LSP数据集与MPII数据集标签转txt文件（字典形式储存）

2024-07-05 16:59| 来源: 网络整理| 查看: 265

LSP数据集和MPII数据集是常见的人体姿态识别公开数据集，数据中的label的保存格式为mat格式（mat格式为matlab存储文件的格式）。

为了方便python和c++调用数据的label，常常会把存储格式设为 txt 格式和 json 格式。为了方便看到数据集中的关键点，作者将关键点进行了可视化展示。

LSP数据集 import glob import os from scipy.io import loadmat import numpy as np from PIL import Image import cv2 colors = [(50, 0, 0), (100, 0, 0), (150, 0, 0), (200, 0, 0), (250, 0, 0), (0, 50, 0), (0, 100, 0),(0, 150, 0), (0, 200, 0),(0, 250, 0), (0, 0, 50), (0, 0, 100), (0, 0, 150), (0, 0, 200), (0, 0, 250), (50, 50, 50)] def save_joints(mat_path,image_path,save_path): """ mat_path 是 lsp数据集mat文件所在地址，包含mat文件名 image_path 是 lsp数据集图像的地址，不包含图像名 save_path 是要将lsp数据集中关键点保存的地址名 lsp数据集共2000张图片 """ joints = loadmat(mat_path) joints = joints["joints"].transpose(2,0,1) joints = joints[:,:2,:] num = 0 for img_path in glob.glob("%s/*.jpg" %image_path): img_name = img_path.split("\\")[-1] img = Image.open(img_path) img = np.array(img,dtype=np.uint8) img = cv2.cvtColor(img,cv2.COLOR_RGB2BGR) cen_points = joints[num,...] points_num = cen_points.shape[-1] point_dict = {} for points_ in range(points_num): point_x = cen_points[0,points_] point_y = cen_points[1,points_] point_dict[str(points_)] = [int(point_x),int(point_y)] img1 = cv2.circle(img, (int(point_x), int(point_y)), 5, colors[points_], thickness=-1) img1 = cv2.putText(img, str(points_), (int(point_x) + 10, int(point_y)), cv2.FONT_HERSHEY_SIMPLEX, 1, colors[points_], 1) with open(os.path.join(save_path,img_name.split(".")[0]+".txt"),"w") as img_txt: img_txt.write(str(point_dict)) img_txt.close() num += 1 # 若不想看图片中关键点的位置是否准确，请注释掉后面两行 cv2.imshow("img",img) cv2.waitKey() MPII数据集 import os from scipy.io import loadmat import numpy as np from PIL import Image import cv2 def save_joints(mat_path,image_path,save_path): joint_data_fn = save_path mat = loadmat(mat_path) mpii_images = image_path for i, (anno, train_flag) in enumerate( zip(mat['RELEASE']['annolist'][0, 0][0], mat['RELEASE']['img_train'][0, 0][0])): img_fn = anno['image']['name'][0, 0][0] img_path = os.path.join(mpii_images, img_fn) if not os.path.exists(img_path): print("error, not exist", img_path) continue img = Image.open(os.path.join(image_path, img_fn)) img = np.array(img,dtype=np.uint8) img1 = cv2.cvtColor(img,cv2.COLOR_RGB2BGR) height, width, _ = img1.shape train_flag = int(train_flag) if 'x1' in str(anno['annorect'].dtype): head_rect = zip( [x1[0, 0] for x1 in anno['annorect']['x1'][0]], [y1[0, 0] for y1 in anno['annorect']['y1'][0]], [x2[0, 0] for x2 in anno['annorect']['x2'][0]], [y2[0, 0] for y2 in anno['annorect']['y2'][0]]) if 'annopoints' in str(anno['annorect'].dtype): # only one person annopoints = anno['annorect']['annopoints'][0] head_x1s = anno['annorect']['x1'][0] head_y1s = anno['annorect']['y1'][0] head_x2s = anno['annorect']['x2'][0] head_y2s = anno['annorect']['y2'][0] image_write = "" for annopoint, head_x1, head_y1, head_x2, head_y2 in zip( annopoints, head_x1s, head_y1s, head_x2s, head_y2s): if annopoint != []: head_rect = [float(head_x1[0, 0]), float(head_y1[0, 0]), float(head_x2[0, 0]), float(head_y2[0, 0])] # build feed_dict feed_dict = {} feed_dict['width'] = width feed_dict['height'] = height # joint coordinates annopoint = annopoint['point'][0, 0] j_id = [str(j_i[0, 0]) for j_i in annopoint['id'][0]] x = [x[0, 0] for x in annopoint['x'][0]] y = [y[0, 0] for y in annopoint['y'][0]] joint_pos = {} for _j_id, (_x, _y) in zip(j_id, zip(x, y)): joint_pos[str(_j_id)] = [float(_x), float(_y)] # joint_pos = fix_wrong_joints(joint_pos) # visiblity list if 'is_visible' in str(annopoint.dtype): vis = [v[0] if v else [0] for v in annopoint['is_visible'][0]] vis = dict([(k, int(v[0])) if len(v) > 0 else v for k, v in zip(j_id, vis)]) else: vis = None feed_dict['x'] = x feed_dict['y'] = y feed_dict['vis'] = vis feed_dict['filename'] = img_fn img1 = cv2.rectangle(img1, (int(head_rect[0]), int(head_rect[1])), (int(head_rect[2]), int(head_rect[3])), color=(255, 0, 0), thickness=4) colors = [(50, 0, 0), (100, 0, 0), (150, 0, 0), (200, 0, 0), (250, 0, 0), (0, 50, 0), (0, 100, 0),(0, 150, 0), (0, 200, 0),(0, 250, 0), (0, 0, 50), (0, 0, 100), (0, 0, 150), (0, 0, 200), (0, 0, 250), (50, 50, 50)] for mm in range(len(joint_pos)): img1 = cv2.circle(img1, (int(joint_pos[str(list(joint_pos.keys())[mm])][0]), int(joint_pos[str(list(joint_pos.keys())[mm])][1])),10, colors[mm], thickness=-1) img1 = cv2.putText(img1, str(mm), (int(joint_pos[str(list(joint_pos.keys())[mm])][0]) + 10, int(joint_pos[str(list(joint_pos.keys())[mm])][1])), cv2.FONT_HERSHEY_SIMPLEX, 1, colors[mm], 1) data = { # 'filename': img_fn, 'train': train_flag, 'head_rect': head_rect, 'is_visible': vis, 'joint_pos': joint_pos } image_write = image_write + str(data) + "\n" fp = open(os.path.join(joint_data_fn,img_fn.split(".")[0]+".txt"), 'w') fp.write(image_write) fp.close() print(f" {img_fn}.txt 保存成功") # 若不想看图片中关键点的位置是否准确，请注释掉后面三行 cv2.imshow("img_video", img1) cv2.waitKey() cv2.destroyAllWindows()

注：MPII数据集label转化做过参考。

参考：GitHub - Fangyh09/PoseDatasets: Filter multiple pose datasets (coco, flic, lsp, mpii, ai_challenge)

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