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| from typing import Tuple | |
| import PIL | |
| import mmcv | |
| import numpy as np | |
| print('50\% \imported utils') | |
| from detectron2.utils.colormap import colormap | |
| print('60\% \imported utils') | |
| from detectron2.utils.visualizer import VisImage, Visualizer | |
| print('80\% \imported utils') | |
| from mmdet.datasets.coco_panoptic import INSTANCE_OFFSET | |
| print('100\% \imported utils') | |
| CLASSES = [ | |
| 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', | |
| 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', | |
| 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', | |
| 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', | |
| 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', | |
| 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', | |
| 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', | |
| 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', | |
| 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', | |
| 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', | |
| 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', | |
| 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', | |
| 'hair drier', 'toothbrush', 'banner', 'blanket', 'bridge', 'cardboard', | |
| 'counter', 'curtain', 'door-stuff', 'floor-wood', 'flower', 'fruit', | |
| 'gravel', 'house', 'light', 'mirror-stuff', 'net', 'pillow', 'platform', | |
| 'playingfield', 'railroad', 'river', 'road', 'roof', 'sand', 'sea', | |
| 'shelf', 'snow', 'stairs', 'tent', 'towel', 'wall-brick', 'wall-stone', | |
| 'wall-tile', 'wall-wood', 'water-other', 'window-blind', 'window-other', | |
| 'tree-merged', 'fence-merged', 'ceiling-merged', 'sky-other-merged', | |
| 'cabinet-merged', 'table-merged', 'floor-other-merged', 'pavement-merged', | |
| 'mountain-merged', 'grass-merged', 'dirt-merged', 'paper-merged', | |
| 'food-other-merged', 'building-other-merged', 'rock-merged', | |
| 'wall-other-merged', 'rug-merged', 'background' | |
| ] | |
| PREDICATES = [ | |
| 'over', | |
| 'in front of', | |
| 'beside', | |
| 'on', | |
| 'in', | |
| 'attached to', | |
| 'hanging from', | |
| 'on back of', | |
| 'falling off', | |
| 'going down', | |
| 'painted on', | |
| 'walking on', | |
| 'running on', | |
| 'crossing', | |
| 'standing on', | |
| 'lying on', | |
| 'sitting on', | |
| 'flying over', | |
| 'jumping over', | |
| 'jumping from', | |
| 'wearing', | |
| 'holding', | |
| 'carrying', | |
| 'looking at', | |
| 'guiding', | |
| 'kissing', | |
| 'eating', | |
| 'drinking', | |
| 'feeding', | |
| 'biting', | |
| 'catching', | |
| 'picking', | |
| 'playing with', | |
| 'chasing', | |
| 'climbing', | |
| 'cleaning', | |
| 'playing', | |
| 'touching', | |
| 'pushing', | |
| 'pulling', | |
| 'opening', | |
| 'cooking', | |
| 'talking to', | |
| 'throwing', | |
| 'slicing', | |
| 'driving', | |
| 'riding', | |
| 'parked on', | |
| 'driving on', | |
| 'about to hit', | |
| 'kicking', | |
| 'swinging', | |
| 'entering', | |
| 'exiting', | |
| 'enclosing', | |
| 'leaning on', | |
| ] | |
| def get_colormap(num_colors: int): | |
| return (np.resize(colormap(), (num_colors, 3))).tolist() | |
| def draw_text( | |
| viz_img: VisImage = None, | |
| text: str = None, | |
| x: float = None, | |
| y: float = None, | |
| color: Tuple[float, float, float] = [0, 0, 0], | |
| size: float = 10, | |
| padding: float = 5, | |
| box_color: str = 'black', | |
| font: str = None, | |
| ) -> float: | |
| text_obj = viz_img.ax.text( | |
| x, | |
| y, | |
| text, | |
| size=size, | |
| # family="sans-serif", | |
| bbox={ | |
| 'facecolor': box_color, | |
| 'alpha': 0.8, | |
| 'pad': padding, | |
| 'edgecolor': 'none', | |
| }, | |
| verticalalignment='top', | |
| horizontalalignment='left', | |
| color=color, | |
| zorder=10, | |
| rotation=0, | |
| ) | |
| viz_img.get_image() | |
| text_dims = text_obj.get_bbox_patch().get_extents() | |
| return text_dims.width | |
| def show_result(img, | |
| result, | |
| is_one_stage, | |
| num_rel=20, | |
| show=False, | |
| out_dir=None, | |
| out_file=None): | |
| # Load image | |
| img = mmcv.imread(img) | |
| img = img.copy() # (H, W, 3) | |
| img_h, img_w = img.shape[:-1] | |
| # Decrease contrast | |
| img = PIL.Image.fromarray(img) | |
| converter = PIL.ImageEnhance.Color(img) | |
| img = converter.enhance(0.01) | |
| if out_file is not None: | |
| mmcv.imwrite(np.asarray(img), 'bw'+out_file) | |
| # Draw masks | |
| pan_results = result.pan_results | |
| ids = np.unique(pan_results)[::-1] | |
| num_classes = 133 | |
| legal_indices = (ids != num_classes) # for VOID label | |
| ids = ids[legal_indices] | |
| # Get predicted labels | |
| labels = np.array([id % INSTANCE_OFFSET for id in ids], dtype=np.int64) | |
| labels = [CLASSES[l] for l in labels] | |
| #For psgtr | |
| rel_obj_labels = result.labels | |
| rel_obj_labels = [CLASSES[l - 1] for l in rel_obj_labels] | |
| # (N_m, H, W) | |
| segms = pan_results[None] == ids[:, None, None] | |
| # Resize predicted masks | |
| segms = [ | |
| mmcv.image.imresize(m.astype(float), (img_w, img_h)) for m in segms | |
| ] | |
| # One stage segmentation | |
| masks = result.masks | |
| # Choose colors for each instance in coco | |
| colormap_coco = get_colormap(len(masks)) if is_one_stage else get_colormap(len(segms)) | |
| colormap_coco = (np.array(colormap_coco) / 255).tolist() | |
| # Viualize masks | |
| viz = Visualizer(img) | |
| viz.overlay_instances( | |
| labels=rel_obj_labels if is_one_stage else labels, | |
| masks=masks if is_one_stage else segms, | |
| assigned_colors=colormap_coco, | |
| ) | |
| viz_img = viz.get_output().get_image() | |
| if out_file is not None: | |
| mmcv.imwrite(viz_img, out_file) | |
| # Draw relations | |
| # Filter out relations | |
| n_rel_topk = num_rel | |
| # Exclude background class | |
| rel_dists = result.rel_dists[:, 1:] | |
| # rel_dists = result.rel_dists | |
| rel_scores = rel_dists.max(1) | |
| # rel_scores = result.triplet_scores | |
| # Extract relations with top scores | |
| rel_topk_idx = np.argpartition(rel_scores, -n_rel_topk)[-n_rel_topk:] | |
| rel_labels_topk = rel_dists[rel_topk_idx].argmax(1) | |
| rel_pair_idxes_topk = result.rel_pair_idxes[rel_topk_idx] | |
| relations = np.concatenate( | |
| [rel_pair_idxes_topk, rel_labels_topk[..., None]], axis=1) | |
| n_rels = len(relations) | |
| top_padding = 20 | |
| bottom_padding = 20 | |
| left_padding = 20 | |
| text_size = 10 | |
| text_padding = 5 | |
| text_height = text_size + 2 * text_padding | |
| row_padding = 10 | |
| height = (top_padding + bottom_padding + n_rels * | |
| (text_height + row_padding) - row_padding) | |
| width = img_w | |
| curr_x = left_padding | |
| curr_y = top_padding | |
| # # Adjust colormaps | |
| # colormap_coco = [adjust_text_color(c, viz) for c in colormap_coco] | |
| viz_graph = VisImage(np.full((height, width, 3), 255)) | |
| all_rel_vis = [] | |
| for i, r in enumerate(relations): | |
| s_idx, o_idx, rel_id = r | |
| s_label = rel_obj_labels[s_idx] | |
| o_label = rel_obj_labels[o_idx] | |
| rel_label = PREDICATES[rel_id] | |
| viz = Visualizer(img) | |
| viz.overlay_instances( | |
| labels=[s_label, o_label], | |
| masks=[masks[s_idx], masks[o_idx]], | |
| assigned_colors=[colormap_coco[s_idx], colormap_coco[o_idx]], | |
| ) | |
| viz_masked_img = viz.get_output().get_image() | |
| viz_graph = VisImage(np.full((40, width, 3), 255)) | |
| curr_x = 2 | |
| curr_y = 2 | |
| text_size = 25 | |
| text_padding = 20 | |
| font = 36 | |
| text_width = draw_text( | |
| viz_img=viz_graph, | |
| text=s_label, | |
| x=curr_x, | |
| y=curr_y, | |
| color=colormap_coco[s_idx], | |
| size=text_size, | |
| padding=text_padding, | |
| font=font, | |
| ) | |
| curr_x += text_width | |
| # Draw relation text | |
| text_width = draw_text( | |
| viz_img=viz_graph, | |
| text=rel_label, | |
| x=curr_x, | |
| y=curr_y, | |
| size=text_size, | |
| padding=text_padding, | |
| box_color='gainsboro', | |
| font=font, | |
| ) | |
| curr_x += text_width | |
| # Draw object text | |
| text_width = draw_text( | |
| viz_img=viz_graph, | |
| text=o_label, | |
| x=curr_x, | |
| y=curr_y, | |
| color=colormap_coco[o_idx], | |
| size=text_size, | |
| padding=text_padding, | |
| font=font, | |
| ) | |
| output_viz_graph = np.vstack([viz_masked_img, viz_graph.get_image()]) | |
| if show: | |
| all_rel_vis.append(output_viz_graph) | |
| return all_rel_vis |