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>
