128 lines
4.3 KiB
Python
128 lines
4.3 KiB
Python
"""
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验证码图片匹配脚本
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在 1.jpg 中找到 2.png、3.png、4.png 的位置
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"""
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import cv2
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import numpy as np
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import os
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def find_template_in_image(main_img_path, template_path, threshold=0.5):
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"""
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在主图中查找模板图片的位置（原始尺寸 + 多尺度）
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支持 PNG alpha 通道作为模板轮廓
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返回置信度最高的匹配结果
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"""
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main_img = cv2.imread(main_img_path)
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template = cv2.imread(template_path, cv2.IMREAD_UNCHANGED)
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if main_img is None:
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print(f"无法读取主图: {main_img_path}")
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return []
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if template is None:
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print(f"无法读取模板: {template_path}")
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return []
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h, w = template.shape[:2]
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main_gray = cv2.cvtColor(main_img, cv2.COLOR_BGR2GRAY)
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# 如果模板有 alpha 通道且 RGB 全黑，用 alpha 通道作为灰度图
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if template.shape[2] == 4:
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alpha = template[:, :, 3]
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rgb_sum = template[:, :, :3].sum()
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if rgb_sum == 0:
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tmpl_gray = alpha # 用 alpha 通道
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else:
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tmpl_gray = cv2.cvtColor(template[:, :, :3], cv2.COLOR_BGR2GRAY)
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else:
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tmpl_gray = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)
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best = None
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# 先尝试原始尺寸
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scales = [1.0] + list(np.linspace(0.6, 1.4, 17))
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for scale in scales:
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rw, rh = int(w * scale), int(h * scale)
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if rh < 5 or rw < 5:
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continue
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if rh > main_gray.shape[0] or rw > main_gray.shape[1]:
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continue
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resized_tmpl = cv2.resize(tmpl_gray, (rw, rh))
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res = cv2.matchTemplate(main_gray, resized_tmpl, cv2.TM_CCOEFF_NORMED)
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_, max_val, _, max_loc = cv2.minMaxLoc(res)
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if best is None or max_val > best["confidence"]:
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best = {
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"x": max_loc[0],
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"y": max_loc[1],
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"w": rw,
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"h": rh,
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"confidence": max_val,
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"scale": scale,
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"center_x": max_loc[0] + rw // 2,
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"center_y": max_loc[1] + rh // 2,
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}
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if best is None or best["confidence"] < threshold:
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return []
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return [best]
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def main():
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base_dir = os.path.join(os.path.dirname(__file__), "images")
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main_img_path = os.path.join(base_dir, "1.jpg")
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templates = ["2.png", "3.png", "4.png"]
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print(f"主图: {main_img_path}\n")
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all_results = {}
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for tmpl_name in templates:
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tmpl_path = os.path.join(base_dir, tmpl_name)
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matches = find_template_in_image(main_img_path, tmpl_path, threshold=0.5)
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all_results[tmpl_name] = matches
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if matches:
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m = matches[0]
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print(f"[{tmpl_name}] 找到匹配:")
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print(f" 位置: ({m['x']}, {m['y']})")
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print(f" 中心点: ({m['center_x']}, {m['center_y']})")
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print(f" 尺寸: {m['w']}x{m['h']}")
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print(f" 置信度: {m['confidence']:.4f}")
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print(f" 缩放比例: {m['scale']:.2f}")
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else:
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print(f"[{tmpl_name}] 未找到匹配（置信度不足）")
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print()
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# 可视化结果，保存标注图
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main_img = cv2.imread(main_img_path)
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colors = [(0, 0, 255), (0, 255, 0), (255, 0, 0)] # 红、绿、蓝
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for i, tmpl_name in enumerate(templates):
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matches = all_results.get(tmpl_name, [])
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for m in matches:
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x, y, w, h = m["x"], m["y"], m["w"], m["h"]
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color = colors[i % len(colors)]
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cv2.rectangle(main_img, (x, y), (x + w, y + h), color, 2)
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cv2.putText(main_img, tmpl_name, (x, y - 5),
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cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)
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# 画中心点
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cx, cy = m["center_x"], m["center_y"]
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cv2.circle(main_img, (cx, cy), 5, color, -1)
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output_path = os.path.join(base_dir, "result.jpg")
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cv2.imwrite(output_path, main_img)
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print(f"标注结果已保存到: {output_path}")
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# 打印点击坐标汇总（用于自动化点击）
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print("\n=== 点击坐标汇总 ===")
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for tmpl_name in templates:
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matches = all_results.get(tmpl_name, [])
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if matches:
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m = matches[0]
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print(f"{tmpl_name}: 点击 ({m['center_x']}, {m['center_y']})")
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else:
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print(f"{tmpl_name}: 未找到")
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if __name__ == "__main__":
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main()
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