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import cv2 |
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import concern.webcv2 as webcv2 |
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import numpy as np |
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import torch |
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from concern.config import Configurable, State |
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from data.processes.make_icdar_data import MakeICDARData |
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class SegDetectorVisualizer(Configurable): |
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vis_num = State(default=4) |
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eager_show = State(default=False) |
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def __init__(self, **kwargs): |
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cmd = kwargs['cmd'] |
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if 'eager_show' in cmd: |
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self.eager_show = cmd['eager_show'] |
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def visualize(self, batch, output_pair, pred): |
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boxes, _ = output_pair |
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result_dict = {} |
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for i in range(batch['image'].size(0)): |
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result_dict.update( |
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self.single_visualize(batch, i, boxes[i], pred)) |
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if self.eager_show: |
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webcv2.waitKey() |
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return {} |
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return result_dict |
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def _visualize_heatmap(self, heatmap, canvas=None): |
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if isinstance(heatmap, torch.Tensor): |
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heatmap = heatmap.cpu().numpy() |
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heatmap = (heatmap[0] * 255).astype(np.uint8) |
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if canvas is None: |
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pred_image = heatmap |
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else: |
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pred_image = (heatmap.reshape( |
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*heatmap.shape[:2], 1).astype(np.float32) / 255 + 1) / 2 * canvas |
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pred_image = pred_image.astype(np.uint8) |
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return pred_image |
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def single_visualize(self, batch, index, boxes, pred): |
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image = batch['image'][index] |
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polygons = batch['polygons'][index] |
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if isinstance(polygons, torch.Tensor): |
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polygons = polygons.cpu().data.numpy() |
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ignore_tags = batch['ignore_tags'][index] |
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original_shape = batch['shape'][index] |
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filename = batch['filename'][index] |
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std = np.array([0.229, 0.224, 0.225]).reshape(3, 1, 1) |
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mean = np.array([0.485, 0.456, 0.406]).reshape(3, 1, 1) |
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image = (image.cpu().numpy() * std + mean).transpose(1, 2, 0) * 255 |
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pred_canvas = image.copy().astype(np.uint8) |
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pred_canvas = cv2.resize(pred_canvas, (original_shape[1], original_shape[0])) |
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if isinstance(pred, dict) and 'thresh' in pred: |
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thresh = self._visualize_heatmap(pred['thresh'][index]) |
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if isinstance(pred, dict) and 'thresh_binary' in pred: |
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thresh_binary = self._visualize_heatmap(pred['thresh_binary'][index]) |
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MakeICDARData.polylines(self, thresh_binary, polygons, ignore_tags) |
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for box in boxes: |
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box = np.array(box).astype(np.int32).reshape(-1, 2) |
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cv2.polylines(pred_canvas, [box], True, (0, 255, 0), 2) |
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if isinstance(pred, dict) and 'thresh_binary' in pred: |
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cv2.polylines(thresh_binary, [box], True, (0, 255, 0), 1) |
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if self.eager_show: |
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webcv2.imshow(filename + ' output', cv2.resize(pred_canvas, (1024, 1024))) |
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if isinstance(pred, dict) and 'thresh' in pred: |
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webcv2.imshow(filename + ' thresh', cv2.resize(thresh, (1024, 1024))) |
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webcv2.imshow(filename + ' pred', cv2.resize(pred_canvas, (1024, 1024))) |
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if isinstance(pred, dict) and 'thresh_binary' in pred: |
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webcv2.imshow(filename + ' thresh_binary', cv2.resize(thresh_binary, (1024, 1024))) |
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return {} |
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else: |
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if isinstance(pred, dict) and 'thresh' in pred: |
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return { |
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filename + '_output': pred_canvas, |
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filename + '_thresh': thresh, |
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} |
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else: |
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return { |
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filename + '_output': pred_canvas, |
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} |
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def demo_visualize(self, image_path, output): |
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boxes, _ = output |
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boxes = boxes[0] |
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original_image = cv2.imread(image_path, cv2.IMREAD_COLOR) |
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original_shape = original_image.shape |
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pred_canvas = original_image.copy().astype(np.uint8) |
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pred_canvas = cv2.resize(pred_canvas, (original_shape[1], original_shape[0])) |
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for box in boxes: |
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box = np.array(box).astype(np.int32).reshape(-1, 2) |
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cv2.polylines(pred_canvas, [box], True, (0, 255, 0), 2) |
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return pred_canvas |
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