Upload run_video_ccip.py

run_video_ccip.py

@@ -0,0 +1,130 @@
+'''
+python run_video_ccip.py Beyond_the_Boundary_Videos_sm Beyond_the_Boundary_Videos_sm_named --image_dir named_image_dir
+
+import pandas as pd 
+import pathlib
+import json
+def read_j(x):
+    with open(x, "r") as f:
+        return json.load(f)
+
+path_s = pd.Series(list(pathlib.Path("Beyond_the_Boundary_Videos_sm_named/").rglob("*.json"))).map(str)
+df = pd.DataFrame(path_s.head(int(1e10)).map(
+    lambda x: (x, read_j(x))
+).values.tolist()
+            ).explode(1).applymap(
+    lambda x: x["results"] if type(x) == type({}) else x
+            ).explode(1)
+df
+right_df = pd.json_normalize(df[1])
+df = pd.concat([df.reset_index().iloc[:, 1:], right_df.reset_index().iloc[:,1:]], axis = 1)
+df = df[
+    df["prediction"] == "Same"
+]
+###df[0].sort_values().drop_duplicates()
+df
+'''
+
+import os
+import json
+from tqdm import tqdm
+from PIL import Image
+from ccip import _VALID_MODEL_NAMES, _DEFAULT_MODEL_NAMES, ccip_difference, ccip_default_threshold
+import pathlib
+import argparse
+from moviepy.editor import VideoFileClip
+
+def load_images_from_directory(image_dir):
+    """
+    从指定目录加载图片，构建字典。
+    键为图片的文件名（不含扩展名），值为图片的 PIL.Image 对象。
+    """
+    name_image_dict = {}
+    image_paths = list(pathlib.Path(image_dir).rglob("*.png")) + list(pathlib.Path(image_dir).rglob("*.jpg")) + list(pathlib.Path(image_dir).rglob("*.jpeg"))
+    
+    for image_path in tqdm(image_paths, desc="Loading images"):
+        image = Image.open(image_path)
+        name = os.path.splitext(os.path.basename(image_path))[0]  # 去掉扩展名
+        name_image_dict[name] = image
+    
+    return name_image_dict
+
+def _compare_with_dataset(imagex, model_name, name_image_dict):
+    threshold = ccip_default_threshold(model_name)
+    results = []
+
+    for name, imagey in name_image_dict.items():
+        diff = ccip_difference(imagex, imagey)
+        result = {
+            "difference": diff,
+            "prediction": 'Same' if diff <= threshold else 'Not Same',
+            "name": name
+        }
+        results.append(result)
+
+    # 按照 diff 值进行排序
+    results.sort(key=lambda x: x["difference"])
+
+    return results
+
+def process_video(video_path, model_name, output_dir, max_frames, name_image_dict):
+    # 打开视频文件
+    clip = VideoFileClip(video_path)
+    duration = clip.duration
+    fps = clip.fps
+    total_frames = int(duration * fps)
+    
+    # 计算帧间隔
+    frame_interval = max(1, total_frames // max_frames)
+    
+    # 生成输出文件名
+    video_name = os.path.splitext(os.path.basename(video_path))[0]
+    output_file = os.path.join(output_dir, f"{video_name}.json")
+    
+    results = []
+    
+    # 采样帧并处理
+    for i in tqdm(range(0, total_frames, frame_interval), desc="Processing frames"):
+        frame = clip.get_frame(i / fps)
+        image = Image.fromarray(frame)
+        frame_results = _compare_with_dataset(image, model_name, name_image_dict)
+        results.append({
+            "frame_time": i / fps,
+            "results": frame_results
+        })
+    
+    # 保存结果到 JSON 文件
+    with open(output_file, 'w') as f:
+        json.dump(results, f, indent=4)
+
+def main():
+    parser = argparse.ArgumentParser(description="Compare videos with a dataset and save results as JSON.")
+    parser.add_argument("input_path", type=str, help="Path to the input video or directory containing videos.")
+    parser.add_argument("output_dir", type=str, help="Directory to save the output JSON files.")
+    parser.add_argument("--image_dir", type=str, required=True, help="Directory containing images to compare with.")
+    parser.add_argument("--model", type=str, default=_DEFAULT_MODEL_NAMES, choices=_VALID_MODEL_NAMES, help="Model to use for comparison.")
+    parser.add_argument("--max_frames", type=int, default=3, help="Maximum number of frames to process per video.")
+    
+    args = parser.parse_args()
+
+    # 确保输出目录存在
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    # 加载图片数据集
+    name_image_dict = load_images_from_directory(args.image_dir)
+
+    # 判断输入路径是文件还是目录
+    if os.path.isfile(args.input_path):
+        video_paths = [args.input_path]
+    elif os.path.isdir(args.input_path):
+        video_paths = list(pathlib.Path(args.input_path).rglob("*.mp4")) + list(pathlib.Path(args.input_path).rglob("*.avi"))
+    else:
+        raise ValueError("Input path must be a valid file or directory.")
+    video_paths = list(map(str, video_paths))
+
+    # 处理每个视频
+    for video_path in tqdm(video_paths, desc="Processing videos"):
+        process_video(video_path, args.model, args.output_dir, args.max_frames, name_image_dict)
+
+if __name__ == '__main__':
+    main()