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