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| # Dependencies, see also requirement.txt ;) | |
| import gradio as gr | |
| import cv2 | |
| import numpy as np | |
| import os | |
| from scenedetect import open_video, SceneManager | |
| from scenedetect.detectors import ContentDetector | |
| from moviepy.video.io.ffmpeg_tools import ffmpeg_extract_subclip | |
| # ————————————————————————————————————————————————— | |
| title = "Scene Edit Detection" | |
| description = "<p style='text-align: center'>Gradio demo of PySceneDetect. <br />Automatically find every shots in a video sequence</p><p style='text-align: center'> 1. gives you timecode in/out for each shot. 2. saves each shot as a splitted mp4 video chunk for you to download. 3. diplays a thumbnail for each shot as a gallery output.<br /> <img id='visitor-badge' alt='visitor badge' src='https://visitor-badge.glitch.me/badge?page_id=gradio-blocks.scene-edit-detection' style='display: inline-block'/></b></p>" | |
| article = "<p style='text-align: center'><a href='http://scenedetect.com/en/latest/' target='_blank'>PySceneDetect website</a> | <a href='https://github.com/Breakthrough/PySceneDetect' target='_blank'>Github Repo</a></p>" | |
| # ————————————————————————————————————————————————— | |
| # SET INPUTS | |
| video_input = gr.Video(source="upload", format="mp4", label="Video Sequence", mirror_webcam=False) | |
| threshold = gr.Slider(label="Threshold pixel comparison: if exceeded, triggers a scene cut. Default: 27.0", minimum=15.0, maximum=40.0, value=27.0) | |
| # ————————————————————————————————————————————————— | |
| def convert_to_tuple(list): | |
| return tuple(list); | |
| def find_scenes(video_path, threshold): | |
| # file name without extension | |
| filename = os.path.splitext(os.path.basename(video_path))[0] | |
| # Open our video, create a scene manager, and add a detector. | |
| video = open_video(video_path) | |
| scene_manager = SceneManager() | |
| scene_manager.add_detector( | |
| ContentDetector(threshold=threshold)) | |
| # Start detection | |
| scene_manager.detect_scenes(video, show_progress=True) | |
| scene_list = scene_manager.get_scene_list() | |
| # Push the list of scenes into data_outputs | |
| data_outputs.append(scene_list) | |
| gradio_components_outputs.append("json") | |
| #print(scene_list) | |
| timecodes = [] | |
| timecodes.append({"title": filename + ".mp4", "fps": scene_list[0][0].get_framerate()}) | |
| shots = [] | |
| stills = [] | |
| # For each shot found, set entry and exit points as seconds from frame number | |
| # Then split video into chunks and store them into shots List | |
| # Then extract first frame of each shot as thumbnail for the gallery | |
| for i, shot in enumerate(scene_list): | |
| # STEP 1 | |
| # Get timecode in seconds | |
| framerate = shot[0].get_framerate() | |
| shot_in = shot[0].get_frames() / framerate | |
| shot_out = shot[1].get_frames() / framerate | |
| tc_in = shot[0].get_timecode() | |
| tc_out = shot[1].get_timecode() | |
| frame_in = shot[0].get_frames() | |
| frame_out = shot[1].get_frames() | |
| timecode = {"tc_in": tc_in, "tc_out": tc_out, "frame_in": frame_in, "frame_out": frame_out} | |
| timecodes.append(timecode) | |
| # Set name template for each shot | |
| target_name = "shot_" + str(i+1) + "_" + str(filename) + ".mp4" | |
| # Split chunk | |
| ffmpeg_extract_subclip(video_path, shot_in, shot_out, targetname=target_name) | |
| # Push chunk into shots List | |
| shots.append(target_name) | |
| # Push each chunk into data_outputs | |
| data_outputs.append(target_name) | |
| gradio_components_outputs.append("video") | |
| # ————————————————————————————————————————————————— | |
| # STEP 2 | |
| # extract first frame of each shot with cv2 | |
| vid = cv2.VideoCapture(video_path) | |
| fps = vid.get(cv2.CAP_PROP_FPS) | |
| print('frames per second =',fps) | |
| frame_id = shot[0].get_frames() # value from scene_list from step 1 | |
| vid.set(cv2.CAP_PROP_POS_FRAMES, frame_id) | |
| ret, frame = vid.read() | |
| # Save frame as PNG file | |
| img = (str(frame_id) + '_screenshot.png', 'shot ' + str(frame_id)) | |
| cv2.imwrite(img,frame) | |
| # Push image into stills List | |
| stills.append(img) | |
| # Push the list of video shots into data_outputs for Gradio file component | |
| data_outputs.append(shots) | |
| gradio_components_outputs.append("file") | |
| # Push the list of still images into data_outputs | |
| data_outputs.append(stills) | |
| gradio_components_outputs.append("gallery") | |
| # This would have been used as gradio outputs, | |
| # if we could set number of outputs after the interface launch | |
| # That's not (yet ?) possible | |
| results = convert_to_tuple(data_outputs) | |
| print(results) | |
| # return List of shots as JSON, List of video chunks, List of still images | |
| # * | |
| # Would be nice to be able to return my results tuple as outputs, | |
| # while number of chunks found is not fixed: | |
| # return results | |
| return timecodes, shots, stills | |
| # ————————————————————————————————————————————————— | |
| # SET DATA AND COMPONENTS OUTPUTS | |
| # This would be filled like this: | |
| # data_outputs = [ [List from detection], "video_chunk_n0.mp4", "video_chunk_n1.mp4", ... , "video_chunk_n.mp4", [List of video filepath to download], [List of still images from each shot found] ] | |
| data_outputs = [] | |
| # This would be filled like this: | |
| # gradio_components_outputs = [ "json", "video", "video", ... , "video", "file", "gallery" ] | |
| gradio_components_outputs = [] | |
| #SET OUTPUTS | |
| # This would be nice if number of outputs could be set after Interface Launch: | |
| # because we do not know how many shots will be detected | |
| # gradio_components_outputs = [ "json", "video", "video", ... , "video", "file", "gallery" ] | |
| # outputs = gradio_components_outputs | |
| # ANOTHER SOLUTION WOULD BE USING A (FUTURE ?) "VIDEO GALLERY" GRADIO COMPONENT FROM LIST :) | |
| outputs = [gr.JSON(label="Shots detected"), gr.File(label="Downloadable Shots"), gr.Gallery(label="Still Images from each shot").style(grid=3)] | |
| # ————————————————————————————————————————————————— | |
| print('Hello Sylvain') | |
| gr.Interface(fn=find_scenes, inputs=[video_input, threshold], outputs=outputs, title=title, description=description, article=article).launch() |