Spaces:
Running
Running
File size: 2,670 Bytes
e0cedf5 e086001 e0cedf5 e086001 e0cedf5 e086001 e0cedf5 e086001 e0cedf5 e086001 e0cedf5 e086001 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 |
import cv2
import os
import sys
from tqdm import tqdm
from utils import resize_image_frame
def capture_slides_bg_modeling(
video_path,
output_dir_path,
type_bgsub,
history,
threshold,
MIN_PERCENT_THRESH,
MAX_PERCENT_THRESH,
):
print(f"Using {type_bgsub} for Background Modeling...")
print("---" * 10)
if type_bgsub == "GMG":
bg_sub = cv2.bgsegm.createBackgroundSubtractorGMG(
initializationFrames=history, decisionThreshold=threshold
)
elif type_bgsub == "KNN":
bg_sub = cv2.createBackgroundSubtractorKNN(
history=history, dist2Threshold=threshold, detectShadows=False
)
else:
raise ValueError("Please choose GMG or KNN as background subtraction method")
capture_frame = False
screenshots_count = 0
# Capture video frames.
cap = cv2.VideoCapture(video_path)
if not cap.isOpened():
print("Unable to open video file: ", video_path)
sys.exit()
num_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
prog_bar = tqdm(total=num_frames)
# Loop over subsequent frames.
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
# Create a copy of the original frame.
orig_frame = frame.copy()
# Resize the frame keeping aspect ratio.
frame = resize_image_frame(frame, resize_width=640)
# Apply each frame through the background subtractor.
fg_mask = bg_sub.apply(frame)
# Compute the percentage of the Foreground mask."
p_non_zero = (cv2.countNonZero(fg_mask) / (1.0 * fg_mask.size)) * 100
# %age of non-zero pixels < MAX_PERCENT_THRESH, implies motion has stopped.
# Therefore, capture the frame.
if p_non_zero < MAX_PERCENT_THRESH and not capture_frame:
capture_frame = True
screenshots_count += 1
png_filename = f"{screenshots_count:03}.jpg"
out_file_path = os.path.join(output_dir_path, png_filename)
cv2.imwrite(out_file_path, orig_frame, [cv2.IMWRITE_JPEG_QUALITY, 75])
prog_bar.set_postfix_str(f"Total Screenshots: {screenshots_count}")
# p_non_zero >= MIN_PERCENT_THRESH, indicates motion/animations.
# Hence wait till the motion across subsequent frames has settled down.
elif capture_frame and p_non_zero >= MIN_PERCENT_THRESH:
capture_frame = False
prog_bar.update(1)
# Release progress bar and video capture object.
prog_bar.close()
cap.release()
|