_id', 'PredictionString'])\n\ntest_df.to_csv('submission.csv', index=False)\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\"\"\"for images, image_ids in test_data_loader:\n\n    images = list(image.to(device) for image in images)\n    outputs = model(images)\n\n    for i, image in enumerate(images):\n\n        sample = images[i].permute(1,2,0).cpu().numpy()\n        boxes = outputs[i]['boxes'].data.cpu().numpy()\n        scores = outputs[i]['scores'].data.cpu().numpy()\n\n        boxes = boxes[scores >= detection_threshold].astype(np.int32)\n\n        fig, ax = plt.subplots(1, 1, figsize=(16, 8))\n\n        for box in boxes:\n            cv2.rectangle(sample,\n                          (box[0], box[1]),\n                          (box[2], box[3]),\n                          (220, 0, 0), 2)\n\n        ax.set_axis_off()\n        ax.imshow(sample)\n\n\"\"\"","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}