10 types of datasets

datasets/image_qualified.py

@@ -15,7 +15,7 @@ def calculate_clarity_score(image_path,target_size=(512, 512)):
     return cv2.Laplacian(image, cv2.CV_64F).var()
 
 
-def process_images(input_folder, output_folder,clarity_score_threshold):
+def process_images(input_folder, output_folder,clarity_score_low,clarity_score_high):
     if not os.path.exists(input_folder):
         print(f"输入文件夹不存在：{input_folder}")
         return
@@ -28,11 +28,11 @@ def process_images(input_folder, output_folder,clarity_score_threshold):
                 continue
             file_path = os.path.join(root, filename)
             # print(f'file_path is {file_path}')
-            i+=1
             try:
                 # 清晰度检测
                 clarity_score = calculate_clarity_score(file_path)
-                if clarity_score > clarity_score_threshold:
+                if clarity_score > clarity_score_low and clarity_score<clarity_score_high:
+                    i+=1
                     # 读取图片
                     with Image.open(file_path) as img:
                         # resize
@@ -46,17 +46,19 @@ def process_images(input_folder, output_folder,clarity_score_threshold):
                             os.makedirs(output_subfolder)
                         output_path = os.path.join(output_subfolder, os.path.splitext(filename)[0] + ".png")
                         img.save(output_path, format="PNG")
-                    print(f"处理并保存图片{i}:{file_path} -> {output_path}")
+                    print(f"处理并保存图片{i}:{file_path} -> {output_path}********")
                 else:
-                    print(f"********图片 {file_path} 清晰度评分 {clarity_score} 不满足要求，跳过********")
+                    print(f"********图片 {file_path} 清晰度评分 {clarity_score} 不满足要求，跳过")
             except Exception as e:
-                print(f"********处理图片 {file_path} 时出错：{e}********")
+                print(f"********处理图片 {file_path} 时出错：{e}")
+        print(f'——————处理完毕,成功/图片数量: {i}/{len(files)}——————\n')
 
 
 if __name__ == "__main__":
-    input_folder = 'data_process/datasets/source'  # 输入文件夹路径
+    input_folder = 'data_process/datasets/temp'  # 输入文件夹路径
     output_folder = 'data_process/datasets/target'  # 输出文件夹路径
-    clarity_score_threshold =50
-    process_images(input_folder, output_folder,clarity_score_threshold)
+    clarity_score_low = 50
+    clarity_score_high =3000
+    process_images(input_folder, output_folder,clarity_score_low,clarity_score_high)