98 lines
3.5 KiB
Python
98 lines
3.5 KiB
Python
# coding=utf-8
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import os
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import sys
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import cv2
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import numpy as np
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# 将项目根目录添加到 sys.path
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project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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if project_root not in sys.path:
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sys.path.append(project_root)
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from Apps.TeLaiDian import Kit
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def detect_price_areas_cv(image_path):
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"""
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专门用于详情页识别价格点击区域的测试函数
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"""
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img = Kit.read_image(image_path)
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if img is None:
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print("错误: 无法读取图片")
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return []
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h, w = img.shape[:2]
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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# 1. 转换为 HSV 空间，方便寻找橘红色价格
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hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
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# 橘红色的 HSV 范围 (大致)
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lower_orange = np.array([0, 150, 150])
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upper_orange = np.array([20, 255, 255])
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mask = cv2.inRange(hsv, lower_orange, upper_orange)
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# 2. 对掩码进行膨胀，连接数字
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kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (50, 20))
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dilated = cv2.dilate(mask, kernel)
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# 3. 寻找轮廓
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contours, _ = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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detected_areas = []
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for cnt in contours:
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x, y, cw, ch = cv2.boundingRect(cnt)
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# 橘红色价格通常在屏幕上半部，且宽度适中
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if 150 < y < h * 0.6 and cw > 100:
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# 我们找到了价格数字。用户说要点击的是这个区域。
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# 我们可以适当扩大这个区域，或者直接取其中心。
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padding = 20
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detected_areas.append([
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max(0, x - padding),
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max(0, y - padding),
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min(w, x + cw + padding),
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min(h, y + ch + padding)
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])
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# 按 X 轴排序，找到最左边的价格
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detected_areas.sort(key=lambda b: b[0])
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return detected_areas
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def run_test(image_path):
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print(f"分析图片: {image_path}")
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# 获取所有可能的区域
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bboxes = detect_price_areas_cv(image_path)
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print(f"检测到 {len(bboxes)} 个潜在区域:")
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for i, box in enumerate(bboxes):
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print(f" 区域 {i}: {box} (w={box[2]-box[0]}, h={box[3]-box[1]})")
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# 生成 _vl.jpg (仅框)
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vl_path = image_path.replace(".jpg", "_vl.jpg")
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img_vl = Kit.read_image(image_path)
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for i, box in enumerate(bboxes):
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cv2.rectangle(img_vl, (box[0], box[1]), (box[2], box[3]), (0, 255, 0), 2)
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cv2.putText(img_vl, str(i), (box[0], box[1]-5), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
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Kit.save_image(vl_path, img_vl)
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print(f"已生成标注图: {vl_path}")
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# 生成 _flag.jpg (框 + 中心点)
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flag_path = image_path.replace(".jpg", "_flag.jpg")
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img_flag = Kit.read_image(image_path)
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for i, box in enumerate(bboxes):
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cv2.rectangle(img_flag, (box[0], box[1]), (box[2], box[3]), (0, 255, 0), 2)
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center_x = (box[0] + box[2]) // 2
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center_y = (box[1] + box[3]) // 2
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cv2.circle(img_flag, (center_x, center_y), 10, (0, 0, 255), -1)
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cv2.putText(img_flag, f"P{i}", (box[0], box[1]-5), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2)
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Kit.save_image(flag_path, img_flag)
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print(f"已生成人工核对图: {flag_path}")
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if __name__ == "__main__":
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test_image = r"d:\dsWork\aiData\Output\Screenshot_20260114_075758.jpg"
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if os.path.exists(test_image):
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run_test(test_image)
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else:
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print(f"错误: 找不到测试图片 {test_image}")
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