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"""
Tests ability to load video in streamlit under the following conditions:
- In-memory
- From OpenCV object
"""
import numpy as np
import cv2
import av
import io
import streamlit as st
import pandas as pd
import numpy as np
from tqdm import tqdm
def make_sample_image(i):
"""Build synthetic "raw BGR" image for testing"""
p = width // 60
img = np.full((height, width, 3), 60, np.uint8)
cv2.putText(
img,
str(i + 1),
(width // 2 - p * 10 * len(str(i + 1)), height // 2 + p * 10),
cv2.FONT_HERSHEY_DUPLEX,
p,
(255, 30, 30),
p * 2,
) # Blue number
return img
n_frames = 100 # Select number of frames (for testing).
width, height, fps = 192, 108, 10 # Select video resolution and framerate.
output_memory_file = io.BytesIO() # Create BytesIO "in memory file".
output = av.open(
output_memory_file, "w", format="mp4"
) # Open "in memory file" as MP4 video output
stream = output.add_stream(
"h264", str(fps)
) # Add H.264 video stream to the MP4 container, with framerate = fps.
stream.width = width # Set frame width
stream.height = height # Set frame height
stream.pix_fmt = "yuv420p" # NOTE: yuv444p doesn't work on mac. Select yuv444p pixel format (better quality than default yuv420p).
stream.options = {
"crf": "17"
} # Select low crf for high quality (the price is larger file size).
# Iterate the created images, encode and write to MP4 memory file.
for i in tqdm(range(n_frames)):
img = make_sample_image(
i
) # Create OpenCV image for testing (resolution 192x108, pixel format BGR).
frame = av.VideoFrame.from_ndarray(
img, format="bgr24"
) # # Convert image from NumPy Array to frame.
packet = stream.encode(frame) # Encode video frame
output.mux(packet) # "Mux" the encoded frame (add the encoded frame to MP4 file).
print("OUTPUT TYPE", type(output))
# Flush the encoder
packet = stream.encode(None)
output.mux(packet)
output.close()
output_memory_file.seek(0)
# video_bytes = output_memory_file.read() # Convert BytesIO to bytes array
st.video(output_memory_file)
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