| from collections import deque |
| import cv2 as cv |
| import numpy as np |
|
|
|
|
| def estimate_focus_measure(frame): |
| gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY) |
| laplacian = cv.Laplacian(gray, cv.CV_64F) |
| return laplacian.var() |
|
|
|
|
| def update_threshold(blur_history): |
| return np.percentile(blur_history, 50) |
|
|
|
|
| def select_most_focused(frame_history, threshold, select_n=5): |
| sorted_frames = sorted(frame_history, key=lambda x: x[1], reverse=True) |
| |
| selected = [frame for frame, fm in sorted_frames[:select_n] if fm > threshold] |
| return selected |
|
|
|
|
| def filter_frames(cap): |
| fps = int(cap.get(cv.CAP_PROP_FPS)) |
| frame_history = deque(maxlen=fps) |
| blur_history = [] |
| most_focused_frames = [] |
|
|
| if cap.isOpened(): |
| ret, frame = cap.read() |
| counter = 1 |
| second = 0 |
|
|
| while ret: |
| fm = estimate_focus_measure(frame) |
| frame_history.append([frame, fm]) |
| blur_history.append(fm) |
|
|
| if counter >= fps: |
| second += 1 |
| threshold = update_threshold(blur_history) |
| if counter % fps == 0: |
| most_focused_frames += select_most_focused(frame_history, threshold) |
| frame_history.clear() |
| ret, frame = cap.read() |
| counter += 1 |
|
|
| if frame_history: |
| threshold = update_threshold(blur_history) |
| most_focused_frames += select_most_focused(frame_history, threshold) |
|
|
| cap.release() |
| return most_focused_frames |
