from __future__ import absolute_import from __future__ import division from __future__ import print_function import cv2 import numpy as np from .ddd_utils import compute_box_3d, project_to_image, draw_box_3d class Debugger(object): def __init__(self, ipynb=False, theme='black', num_classes=-1, dataset=None, down_ratio=4): self.ipynb = ipynb if not self.ipynb: import matplotlib.pyplot as plt self.plt = plt self.imgs = {} self.theme = theme colors = [(color_list[_]).astype(np.uint8) \ for _ in range(len(color_list))] self.colors = np.array(colors, dtype=np.uint8).reshape(len(colors), 1, 1, 3) if self.theme == 'white': self.colors = self.colors.reshape(-1)[::-1].reshape(len(colors), 1, 1, 3) self.colors = np.clip(self.colors, 0., 0.6 * 255).astype(np.uint8) self.dim_scale = 1 if dataset == 'coco_hp': self.names = ['p'] self.num_class = 1 self.num_joints = 17 self.edges = [[0, 1], [0, 2], [1, 3], [2, 4], [3, 5], [4, 6], [5, 6], [5, 7], [7, 9], [6, 8], [8, 10], [5, 11], [6, 12], [11, 12], [11, 13], [13, 15], [12, 14], [14, 16]] self.ec = [(255, 0, 0), (0, 0, 255), (255, 0, 0), (0, 0, 255), (255, 0, 0), (0, 0, 255), (255, 0, 255), (255, 0, 0), (255, 0, 0), (0, 0, 255), (0, 0, 255), (255, 0, 0), (0, 0, 255), (255, 0, 255), (255, 0, 0), (255, 0, 0), (0, 0, 255), (0, 0, 255)] self.colors_hp = [(255, 0, 255), (255, 0, 0), (0, 0, 255), (255, 0, 0), (0, 0, 255), (255, 0, 0), (0, 0, 255), (255, 0, 0), (0, 0, 255), (255, 0, 0), (0, 0, 255), (255, 0, 0), (0, 0, 255), (255, 0, 0), (0, 0, 255), (255, 0, 0), (0, 0, 255)] elif num_classes == 80 or dataset == 'coco': self.names = coco_class_name elif num_classes == 20 or dataset == 'pascal': self.names = pascal_class_name elif num_classes == 1 and dataset == 'table': self.names = table_class_name elif num_classes == 16 or dataset == 'huntie': self.names = huntie_class_name elif dataset == 'vehicle': self.names = vehicle_class_name elif num_classes == 2 or dataset == 'video': self.names = video_class_name elif dataset == 'gta': self.names = gta_class_name self.focal_length = 935.3074360871937 self.W = 1920 self.H = 1080 self.dim_scale = 3 elif dataset == 'viper': self.names = gta_class_name self.focal_length = 1158 self.W = 1920 self.H = 1080 self.dim_scale = 3 elif num_classes == 3 or dataset == 'kitti': self.names = kitti_class_name self.focal_length = 721.5377 self.W = 1242 self.H = 375 # num_classes = len(self.names) self.down_ratio = down_ratio # for bird view self.world_size = 64 self.out_size = 384 def add_img(self, img, img_id='default', revert_color=False): if revert_color: img = 255 - img self.imgs[img_id] = img.copy() def add_mask(self, mask, bg, imgId='default', trans=0.8): self.imgs[imgId] = (mask.reshape( mask.shape[0], mask.shape[1], 1) * 255 * trans + \ bg * (1 - trans)).astype(np.uint8) def show_img(self, pause=False, imgId='default'): cv2.imshow('{}'.format(imgId), self.imgs[imgId]) if pause: cv2.waitKey() def add_blend_img(self, back, fore, img_id='blend', trans=0.7): if self.theme == 'white': fore = 255 - fore if fore.shape[0] != back.shape[0] or fore.shape[0] != back.shape[1]: fore = cv2.resize(fore, (back.shape[1], back.shape[0])) if len(fore.shape) == 2: fore = fore.reshape(fore.shape[0], fore.shape[1], 1) self.imgs[img_id] = (back * (1. - trans) + fore * trans) self.imgs[img_id][self.imgs[img_id] > 255] = 255 self.imgs[img_id][self.imgs[img_id] < 0] = 0 self.imgs[img_id] = self.imgs[img_id].astype(np.uint8).copy() ''' # slow version def gen_colormap(self, img, output_res=None): # num_classes = len(self.colors) img[img < 0] = 0 h, w = img.shape[1], img.shape[2] if output_res is None: output_res = (h * self.down_ratio, w * self.down_ratio) color_map = np.zeros((output_res[0], output_res[1], 3), dtype=np.uint8) for i in range(img.shape[0]): resized = cv2.resize(img[i], (output_res[1], output_res[0])) resized = resized.reshape(output_res[0], output_res[1], 1) cl = self.colors[i] if not (self.theme == 'white') \ else 255 - self.colors[i] color_map = np.maximum(color_map, (resized * cl).astype(np.uint8)) return color_map ''' def gen_colormap(self, img, output_res=None): img = img.copy() c, h, w = img.shape[0], img.shape[1], img.shape[2] if output_res is None: output_res = (h * self.down_ratio, w * self.down_ratio) img = img.transpose(1, 2, 0).reshape(h, w, c, 1).astype(np.float32) colors = np.array( self.colors, dtype=np.float32).reshape(-1, 3)[:c].reshape(1, 1, c, 3) if self.theme == 'white': colors = 255 - colors color_map = (img * colors).max(axis=2).astype(np.uint8) color_map = cv2.resize(color_map, (output_res[0], output_res[1])) return color_map ''' # slow def gen_colormap_hp(self, img, output_res=None): # num_classes = len(self.colors) # img[img < 0] = 0 h, w = img.shape[1], img.shape[2] if output_res is None: output_res = (h * self.down_ratio, w * self.down_ratio) color_map = np.zeros((output_res[0], output_res[1], 3), dtype=np.uint8) for i in range(img.shape[0]): resized = cv2.resize(img[i], (output_res[1], output_res[0])) resized = resized.reshape(output_res[0], output_res[1], 1) cl = self.colors_hp[i] if not (self.theme == 'white') else \ (255 - np.array(self.colors_hp[i])) color_map = np.maximum(color_map, (resized * cl).astype(np.uint8)) return color_map ''' def gen_colormap_hp(self, img, output_res=None): c, h, w = img.shape[0], img.shape[1], img.shape[2] if output_res is None: output_res = (h * self.down_ratio, w * self.down_ratio) img = img.transpose(1, 2, 0).reshape(h, w, c, 1).astype(np.float32) colors = np.array( self.colors_hp, dtype=np.float32).reshape(-1, 3)[:c].reshape(1, 1, c, 3) if self.theme == 'white': colors = 255 - colors color_map = (img * colors).max(axis=2).astype(np.uint8) color_map = cv2.resize(color_map, (output_res[0], output_res[1])) return color_map def add_rect(self, rect1, rect2, c, conf=1, img_id='default'): cv2.rectangle( self.imgs[img_id], (rect1[0], rect1[1]), (rect2[0], rect2[1]), c, 2) if conf < 1: cv2.circle(self.imgs[img_id], (rect1[0], rect1[1]), int(10 * conf), c, 1) cv2.circle(self.imgs[img_id], (rect2[0], rect2[1]), int(10 * conf), c, 1) cv2.circle(self.imgs[img_id], (rect1[0], rect2[1]), int(10 * conf), c, 1) cv2.circle(self.imgs[img_id], (rect2[0], rect1[1]), int(10 * conf), c, 1) def add_coco_bbox(self, bbox, cat, conf=1, show_txt=False, img_id='default'): bbox = np.array(bbox, dtype=np.int32) # cat = (int(cat) + 1) % 80 cat = int(cat) # print('cat', cat, self.names[cat]) c = self.colors[cat][0][0].tolist() if self.theme == 'white': c = (255 - np.array(c)).tolist() # txt = '{}{:.1f}'.format(self.names[cat], conf) txt = '{}{:.1f}'.format(cat, conf) font = cv2.FONT_HERSHEY_SIMPLEX cat_size = cv2.getTextSize(txt, font, 0.5, 2)[0] cv2.rectangle( self.imgs[img_id], (bbox[0], bbox[1]), (bbox[2], bbox[3]), (0, 0, 255), 1) if show_txt: cv2.rectangle(self.imgs[img_id], (bbox[0], bbox[1] - cat_size[1] - 2), (bbox[0] + cat_size[0], bbox[1] - 2), c, -1) cv2.putText(self.imgs[img_id], txt, (bbox[0], bbox[1] - 2), font, 0.5, (0, 0, 0), thickness=1, lineType=cv2.LINE_AA) def add_4ps_coco_bbox(self, bbox, cat, conf=1, show_txt=False, img_id='default'): bbox = np.array(bbox, dtype=np.int32) # cat = (int(cat) + 1) % 80 cat = int(cat) c = self.colors[cat][0][0].tolist() if self.theme == 'white': c = (255 - np.array(c)).tolist() txt = '{}_{:.1f}_{}_{}'.format(str(cat), conf, bbox[-2], bbox[-1]) font = cv2.FONT_HERSHEY_SIMPLEX cat_size = cv2.getTextSize(txt, font, 0.5, 2)[0] cv2.line(self.imgs[img_id], (bbox[0], bbox[1]), (bbox[2], bbox[3]), (0, 0, 255), 2) cv2.line(self.imgs[img_id], (bbox[2], bbox[3]), (bbox[4], bbox[5]), (0, 255, 0), 2) cv2.line(self.imgs[img_id], (bbox[4], bbox[5]), (bbox[6], bbox[7]), (255, 0, 0), 2) cv2.line(self.imgs[img_id], (bbox[6], bbox[7]), (bbox[0], bbox[1]), (0, 255, 255), 2) # cv2.rectangle( # self.imgs[img_id], (bbox[0], bbox[1]), (bbox[2], bbox[3]), (0,0,255), 1) if show_txt: # cv2.rectangle(self.imgs[img_id], # (bbox[0], bbox[1] - cat_size[1] - 2), # (bbox[0] + cat_size[0], bbox[1] - 2), c, -1) cv2.putText(self.imgs[img_id], txt, (int((bbox[0] + bbox[6]) / 2), int((bbox[1] + bbox[7]) / 2)), font, 1, (0, 0, 255), thickness=1, lineType=cv2.LINE_AA) def add_coco_hp(self, points, img_id='default'): points = np.array(points, dtype=np.int32).reshape(self.num_joints, 2) for j in range(self.num_joints): cv2.circle(self.imgs[img_id], (points[j, 0], points[j, 1]), 3, self.colors_hp[j], -1) for j, e in enumerate(self.edges): if points[e].min() > 0: cv2.line(self.imgs[img_id], (points[e[0], 0], points[e[0], 1]), (points[e[1], 0], points[e[1], 1]), self.ec[j], 2, lineType=cv2.LINE_AA) def add_points(self, points, img_id='default'): num_classes = len(points) # assert num_classes == len(self.colors) for i in range(num_classes): for j in range(len(points[i])): c = self.colors[i, 0, 0] cv2.circle(self.imgs[img_id], (points[i][j][0] * self.down_ratio, points[i][j][1] * self.down_ratio), 5, (255, 255, 255), -1) cv2.circle(self.imgs[img_id], (points[i][j][0] * self.down_ratio, points[i][j][1] * self.down_ratio), 3, (int(c[0]), int(c[1]), int(c[2])), -1) def add_corner(self, corner, img_id='default'): font = cv2.FONT_HERSHEY_SIMPLEX cls = int(corner[2]) if cls == 0: rgb = (0, 0, 255) if cls == 1: rgb = (0, 255, 0) if cls == 2: rgb = (255, 0, 0) if cls == 3: rgb = (0, 0, 0) cv2.circle(self.imgs[img_id], (int(corner[0]), int(corner[1])), 3, (255, 0, 0), 2) cv2.putText(self.imgs[img_id], str(cls), (int(corner[0]) - 5, int(corner[1]) - 5), font, 0.5, rgb, thickness=1, lineType=cv2.LINE_AA) def show_all_imgs(self, pause=False, time=0): if not self.ipynb: for i, v in self.imgs.items(): cv2.imshow('{}'.format(i), v) if cv2.waitKey(0 if pause else 1) == 27: import sys sys.exit(0) else: self.ax = None nImgs = len(self.imgs) fig = self.plt.figure(figsize=(nImgs * 10, 10)) nCols = nImgs nRows = nImgs // nCols for i, (k, v) in enumerate(self.imgs.items()): fig.add_subplot(1, nImgs, i + 1) if len(v.shape) == 3: self.plt.imshow(cv2.cvtColor(v, cv2.COLOR_BGR2RGB)) else: self.plt.imshow(v) self.plt.show() def save_img(self, imgId='default', path='./cache/debug/'): cv2.imwrite(path + '{}.png'.format(imgId), self.imgs[imgId]) def save_all_imgs(self, image_name, path='./cache/debug/', prefix='', genID=False): if genID: try: idx = int(np.loadtxt(path + '/id.txt')) except: idx = 0 prefix = idx np.savetxt(path + '/id.txt', np.ones(1) * (idx + 1), fmt='%d') for i, v in self.imgs.items(): # pdb.set_trace() # cv2.imwrite(path + '/{}{}.png'.format(prefix,i), v) cv2.imwrite(path + '/%s' % image_name, v) # print(path+'/%s'%image_name) def remove_side(self, img_id, img): if not (img_id in self.imgs): return ws = img.sum(axis=2).sum(axis=0) l = 0 while ws[l] == 0 and l < len(ws): l += 1 r = ws.shape[0] - 1 while ws[r] == 0 and r > 0: r -= 1 hs = img.sum(axis=2).sum(axis=1) t = 0 while hs[t] == 0 and t < len(hs): t += 1 b = hs.shape[0] - 1 while hs[b] == 0 and b > 0: b -= 1 self.imgs[img_id] = self.imgs[img_id][t:b + 1, l:r + 1].copy() def project_3d_to_bird(self, pt): pt[0] += self.world_size / 2 pt[1] = self.world_size - pt[1] pt = pt * self.out_size / self.world_size return pt.astype(np.int32) def add_ct_detection( self, img, dets, show_box=False, show_txt=True, center_thresh=0.5, img_id='det'): # dets: max_preds x 5 self.imgs[img_id] = img.copy() if type(dets) == type({}): for cat in dets: for i in range(len(dets[cat])): if dets[cat][i, 2] > center_thresh: cl = (self.colors[cat, 0, 0]).tolist() ct = dets[cat][i, :2].astype(np.int32) if show_box: w, h = dets[cat][i, -2], dets[cat][i, -1] x, y = dets[cat][i, 0], dets[cat][i, 1] bbox = np.array([x - w / 2, y - h / 2, x + w / 2, y + h / 2], dtype=np.float32) self.add_coco_bbox( bbox, cat - 1, dets[cat][i, 2], show_txt=show_txt, img_id=img_id) else: for i in range(len(dets)): if dets[i, 2] > center_thresh: # print('dets', dets[i]) cat = int(dets[i, -1]) cl = (self.colors[cat, 0, 0] if self.theme == 'black' else \ 255 - self.colors[cat, 0, 0]).tolist() ct = dets[i, :2].astype(np.int32) * self.down_ratio cv2.circle(self.imgs[img_id], (ct[0], ct[1]), 3, cl, -1) if show_box: w, h = dets[i, -3] * self.down_ratio, dets[i, -2] * self.down_ratio x, y = dets[i, 0] * self.down_ratio, dets[i, 1] * self.down_ratio bbox = np.array([x - w / 2, y - h / 2, x + w / 2, y + h / 2], dtype=np.float32) self.add_coco_bbox(bbox, dets[i, -1], dets[i, 2], img_id=img_id) def add_3d_detection( self, image_or_path, dets, calib, show_txt=False, center_thresh=0.5, img_id='det'): if isinstance(image_or_path, np.ndarray): self.imgs[img_id] = image_or_path else: self.imgs[img_id] = cv2.imread(image_or_path) for cat in dets: for i in range(len(dets[cat])): cl = (self.colors[cat - 1, 0, 0]).tolist() if dets[cat][i, -1] > center_thresh: dim = dets[cat][i, 5:8] loc = dets[cat][i, 8:11] rot_y = dets[cat][i, 11] # loc[1] = loc[1] - dim[0] / 2 + dim[0] / 2 / self.dim_scale # dim = dim / self.dim_scale if loc[2] > 1: box_3d = compute_box_3d(dim, loc, rot_y) box_2d = project_to_image(box_3d, calib) self.imgs[img_id] = draw_box_3d(self.imgs[img_id], box_2d, cl) def compose_vis_add( self, img_path, dets, calib, center_thresh, pred, bev, img_id='out'): self.imgs[img_id] = cv2.imread(img_path) # h, w = self.imgs[img_id].shape[:2] # pred = cv2.resize(pred, (h, w)) h, w = pred.shape[:2] hs, ws = self.imgs[img_id].shape[0] / h, self.imgs[img_id].shape[1] / w self.imgs[img_id] = cv2.resize(self.imgs[img_id], (w, h)) self.add_blend_img(self.imgs[img_id], pred, img_id) for cat in dets: for i in range(len(dets[cat])): cl = (self.colors[cat - 1, 0, 0]).tolist() if dets[cat][i, -1] > center_thresh: dim = dets[cat][i, 5:8] loc = dets[cat][i, 8:11] rot_y = dets[cat][i, 11] # loc[1] = loc[1] - dim[0] / 2 + dim[0] / 2 / self.dim_scale # dim = dim / self.dim_scale if loc[2] > 1: box_3d = compute_box_3d(dim, loc, rot_y) box_2d = project_to_image(box_3d, calib) box_2d[:, 0] /= hs box_2d[:, 1] /= ws self.imgs[img_id] = draw_box_3d(self.imgs[img_id], box_2d, cl) self.imgs[img_id] = np.concatenate( [self.imgs[img_id], self.imgs[bev]], axis=1) def add_2d_detection( self, img, dets, show_box=False, show_txt=True, center_thresh=0.5, img_id='det'): self.imgs[img_id] = img for cat in dets: for i in range(len(dets[cat])): cl = (self.colors[cat - 1, 0, 0]).tolist() if dets[cat][i, -1] > center_thresh: bbox = dets[cat][i, 1:5] self.add_coco_bbox( bbox, cat - 1, dets[cat][i, -1], show_txt=show_txt, img_id=img_id) def add_bird_view(self, dets, center_thresh=0.3, img_id='bird'): bird_view = np.ones((self.out_size, self.out_size, 3), dtype=np.uint8) * 230 for cat in dets: cl = (self.colors[cat - 1, 0, 0]).tolist() lc = (250, 152, 12) for i in range(len(dets[cat])): if dets[cat][i, -1] > center_thresh: dim = dets[cat][i, 5:8] loc = dets[cat][i, 8:11] rot_y = dets[cat][i, 11] rect = compute_box_3d(dim, loc, rot_y)[:4, [0, 2]] for k in range(4): rect[k] = self.project_3d_to_bird(rect[k]) # cv2.circle(bird_view, (rect[k][0], rect[k][1]), 2, lc, -1) cv2.polylines( bird_view, [rect.reshape(-1, 1, 2).astype(np.int32)], True, lc, 2, lineType=cv2.LINE_AA) for e in [[0, 1]]: t = 4 if e == [0, 1] else 1 cv2.line(bird_view, (rect[e[0]][0], rect[e[0]][1]), (rect[e[1]][0], rect[e[1]][1]), lc, t, lineType=cv2.LINE_AA) self.imgs[img_id] = bird_view def add_bird_views(self, dets_dt, dets_gt, center_thresh=0.3, img_id='bird'): alpha = 0.5 bird_view = np.ones((self.out_size, self.out_size, 3), dtype=np.uint8) * 230 for ii, (dets, lc, cc) in enumerate( [(dets_gt, (12, 49, 250), (0, 0, 255)), (dets_dt, (250, 152, 12), (255, 0, 0))]): # cc = np.array(lc, dtype=np.uint8).reshape(1, 1, 3) for cat in dets: cl = (self.colors[cat - 1, 0, 0]).tolist() for i in range(len(dets[cat])): if dets[cat][i, -1] > center_thresh: dim = dets[cat][i, 5:8] loc = dets[cat][i, 8:11] rot_y = dets[cat][i, 11] rect = compute_box_3d(dim, loc, rot_y)[:4, [0, 2]] for k in range(4): rect[k] = self.project_3d_to_bird(rect[k]) if ii == 0: cv2.fillPoly( bird_view, [rect.reshape(-1, 1, 2).astype(np.int32)], lc, lineType=cv2.LINE_AA) else: cv2.polylines( bird_view, [rect.reshape(-1, 1, 2).astype(np.int32)], True, lc, 2, lineType=cv2.LINE_AA) # for e in [[0, 1], [1, 2], [2, 3], [3, 0]]: for e in [[0, 1]]: t = 4 if e == [0, 1] else 1 cv2.line(bird_view, (rect[e[0]][0], rect[e[0]][1]), (rect[e[1]][0], rect[e[1]][1]), lc, t, lineType=cv2.LINE_AA) self.imgs[img_id] = bird_view kitti_class_name = [ 'p', 'v', 'b' ] gta_class_name = [ 'p', 'v' ] pascal_class_name = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"] coco_class_name = [ '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' ] table_class_name = ["box"] huntie_class_name = ['hcp', 'fjxcd', 'czcfp', 'defp', 'zzsfp', 'qtfp', 'sfz_front', 'sfz_back', 'xsz_first', 'xsz_second', 'bank_card', 'jsz_first', 'roll_ticket', 'czr', 'huzhu', 'FedEx', 'birth_certification', 'blicence', 'car_invoice', 'estate', 'food_blicence', 'food_plicence', "jsz_first", "passport_china", "permit_china", "permit_china_miner", "house_cert", "book_blicense", "medical_license", "medical_instrument_license"] video_class_name = ['phone_contract', 'phone_signature'] vehicle_class_name = ["first", "second"] color_list = np.array( [ 1.000, 1.000, 1.000, 0.850, 0.325, 0.098, 0.929, 0.694, 0.125, 0.494, 0.184, 0.556, 0.466, 0.674, 0.188, 0.301, 0.745, 0.933, 0.635, 0.078, 0.184, 0.300, 0.300, 0.300, 0.600, 0.600, 0.600, 1.000, 0.000, 0.000, 1.000, 0.500, 0.000, 0.749, 0.749, 0.000, 0.000, 1.000, 0.000, 0.000, 0.000, 1.000, 0.667, 0.000, 1.000, 0.333, 0.333, 0.000, 0.333, 0.667, 0.000, 0.333, 1.000, 0.000, 0.667, 0.333, 0.000, 0.667, 0.667, 0.000, 0.667, 1.000, 0.000, 1.000, 0.333, 0.000, 1.000, 0.667, 0.000, 1.000, 1.000, 0.000, 0.000, 0.333, 0.500, 0.000, 0.667, 0.500, 0.000, 1.000, 0.500, 0.333, 0.000, 0.500, 0.333, 0.333, 0.500, 0.333, 0.667, 0.500, 0.333, 1.000, 0.500, 0.667, 0.000, 0.500, 0.667, 0.333, 0.500, 0.667, 0.667, 0.500, 0.667, 1.000, 0.500, 1.000, 0.000, 0.500, 1.000, 0.333, 0.500, 1.000, 0.667, 0.500, 1.000, 1.000, 0.500, 0.000, 0.333, 1.000, 0.000, 0.667, 1.000, 0.000, 1.000, 1.000, 0.333, 0.000, 1.000, 0.333, 0.333, 1.000, 0.333, 0.667, 1.000, 0.333, 1.000, 1.000, 0.667, 0.000, 1.000, 0.667, 0.333, 1.000, 0.667, 0.667, 1.000, 0.667, 1.000, 1.000, 1.000, 0.000, 1.000, 1.000, 0.333, 1.000, 1.000, 0.667, 1.000, 0.167, 0.000, 0.000, 0.333, 0.000, 0.000, 0.500, 0.000, 0.000, 0.667, 0.000, 0.000, 0.833, 0.000, 0.000, 1.000, 0.000, 0.000, 0.000, 0.167, 0.000, 0.000, 0.333, 0.000, 0.000, 0.500, 0.000, 0.000, 0.667, 0.000, 0.000, 0.833, 0.000, 0.000, 1.000, 0.000, 0.000, 0.000, 0.167, 0.000, 0.000, 0.333, 0.000, 0.000, 0.500, 0.000, 0.000, 0.667, 0.000, 0.000, 0.833, 0.000, 0.000, 1.000, 0.000, 0.000, 0.000, 0.143, 0.143, 0.143, 0.286, 0.286, 0.286, 0.429, 0.429, 0.429, 0.571, 0.571, 0.571, 0.714, 0.714, 0.714, 0.857, 0.857, 0.857, 0.000, 0.447, 0.741, 0.50, 0.5, 0 ] ).astype(np.float32) color_list = color_list.reshape((-1, 3)) * 255