|
|
|
import argparse
|
|
import os
|
|
import sys
|
|
import cv2
|
|
import numpy as np
|
|
import json
|
|
from datetime import datetime
|
|
|
|
def compute_colorfulness(frame):
|
|
|
|
b, g, r = cv2.split(frame.astype('float'))
|
|
|
|
rg = r - g
|
|
yb = 0.5 * (r + g) - b
|
|
|
|
std_rg, std_yb = np.std(rg), np.std(yb)
|
|
mean_rg, mean_yb = np.mean(rg), np.mean(yb)
|
|
|
|
return np.sqrt(std_rg**2 + std_yb**2) + 0.3 * np.sqrt(mean_rg**2 + mean_yb**2)
|
|
|
|
def sample_frames(video_path, max_frames=30):
|
|
cap = cv2.VideoCapture(video_path)
|
|
if not cap.isOpened():
|
|
return []
|
|
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
|
|
step = max(1, total_frames // max_frames)
|
|
frames = []
|
|
for idx in range(0, total_frames, step):
|
|
cap.set(cv2.CAP_PROP_POS_FRAMES, idx)
|
|
ret, frame = cap.read()
|
|
if not ret:
|
|
break
|
|
|
|
h, w = frame.shape[:2]
|
|
new_w = 256
|
|
new_h = int(h * (256 / w))
|
|
frame = cv2.resize(frame, (new_w, new_h))
|
|
frames.append(frame)
|
|
cap.release()
|
|
return frames
|
|
|
|
def main():
|
|
parser = argparse.ArgumentParser(description="Evaluate video colorfulness (detect non-BW video).")
|
|
parser.add_argument("--output", type=str, required=True,
|
|
help="Path to the input video file.")
|
|
parser.add_argument("--threshold", type=float, default=10.0,
|
|
help="Colorfulness threshold for pass/fail (default: 10.0).")
|
|
parser.add_argument("--result", help="Path to append the jsonl result.")
|
|
args = parser.parse_args()
|
|
|
|
input_path = args.output
|
|
process_ok = False
|
|
results_ok = False
|
|
comments = []
|
|
|
|
|
|
if not os.path.exists(input_path):
|
|
comments.append(f"Input file not found: {input_path}")
|
|
elif os.path.getsize(input_path) == 0:
|
|
comments.append(f"Input file is empty: {input_path}")
|
|
else:
|
|
ext = os.path.splitext(input_path)[1].lower()
|
|
if ext not in ['.mp4', '.avi', '.mov', '.mkv']:
|
|
comments.append(f"Unsupported file format: {ext}")
|
|
else:
|
|
process_ok = True
|
|
|
|
if process_ok:
|
|
frames = sample_frames(input_path)
|
|
if not frames:
|
|
comments.append("Failed to read any frames from video.")
|
|
else:
|
|
scores = [compute_colorfulness(f) for f in frames]
|
|
avg_score = float(np.mean(scores))
|
|
comments.append(f"Average colorfulness: {avg_score:.2f}")
|
|
results_ok = avg_score > args.threshold
|
|
comments.append("Pass" if results_ok else "Fail")
|
|
|
|
|
|
print("=== Evaluation ===")
|
|
print("Process OK: ", process_ok)
|
|
if process_ok:
|
|
print("Result OK: ", results_ok)
|
|
for c in comments:
|
|
print(c)
|
|
|
|
|
|
if args.result:
|
|
record = {
|
|
"Process": process_ok,
|
|
"Result": results_ok,
|
|
"TimePoint": datetime.now().isoformat(),
|
|
"comments": "; ".join(comments)
|
|
}
|
|
os.makedirs(os.path.dirname(args.result), exist_ok=True)
|
|
with open(args.result, 'a', encoding='utf-8') as f:
|
|
json_line = json.dumps(record, default=str, ensure_ascii=False)
|
|
f.write(json_line + "\n")
|
|
|
|
if __name__ == "__main__":
|
|
main() |