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DATA/test_landmark.py

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+# import os
+# import cv2
+# import time
+# import glob
+# import argparse
+# import scipy
+# import numpy as np
+# from PIL import Image
+# import torch
+# from tqdm import tqdm
+# from itertools import cycle
+
+# from extract_kp_videos_safe import KeypointExtractor
+# import numpy as np
+# from PIL import Image
+# with torch.no_grad():
+#     img_np =cv2.imread('Strawberry Monster.png')
+#     predictor = KeypointExtractor('cuda')
+#     dets = predictor.det_net.detect_faces(img_np, 0.97)
+#     if len(dets) == 0:
+#        detect = False
+#     else:
+#         print("success")
+# import os
+# import cv2
+# import torch
+# from tqdm import tqdm
+# from extract_kp_videos_safe import KeypointExtractor
+
+# # 创建 KeypointExtractor 实例
+
+# # 设置文件夹路径
+# folder_path = 'control_inversion'
+# landmark_detect_false=0
+# landmark_detect_success=0
+# # 遍历文件夹中的图像文件
+# for filename in tqdm(os.listdir(path)):
+#     if filename.endswith('.png') or filename.endswith('.jpg'):
+#         # 读取图像
+#         image_path = os.path.join(folder_path, filename)
+#         img_np = cv2.imread(image_path)
+
+#         # 进行人脸检测和关键点提取
+#         with torch.no_grad():
+#             predictor = KeypointExtractor('cuda')
+#             dets = predictor.det_net.detect_faces(img_np, 0.97)
+#         if len(dets) == 0:
+#             landmark_detect_false += 1
+#         else:
+#             landmark_detect_success += 1
+
+# detect_rate = landmark_detect_success/(landmark_detect_success+landmark_detect_false)
+# print(detect_rate)
+# import os
+# import cv2
+# import torch
+# from tqdm import tqdm
+# from extract_kp_videos_safe import KeypointExtractor
+
+# # 设置文件夹路径
+# folder_path = 'prompts'
+
+# # 初始化成功和失败的计数
+# total_landmark_detect_success = 0
+# total_landmark_detect_false = 0
+
+# # 遍历文件夹中的 txt 文件
+# for txt_filename in os.listdir(folder_path):
+#     if txt_filename.endswith('.txt'):
+#         txt_file_path = os.path.join(folder_path, txt_filename)
+
+#         # 读取 txt 文件中的图片列表
+#         with open(txt_file_path, 'r') as file:
+#             image_list = file.read().splitlines()
+
+#         landmark_detect_success = 0
+#         landmark_detect_false = 0
+
+#         # 遍历 txt 文件中的图片列表
+#         for image_filename in tqdm(image_list, desc=f'Processing {txt_filename}'):
+#             image_path = os.path.join('control_inversion', image_filename+'.png')
+#             if image_path.endswith('.png') or image_path.endswith('.jpg'):
+#                 img_np = cv2.imread(image_path)
+
+#                 # 进行人脸检测和关键点提取
+#                 with torch.no_grad():
+#                     predictor = KeypointExtractor('cuda')
+#                     dets = predictor.det_net.detect_faces(img_np, 0.97)
+
+#                 if len(dets) == 0:
+#                     landmark_detect_false += 1
+#                 else:
+#                     landmark_detect_success += 1
+
+#         # 计算检测率
+#         detect_rate = landmark_detect_success / (landmark_detect_success + landmark_detect_false)
+#         print(f'{txt_filename}: Detect Rate = {detect_rate}')
+
+#         # 更新总的计数
+#         total_landmark_detect_success += landmark_detect_success
+#         total_landmark_detect_false += landmark_detect_false
+
+# # 计算总的检测率
+# total_detect_rate = total_landmark_detect_success / (total_landmark_detect_success + total_landmark_detect_false)
+# print(f'Total Detect Rate = {total_detect_rate}')
+import os
+import sys
+import cv2
+import torch
+from tqdm import tqdm
+from chat_anything.sad_talker.face3d.extract_kp_videos_safe import KeypointExtractor
+
+# 设置文件夹路径
+folder_path = sys.argv[1]
+
+# 初始化成功和失败的计数
+total_landmark_detect_success = 0
+total_landmark_detect_false = 0
+
+# 遍历文件夹中的 txt 文件
+for txt_filename in os.listdir(folder_path):
+    if txt_filename.endswith('.txt'):
+        txt_file_path = os.path.join(folder_path, txt_filename)
+
+        # # 读取 txt 文件中的图片列表
+        # with open(txt_file_path, 'r') as file:
+        #     image_list = file.read().splitlines()
+        image_list = os.listdir(txt_file_path)
+        landmark_detect_success = 0
+        landmark_detect_false = 0
+
+        # 遍历 txt 文件中的图片列表
+        for image_filename in tqdm(image_list, desc=f'Processing {txt_filename}'):
+            image_path = os.path.join(txt_file_path, image_filename)
+            if image_path.endswith('.png') or image_path.endswith('.jpg'):
+                img_np = cv2.imread(image_path)
+
+                # 进行人脸检测和关键点提取
+                with torch.no_grad():
+                    predictor = KeypointExtractor('cuda')
+                    dets = predictor.det_net.detect_faces(img_np, 0.97)
+
+                if len(dets) == 0:
+                    landmark_detect_false += 1
+                else:
+                    landmark_detect_success += 1
+
+        # 计算检测率
+        detect_rate = landmark_detect_success / (landmark_detect_success + landmark_detect_false)
+        print(f'{txt_filename}: Detect Rate = {detect_rate}')
+
+        # 更新总的计数
+        total_landmark_detect_success += landmark_detect_success
+        total_landmark_detect_false += landmark_detect_false
+
+# 计算总的检测率
+total_detect_rate = total_landmark_detect_success / (total_landmark_detect_success + total_landmark_detect_false)
+print(f'Total Detect Rate = {total_detect_rate}')