mirror of
https://github.com/kkroening/ffmpeg-python.git
synced 2025-04-06 04:15:44 +08:00
Karl Kroening
GitHub
commit
3520e9318d
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@ -57,7 +57,10 @@ out, _ = (ffmpeg
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)
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```
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## [JupyterLab/Notebook widgets](https://github.com/kkroening/ffmpeg-python/blob/master/examples/ffmpeg-numpy.ipynb)
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## [Jupyter Frame Viewer](https://github.com/kkroening/ffmpeg-python/blob/master/examples/ffmpeg-numpy.ipynb)
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<img src="https://raw.githubusercontent.com/kkroening/ffmpeg-python/master/doc/jupyter-screenshot.png" alt="jupyter screenshot" width="75%" />
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## [Jupyter Pipeline Editor](https://github.com/kkroening/ffmpeg-python/blob/master/examples/ffmpeg-numpy.ipynb)
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<img src="https://raw.githubusercontent.com/kkroening/ffmpeg-python/master/doc/jupyter-demo.png" alt="jupyter demo" width="75%" />
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@ -2,7 +2,7 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 116,
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -15,12 +15,12 @@
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},
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{
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"cell_type": "code",
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"execution_count": 117,
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"probe = ffmpeg.probe('in.mp4')\n",
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"video_info = next(stream for stream in probe['streams'] if stream['codec_type'] == 'video')\n",
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"video_info = next(s for s in probe['streams'] if s['codec_type'] == 'video')\n",
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"width = int(video_info['width'])\n",
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"height = int(video_info['height'])\n",
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"num_frames = int(video_info['nb_frames'])"
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@ -28,32 +28,13 @@
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},
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{
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"cell_type": "code",
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"execution_count": 118,
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"metadata": {},
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"outputs": [],
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"source": [
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"out, err = (\n",
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" ffmpeg\n",
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" .input('in.mp4')\n",
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" .output('pipe:', format='rawvideo', pix_fmt='rgb24')\n",
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" .run(capture_stdout=True)\n",
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")\n",
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"video = (\n",
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" np\n",
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" .frombuffer(out, np.uint8)\n",
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" .reshape([-1, height, width, 3])\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 115,
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "17d13d7551114fb39a1fad933cf0398a",
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"model_id": "5f63dc164956464c994ec58d86ee7cd9",
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"version_major": 2,
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"version_minor": 0
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},
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@ -66,11 +47,143 @@
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}
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],
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"source": [
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"out, err = (\n",
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" ffmpeg\n",
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" .input('in.mp4')\n",
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" .output('pipe:', format='rawvideo', pix_fmt='rgb24')\n",
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" .run(capture_stdout=True)\n",
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")\n",
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"video = (\n",
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" np\n",
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" .frombuffer(out, np.uint8)\n",
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" .reshape([-1, height, width, 3])\n",
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")\n",
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"\n",
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"@interact(frame=(0, num_frames))\n",
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"def show_frame(frame=0):\n",
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" plt.imshow(video[frame,:,:,:])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "84bcac52195f47f8854f09acd7666b84",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"interactive(children=(Checkbox(value=True, description='enable_overlay'), Checkbox(value=True, description='en…"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"from io import BytesIO\n",
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"from PIL import Image\n",
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"\n",
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"\n",
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"def extract_frame(stream, frame_num):\n",
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" while isinstance(stream, ffmpeg.nodes.OutputStream):\n",
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" stream = stream.node.incoming_edges[0].upstream_node.stream()\n",
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" out, _ = (\n",
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" stream\n",
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" .filter_('select', 'gte(n,{})'.format(frame_num))\n",
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" .output('pipe:', format='rawvideo', pix_fmt='rgb24', vframes=1)\n",
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" .run(capture_stdout=True, capture_stderr=True)\n",
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" )\n",
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" return np.frombuffer(out, np.uint8).reshape([height, width, 3])\n",
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"\n",
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"\n",
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"def png_to_np(png_bytes):\n",
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" buffer = BytesIO(png_bytes)\n",
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" pil_image = Image.open(buffer)\n",
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" return np.array(pil_image)\n",
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" \n",
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"\n",
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"def build_graph(\n",
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" enable_overlay, flip_overlay, enable_box, box_x, box_y,\n",
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" thickness, color):\n",
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"\n",
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" stream = ffmpeg.input('in.mp4')\n",
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"\n",
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" if enable_overlay:\n",
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" overlay = ffmpeg.input('overlay.png')\n",
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" if flip_overlay:\n",
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" overlay = overlay.hflip()\n",
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" stream = stream.overlay(overlay)\n",
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"\n",
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" if enable_box:\n",
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" stream = stream.drawbox(\n",
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" box_x, box_y, 120, 120, color=color, t=thickness)\n",
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"\n",
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" return stream.output('out.mp4')\n",
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"\n",
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"\n",
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"def show_image(ax, stream, frame_num):\n",
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" try:\n",
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" image = extract_frame(stream, frame_num)\n",
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" ax.imshow(image)\n",
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" ax.axis('off')\n",
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" except ffmpeg.Error as e:\n",
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" print(e.stderr.decode())\n",
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"\n",
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"\n",
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"def show_graph(ax, stream, detail):\n",
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" data = ffmpeg.view(stream, detail=detail, pipe=True)\n",
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" image = png_to_np(data)\n",
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" ax.imshow(image, aspect='equal', interpolation='hanning')\n",
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" ax.set_xlim(0, 1100)\n",
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" ax.axis('off')\n",
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"\n",
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"\n",
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"@interact(\n",
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" frame_num=(0, num_frames),\n",
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" box_x=(0, 200),\n",
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" box_y=(0, 200),\n",
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" thickness=(1, 40),\n",
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" color=['red', 'green', 'magenta', 'blue'],\n",
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")\n",
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"def f(\n",
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" enable_overlay=True,\n",
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" enable_box=True,\n",
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" flip_overlay=True,\n",
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" graph_detail=False,\n",
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" frame_num=0,\n",
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" box_x=50,\n",
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" box_y=50,\n",
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" thickness=5,\n",
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" color='red'):\n",
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"\n",
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" stream = build_graph(\n",
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" enable_overlay,\n",
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" flip_overlay,\n",
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" enable_box,\n",
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" box_x,\n",
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" box_y,\n",
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" thickness,\n",
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" color\n",
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" )\n",
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"\n",
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" fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(15,4))\n",
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" plt.tight_layout()\n",
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" show_image(ax0, stream, frame_num)\n",
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" show_graph(ax1, stream, graph_detail)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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BIN
examples/in.mp4
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examples/in.mp4
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BIN
examples/overlay.png
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examples/overlay.png
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After Width: | Height: | Size: 2.2 KiB |
@ -1,4 +1,9 @@
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ffmpeg-python
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gevent
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google-cloud-speech
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graphviz
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ipywidgets
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jupyter
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matplotlib
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Pillow
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tqdm
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