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.gitattributes

@@ -36,3 +36,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 build/lib.linux-x86_64-cpython-310/sam2/_C.so filter=lfs diff=lfs merge=lfs -text
 build/temp.linux-x86_64-cpython-310/sam2/csrc/connected_components.o filter=lfs diff=lfs merge=lfs -text
 sam2/_C.so filter=lfs diff=lfs merge=lfs -text
+逆风散羽之别.mp4 filter=lfs diff=lfs merge=lfs -text

video_filter.py

@@ -0,0 +1,91 @@
+'''
+python video_filter.py input_video.mp4 mask_area_ratios.json 0.1 filtered_video_01.mp4
+
+python video_filter.py input_video.mp4 mask_area_ratios.json 0.0 filtered_video_00.mp4
+'''
+
+import argparse
+import cv2
+import json
+import os
+import numpy as np
+from moviepy.editor import ImageSequenceClip
+
+def process_video(video_path, json_path, threshold, output_path):
+    # 读取视频
+    if os.path.exists("input_video.mp4"):
+        video_path = "input_video.mp4"
+
+    cap = cv2.VideoCapture(video_path)
+    input_fps = cap.get(cv2.CAP_PROP_FPS)  # 获取输入视频的帧率
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))  # 获取总帧数
+
+    # 读取所有帧
+    all_frames = []
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)  # 转换为RGB
+        all_frames.append(frame_rgb)
+    cap.release()
+
+    # 读取JSON文件
+    with open(json_path, 'r') as f:
+        mask_area_ratios = json.load(f)
+
+    # 获取mask_area_ratios中的帧索引和对应的ratio
+    frame_indices = [int(key.split("_")[1]) for key in mask_area_ratios.keys()]
+    ratios = [mask_area_ratios[key] for key in mask_area_ratios.keys()]
+
+    # 创建一个长度为 total_frames 的数组来存储对应的 ratio
+    all_ratios = [None] * len(all_frames)
+
+    # 将 mask_area_ratios 的帧索引和对应的 ratio 映射到 all_ratios
+    for frame_index, ratio in zip(frame_indices, ratios):
+        scaled_index = int(frame_index * (len(all_frames) - 1) / max(frame_indices))
+        all_ratios[scaled_index] = ratio
+
+    # 填充缺失的 ratio 值，使用最邻近值
+    for i in range(len(all_ratios)):
+        if all_ratios[i] is None:
+            # 找到最近的非空值
+            left_index = i - 1
+            right_index = i + 1
+            while left_index >= 0 and all_ratios[left_index] is None:
+                left_index -= 1
+            while right_index < len(all_ratios) and all_ratios[right_index] is None:
+                right_index += 1
+
+            if left_index >= 0 and right_index < len(all_ratios):
+                # 使用最近的左边或右边的值
+                all_ratios[i] = all_ratios[left_index] if all_ratios[left_index] is not None else all_ratios[right_index]
+            elif left_index >= 0:
+                all_ratios[i] = all_ratios[left_index]
+            elif right_index < len(all_ratios):
+                all_ratios[i] = all_ratios[right_index]
+
+    # 筛选符合条件的帧
+    frames = []
+    for frame_idx, ratio in enumerate(all_ratios):
+        if ratio >= threshold:
+            frames.append(all_frames[frame_idx])
+
+    # 保存符合条件的帧为视频
+    if frames:
+        clip = ImageSequenceClip(frames, fps=input_fps)  # 使用输入视频的帧率
+        clip.write_videofile(output_path, codec="libx264")
+        print(f"Output video saved to {output_path}")
+    else:
+        print("No frames meet the threshold condition.")
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Filter video frames based on mask area ratios.")
+    parser.add_argument("video_path", type=str, nargs='?', default="input_video.mp4", help="Path to the input video file. Default: input_video.mp4")
+    parser.add_argument("json_path", type=str, nargs='?', default="mask_area_ratios.json", help="Path to the JSON file containing mask area ratios. Default: mask_area_ratios.json")
+    parser.add_argument("threshold", type=float, nargs='?', default=0.25, help="Threshold for mask area ratio. Default: 0.25")
+    parser.add_argument("output_path", type=str, nargs='?', default="output_seg_video.mp4", help="Path to save the output video. Default: output_seg_video.mp4")
+
+    args = parser.parse_args()
+
+    process_video(args.video_path, args.json_path, args.threshold, args.output_path)

video_filter_app.py

@@ -0,0 +1,90 @@
+import gradio as gr
+import cv2
+import json
+import os
+import numpy as np
+from moviepy.editor import ImageSequenceClip
+
+def process_video(video_path, json_path, threshold):
+    # 读取视频
+    if os.path.exists("input_video.mp4"):
+        video_path = "input_video.mp4"
+
+    cap = cv2.VideoCapture(video_path)
+    input_fps = cap.get(cv2.CAP_PROP_FPS)  # 获取输入视频的帧率
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))  # 获取总帧数
+
+    # 读取所有帧
+    all_frames = []
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)  # 转换为RGB
+        all_frames.append(frame_rgb)
+    cap.release()
+
+    # 读取JSON文件
+    with open(json_path, 'r') as f:
+        mask_area_ratios = json.load(f)
+
+    # 获取mask_area_ratios中的帧索引和对应的ratio
+    frame_indices = [int(key.split("_")[1]) for key in mask_area_ratios.keys()]
+    ratios = [mask_area_ratios[key] for key in mask_area_ratios.keys()]
+
+    # 创建一个长度为 total_frames 的数组来存储对应的 ratio
+    all_ratios = [None] * len(all_frames)
+
+    # 将 mask_area_ratios 的帧索引和对应的 ratio 映射到 all_ratios
+    for frame_index, ratio in zip(frame_indices, ratios):
+        scaled_index = int(frame_index * (len(all_frames) - 1) / max(frame_indices))
+        all_ratios[scaled_index] = ratio
+
+    # 填充缺失的 ratio 值，使用最邻近值
+    for i in range(len(all_ratios)):
+        if all_ratios[i] is None:
+            # 找到最近的非空值
+            left_index = i - 1
+            right_index = i + 1
+            while left_index >= 0 and all_ratios[left_index] is None:
+                left_index -= 1
+            while right_index < len(all_ratios) and all_ratios[right_index] is None:
+                right_index += 1
+
+            if left_index >= 0 and right_index < len(all_ratios):
+                # 使用最近的左边或右边的值
+                all_ratios[i] = all_ratios[left_index] if all_ratios[left_index] is not None else all_ratios[right_index]
+            elif left_index >= 0:
+                all_ratios[i] = all_ratios[left_index]
+            elif right_index < len(all_ratios):
+                all_ratios[i] = all_ratios[right_index]
+
+    # 筛选符合条件的帧
+    frames = []
+    for frame_idx, ratio in enumerate(all_ratios):
+        if ratio >= threshold:
+            frames.append(all_frames[frame_idx])
+
+    # 保存符合条件的帧为视频
+    if frames:
+        output_path = "output_seg_video.mp4"
+        clip = ImageSequenceClip(frames, fps=input_fps)  # 使用输入视频的帧率
+        clip.write_videofile(output_path, codec="libx264")
+        return output_path
+    else:
+        return "No frames meet the threshold condition."
+
+# Gradio界面
+iface = gr.Interface(
+    fn=process_video,
+    inputs=[
+        gr.Video(label="Input Video", value="input_video.mp4"),
+        gr.Textbox(label="JSON File Path", value="mask_area_ratios.json"),
+        gr.Number(label="Threshold", value=0.25)
+    ],
+    outputs=gr.Video(label="Output Video"),
+    title="Video Frame Filter",
+    description="Filter video frames based on mask area ratios and save the result as a new video."
+)
+
+iface.launch(share=True)

逆风散羽之别.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0e24dc9cbb5b65e0a3a77631a99311fb6e048abee86396f0d69a2ceb95998038
+size 59887885