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105 lines
3.1 KiB
Python
Executable File
105 lines
3.1 KiB
Python
Executable File
#!/usr/bin/env python3
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import cv2
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import numpy as np
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import sys
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import os
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def stitch_video(video_path, output_path):
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if not os.path.exists(video_path):
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return
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cap = cv2.VideoCapture(video_path)
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if not cap.isOpened():
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print("❌ 无法打开视频")
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return
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frames = []
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ret, prev_frame = cap.read()
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if not ret: return
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frames.append(prev_frame)
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anchor_frame = prev_frame.copy()
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# ==========================
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# 核心参数 (手动滚动优化)
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# ==========================
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MIN_SCROLL = 2
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MATCH_CONFIDENCE = 0.5
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# 忽略上下边缘 (防止浏览器地址栏/状态栏干扰)
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IGNORE_Y_TOP = 0.15
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IGNORE_Y_BOTTOM = 0.15
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IGNORE_X = 0.15
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h, w, _ = anchor_frame.shape
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# 有效特征区
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x1 = int(w * IGNORE_X)
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x2 = int(w * (1 - IGNORE_X))
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y1 = int(h * IGNORE_Y_TOP)
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template_h = int(h * 0.2)
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print(f"⚡ 正在分析 (梯度匹配模式)...")
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last_shift = 0
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SEARCH_WINDOW = 50
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while True:
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ret, curr_frame = cap.read()
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if not ret: break
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# 1. 梯度处理 (解决白底黑字问题)
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curr_gray = cv2.cvtColor(curr_frame, cv2.COLOR_BGR2GRAY)
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anchor_gray = cv2.cvtColor(anchor_frame, cv2.COLOR_BGR2GRAY)
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curr_grad = cv2.Sobel(curr_gray, cv2.CV_8U, 0, 1, ksize=3)
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anchor_grad = cv2.Sobel(anchor_gray, cv2.CV_8U, 0, 1, ksize=3)
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# 2. 提取模板
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template = curr_grad[y1 : y1 + template_h, x1:x2]
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roi = anchor_grad[y1:, x1:x2]
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# 3. 匹配
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res = cv2.matchTemplate(roi, template, cv2.TM_CCOEFF_NORMED)
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# 4. 惯性约束
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if last_shift > 0:
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mask = np.zeros_like(res)
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target_y = last_shift
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y_min = max(0, target_y - SEARCH_WINDOW)
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y_max = min(res.shape[0], target_y + SEARCH_WINDOW)
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mask[y_min:y_max, :] = 1
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res = np.multiply(res, mask)
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min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
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shift = max_loc[1]
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# 5. 判定
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if max_val > MATCH_CONFIDENCE and shift > MIN_SCROLL and shift < (roi.shape[0] - 5):
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new_content_start_y = h - shift
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if new_content_start_y < h:
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new_part = curr_frame[new_content_start_y:, :, :]
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frames.append(new_part)
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anchor_frame = curr_frame.copy()
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if last_shift == 0: last_shift = shift
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else: last_shift = int(last_shift * 0.6 + shift * 0.4)
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cap.release()
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if len(frames) > 1:
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try:
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full_image = np.vstack(frames)
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cv2.imwrite(output_path, full_image, [cv2.IMWRITE_PNG_COMPRESSION, 3])
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print(f"🎉 处理完成")
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except Exception as e:
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print(f"❌ 保存失败: {e}")
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else:
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print("⚠️ 未检测到滚动，保存第一帧")
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cv2.imwrite(output_path, frames[0])
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if __name__ == "__main__":
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if len(sys.argv) < 3:
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print("Usage: python stitch.py <input_video> <output_image>")
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else:
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stitch_video(sys.argv[1], sys.argv[2]) |