import cv2 import numpy as np import imageio as io from matplotlib import cm import time import PIL import pycocotools.mask as mask_utils from . import palette CLASSES = ['adult', 'baby', 'bag', 'ball', 'ballon', 'basket', 'bat', 'bed', 'bench', 'beverage', 'bike', 'bird', 'blanket', 'board', 'book', 'bottle', 'bowl', 'box', 'bread', 'brush', 'bucket', 'cabinet', 'cake', 'camera', 'can', 'candle', 'car', 'card', 'carpet', 'cart', 'cat', 'cellphone', 'chair', 'child', 'chopstick', 'cloth', 'computer', 'condiment', 'cookie', 'countertop', 'cover', 'cup', 'curtain', 'dog', 'door', 'drawer', 'dustbin', 'egg', 'fan', 'faucet', 'fence', 'flower', 'fork', 'fridge', 'fruit', 'gift', 'glass', 'glasses', 'glove', 'grain', 'guitar', 'hat', 'helmet', 'horse', 'iron', 'knife', 'light', 'lighter', 'mat', 'meat', 'microphone', 'microwave', 'mop', 'net', 'noodle', 'others', 'oven', 'pan', 'paper', 'piano', 'pillow', 'pizza', 'plant', 'plate', 'pot', 'powder', 'rack', 'racket', 'rag', 'ring', 'scissor', 'shelf', 'shoe', 'simmering', 'sink', 'slide', 'sofa', 'spatula', 'sponge', 'spoon', 'spray', 'stairs', 'stand', 'stove', 'switch', 'table', 'teapot', 'towel', 'toy', 'tray', 'tv', 'vaccum', 'vegetable', 'washer', 'window', 'ceiling', 'floor', 'grass', 'ground', 'rock', 'sand', 'sky', 'snow', 'tree', 'wall', 'water'] def dump_predictions(pred, lbl_set, img, prefix): ''' Save: 1. Predicted labels for evaluation 2. Label heatmaps for visualization ''' lbl_set = palette.tensor.astype(np.uint8) sz = img.shape[:-1] # Upsample predicted soft label maps # pred_dist = pred.copy() pred_dist = cv2.resize(pred, sz[::-1])[:] # Argmax to get the hard label for index pred_lbl = np.argmax(pred_dist, axis=-1) pred_lbl = np.array(lbl_set, dtype=np.int32)[pred_lbl] mask = np.float32(pred_lbl.sum(2) > 0)[:,:,None] alpha = 0.5 img_with_label = mask * (np.float32(img) * alpha + \ np.float32(pred_lbl) * (1-alpha)) + (1-mask) * np.float32(img) # Visualize label distribution for object 1 (debugging/analysis) pred_soft = pred_dist[..., 1] pred_soft = cv2.resize(pred_soft, (img.shape[1], img.shape[0]), interpolation=cv2.INTER_NEAREST) pred_soft = cm.jet(pred_soft)[..., :3] * 255.0 img_with_heatmap1 = np.float32(img) * 0.5 + np.float32(pred_soft) * 0.5 # Save blend image for visualization io.imwrite('%s_blend.jpg' % prefix, np.uint8(img_with_label)) if prefix[-4] != '.': # Super HACK-y imname2 = prefix + '_mask.png' else: imname2 = prefix.replace('jpg','png') # Save predicted labels for evaluation io.imwrite(imname2, np.uint8(pred_lbl)) return img_with_label, pred_lbl, img_with_heatmap1 def make_gif(video, outname='/tmp/test.gif', sz=256): if hasattr(video, 'shape'): video = video.cpu() if video.shape[0] == 3: video = video.transpose(0, 1) video = video.numpy().transpose(0, 2, 3, 1) video = (video*255).astype(np.uint8) video = [cv2.resize(vv, (sz, sz)) for vv in video] if outname is None: return np.stack(video) io.mimsave(outname, video, duration = 0.2) def get_color(idx): idx = idx * 17 color = ((37 * idx) % 255, (17 * idx) % 255, (29 * idx) % 255) return color def plot_tracking(image, obs, obj_ids, scores=None, frame_id=0, fps=0.): im = np.ascontiguousarray(np.copy(image)) im_h, im_w = im.shape[:2] text_scale = max(1, image.shape[1] / 1600.) text_thickness = 1 if text_scale > 1.1 else 1 line_thickness = max(1, int(image.shape[1] / 150.)) alpha = 0.4 for i, ob in enumerate(obs): obj_id = int(obj_ids[i]) id_text = '{}_{}'.format(CLASSES[ob['class_id']], str(obj_id)) _line_thickness = 1 if obj_id <= 0 else line_thickness color = get_color(obj_id) if len(ob) == 4: x1, y1, w, h = ob intbox = tuple(map(int, (x1, y1, x1 + w, y1 + h))) cv2.rectangle(im, intbox[0:2], intbox[2:4], color=color, thickness=line_thickness) cv2.putText(im, id_text, (intbox[0], intbox[1] + 30), cv2.FONT_HERSHEY_PLAIN, text_scale, (0, 0, 255), thickness=text_thickness) elif isinstance(ob, dict): mask = mask_utils.decode(ob) mask = cv2.resize(mask, (im_w, im_h), interpolation=cv2.INTER_LINEAR) mask = (mask > 0.5).astype(np.uint8)[:,:,None] mask_color = mask * color im = (1 - mask) * im + mask * (alpha*im + (1-alpha)*mask_color) # put the id tag at the mask centroid M = cv2.moments(mask) if M['m00'] != 0: cx = int(M['m10'] / M['m00']) cy = int(M['m01'] / M['m00']) else: cx, cy = mask.shape[1]//2, mask.shape[0]//2 (text_width, text_height), _ = cv2.getTextSize(id_text, cv2.FONT_HERSHEY_PLAIN, text_scale, text_thickness) tag = np.zeros((text_height + 10, text_width + 10, 3), dtype=np.uint8) cv2.putText(tag, id_text, (5, text_height + 5), cv2.FONT_HERSHEY_PLAIN, text_scale, (255,255,255), thickness=text_thickness) # put the tag on image start_x = cx - tag.shape[1] // 2 start_y = cy - tag.shape[0] // 2 tag_height, tag_width = tag.shape[:2] start_x = max(start_x, 0) start_y = max(start_y, 0) end_x = start_x + tag_width end_y = start_y + tag_height if end_x > im.shape[1]: end_x = im.shape[1] tag_width = im.shape[1] - start_x if end_y > im.shape[0]: end_y = im.shape[0] tag_height = im.shape[0] - start_y im[start_y:end_y, start_x:end_x] = tag[:tag_height, :tag_width] else: raise ValueError('Observation format not supported.') return im def vis_pose(oriImg, points): pa = np.zeros(15) pa[2] = 0 pa[12] = 8 pa[8] = 4 pa[4] = 0 pa[11] = 7 pa[7] = 3 pa[3] = 0 pa[0] = 1 pa[14] = 10 pa[10] = 6 pa[6] = 1 pa[13] = 9 pa[9] = 5 pa[5] = 1 colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0], [0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255], [170,0,255],[255,0,255]] canvas = oriImg stickwidth = 4 x = points[0, :] y = points[1, :] for n in range(len(x)): pair_id = int(pa[n]) x1 = int(x[pair_id]) y1 = int(y[pair_id]) x2 = int(x[n]) y2 = int(y[n]) if x1 >= 0 and y1 >= 0 and x2 >= 0 and y2 >= 0: cv2.line(canvas, (x1, y1), (x2, y2), colors[n], 8) return canvas def draw_skeleton(aa, kp, color, show_skeleton_labels=False, dataset= "PoseTrack"): if dataset == "COCO": skeleton = [[16, 14], [14, 12], [17, 15], [15, 13], [12, 13], [6, 12], [7, 13], [6, 7], [6, 8], [7, 9], [8, 10], [9, 11], [2, 3], [1, 2], [1, 3], [2, 4], [3, 5], [4, 6], [5, 7]] kp_names = ['nose', 'l_eye', 'r_eye', 'l_ear', 'r_ear', 'l_shoulder', 'r_shoulder', 'l_elbow', 'r_elbow', 'l_wrist', 'r_wrist', 'l_hip', 'r_hip', 'l_knee', 'r_knee', 'l_ankle', 'r_ankle'] elif dataset == "PoseTrack": skeleton = [[10, 11], [11, 12], [9,8], [8,7], [10, 13], [9, 13], [13, 15], [10,4], [4,5], [5,6], [9,3], [3,2], [2,1]] kp_names = ['right_ankle', 'right_knee', 'right_pelvis', 'left_pelvis', 'left_knee', 'left_ankle', 'right_wrist', 'right_elbow', 'right_shoulder', 'left_shoulder', 'left_elbow', 'left_wrist', 'upper_neck', 'nose', 'head'] for i, j in skeleton: if kp[i-1][0] >= 0 and kp[i-1][1] >= 0 and kp[j-1][0] >= 0 and kp[j-1][1] >= 0 and \ (len(kp[i-1]) <= 2 or (len(kp[i-1]) > 2 and kp[i-1][2] > 0.1 and kp[j-1][2] > 0.1)): st = (int(kp[i-1][0]), int(kp[i-1][1])) ed = (int(kp[j-1][0]), int(kp[j-1][1])) cv2.line(aa, st, ed, color, max(1, int(aa.shape[1]/150.))) for j in range(len(kp)): if kp[j][0] >= 0 and kp[j][1] >= 0: pt = (int(kp[j][0]), int(kp[j][1])) if len(kp[j]) <= 2 or (len(kp[j]) > 2 and kp[j][2] > 1.1): cv2.circle(aa, pt, 2, tuple((0,0,255)), 2) elif len(kp[j]) <= 2 or (len(kp[j]) > 2 and kp[j][2] > 0.1): cv2.circle(aa, pt, 2, tuple((255,0,0)), 2) if show_skeleton_labels and (len(kp[j]) <= 2 or (len(kp[j]) > 2 and kp[j][2] > 0.1)): cv2.putText(aa, kp_names[j], tuple(kp[j][:2]), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 0))