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import datetime
from glob import glob
import cv2
import os
from tqdm import tqdm
def mp4_to_png(input_path: str, save_path: str, scale_factor: float) -> str:
""" Converts mp4 to pngs for each frame of the video.
Args: input_path is the path to the mp4 file, save_path is the directory to save the frames.
Returns: save_path, fps the number of frames per second.
"""
# get frames per second
fps = int(cv2.VideoCapture(input_path).get(cv2.CAP_PROP_FPS))
# run subprocess to convert mp4 to pngs
os.system(f"ffmpeg -i {input_path} -vf 'scale=iw*{scale_factor}:ih*{scale_factor}, fps={fps}' {save_path}/frame%08d.png")
return fps
def frame_to_timestamp(frame_number: int, fps: int):
# Calculate the timestamp in seconds
timestamp_seconds = frame_number / fps
# Convert the timestamp to hh:mm:ss
minutes, seconds = divmod(timestamp_seconds, 60)
hours, minutes = divmod(minutes, 60)
# print("Timestamp:", hours, ":", minutes, ":", seconds)
timestamp = f"{int(hours):02d}:{int(minutes):02d}:{seconds:06.3f}"
return timestamp
def vid_stitcher(frames_dir: str, output_path: str, fps: int = 30) -> str:
"""
Takes a list of frames as numpy arrays and writes them to a video file.
"""
# Get the list of frames
frame_list = sorted(glob(os.path.join(frames_dir, 'frame*.png')))
# Prepare the VideoWriter
frame = cv2.imread(frame_list[0])
height, width, _ = frame.shape
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
# Use multithreading to read frames faster
from concurrent.futures import ThreadPoolExecutor
with ThreadPoolExecutor() as executor:
frames = list(executor.map(cv2.imread, frame_list))
# Write frames to the video
with tqdm(total=len(frame_list), desc='Stitching frames') as pbar:
for frame in frames:
out.write(frame)
pbar.update(1)
return output_path |