def main(): parser = argparse.ArgumentParser(description='Create driving video.') parser.add_argument( 'image_folder', type=str, default='', help= 'Path to image folder. The video will be created from these images.') parser.add_argument('--fps', type=int, default=60, help='FPS (Frames per second) setting for the video.') parser.add_argument('--processed', action='store_true', help='Transform the image back to the model space.') args = parser.parse_args() video_file = args.image_folder + '.mp4' print("Creating video {}, FPS={}".format(video_file, args.fps)) clip = ImageSequenceClip(args.image_folder, fps=args.fps) if args.processed: def tmp(im): processed = utils.process_image(im) processed = cv2.cvtColor(processed, color_space['src']) return np.hstack((im, processed)) clip = clip.fl_image(tmp) clip.write_videofile(video_file)
def main(): parser = argparse.ArgumentParser(description='Create driving video.') parser.add_argument( 'image_folder', type=str, default='', help= 'Path to image folder. The video will be created from these images.') parser.add_argument('--fps', type=int, default=60, help='FPS (Frames per second) setting for the video.') args = parser.parse_args() video_file = args.image_folder + '.mp4' print("Creating video {}, FPS={}".format(video_file, args.fps)) clip = ImageSequenceClip(args.image_folder, fps=args.fps) # Show preprocessing effects video_processor = model.VideoProcessor() clip = clip.fl_image(video_processor.process_frame) clip.write_videofile(video_file)
def main(argv): if os.path.exists(OUTPUT_DIR): shutil.rmtree(OUTPUT_DIR) os.makedirs(OUTPUT_DIR) clip = ImageSequenceClip(data.images, fps=60) new_clip = clip.fl_image(process_image) new_clip.write_videofile(VIDEO_OUTPUT, audio=False)
# # In[ ]: # Import everything needed to edit/save/watch video clips from moviepy.editor import VideoFileClip from moviepy.editor import ImageSequenceClip # Define pathname to save the output video output = '../output/test_mapping.mp4' data = Databucket() # Re-initialize data in case you're running this cell multiple times clip = ImageSequenceClip(data.images, fps=60) # Note: output video will be sped up because # recording rate in simulator is fps=25 new_clip = clip.fl_image(process_image) #NOTE: this function expects color images!! get_ipython().run_line_magic('time', 'new_clip.write_videofile(output, audio=False)') # ### This next cell should function as an inline video player # If this fails to render the video, try running the following cell (alternative video rendering method). You can also simply have a look at the saved mp4 in your `/output` folder # In[ ]: from IPython.display import HTML HTML(""" <video width="960" height="540" controls> <source src="{0}"> </video>
# In[ ]: # Import everything needed to edit/save/watch video clips from moviepy.editor import VideoFileClip from moviepy.editor import ImageSequenceClip # Define pathname to save the output video output = './output/test_mapping.mp4' data = Databucket() # Re-initialize data in case you're running this cell multiple times # Note: output video will be sped up because clip = ImageSequenceClip(data.images, fps=60) # recording rate in simulator is fps=25 # NOTE: this function expects color images!! new_clip = clip.fl_image(process_image) get_ipython().magic('time new_clip.write_videofile(output, audio=False)') # ### This next cell should function as an inline video player # If this fails to render the video, try running the following cell (alternative video rendering method). You can also simply have a look at the saved mp4 in your `/output` folder # In[ ]: output = './output/test_mapping.mp4' from IPython.display import HTML HTML(""" <video width="960" height="540" controls> <source src="{0}"> </video>