from typing import Tuple import os.path as osp import PIL import mmcv import numpy as np from detectron2.utils.colormap import colormap from detectron2.utils.visualizer import VisImage, Visualizer from mmdet.datasets.coco_panoptic import INSTANCE_OFFSET 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