424 lines
14 KiB
Python
Executable File
424 lines
14 KiB
Python
Executable File
import cv2
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import numpy as np
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def detect_arrow_direction(image, observe=False, delay=500):
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"""
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从图像中提取绿色箭头并判断其指向方向（左或右）
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参数:
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image: 输入图像，可以是文件路径或者已加载的图像数组
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observe: 是否输出中间状态信息和可视化结果，默认为False
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delay: 展示每个步骤的等待时间(毫秒)，默认为500ms
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返回:
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direction: 字符串，"left"表示左箭头，"right"表示右箭头，"unknown"表示无法确定
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"""
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# 如果输入是字符串（文件路径），则加载图像
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if isinstance(image, str):
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img = cv2.imread(image)
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else:
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img = image.copy()
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if img is None:
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print("无法加载图像")
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return "unknown"
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if observe:
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print("步骤1: 原始图像已加载")
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cv2.imshow("原始图像", img)
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cv2.waitKey(delay)
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# 转换到HSV颜色空间以便更容易提取绿色
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hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
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if observe:
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print("步骤2: 转换到HSV颜色空间")
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cv2.imshow("HSV图像", hsv)
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cv2.waitKey(delay)
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# 绿色的HSV范围
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# 调整这些值以匹配图像中绿色的具体色调··
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lower_green = np.array([40, 50, 50])
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upper_green = np.array([80, 255, 255])
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# 创建绿色的掩码
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mask = cv2.inRange(hsv, lower_green, upper_green)
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if observe:
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print("步骤3: 创建绿色掩码")
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cv2.imshow("绿色掩码", mask)
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cv2.waitKey(delay)
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# 应用掩码，只保留绿色部分
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green_only = cv2.bitwise_and(img, img, mask=mask)
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if observe:
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print("步骤4: 提取绿色部分")
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cv2.imshow("只保留绿色", green_only)
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cv2.waitKey(delay)
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# 将掩码转为灰度图
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gray = mask.copy()
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# 查找轮廓
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contours, _ = cv2.findContours(gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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# 如果没有找到轮廓，返回未知
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if not contours:
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if observe:
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print("未找到轮廓")
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return "unknown"
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if observe:
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print(f"步骤5: 找到 {len(contours)} 个轮廓")
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contour_img = img.copy()
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cv2.drawContours(contour_img, contours, -1, (0, 255, 0), 2)
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cv2.imshow("所有轮廓", contour_img)
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cv2.waitKey(delay)
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# 找到最大的轮廓（假设是箭头）
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max_contour = max(contours, key=cv2.contourArea)
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if observe:
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print("步骤6: 提取最大轮廓")
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max_contour_img = img.copy()
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cv2.drawContours(max_contour_img, [max_contour], -1, (0, 0, 255), 2)
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cv2.imshow("最大轮廓", max_contour_img)
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cv2.waitKey(delay)
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# 使用多边形近似轮廓
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epsilon = 0.02 * cv2.arcLength(max_contour, True)
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approx = cv2.approxPolyDP(max_contour, epsilon, True)
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if observe:
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print(f"步骤7: 多边形近似，顶点数: {len(approx)}")
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approx_img = img.copy()
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cv2.drawContours(approx_img, [approx], -1, (255, 0, 0), 2)
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cv2.imshow("多边形近似", approx_img)
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cv2.waitKey(delay)
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# 计算凸包
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hull = cv2.convexHull(max_contour)
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if observe:
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print("步骤8: 计算凸包")
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hull_img = img.copy()
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cv2.drawContours(hull_img, [hull], -1, (0, 255, 0), 2)
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cv2.imshow("凸包", hull_img)
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cv2.waitKey(delay)
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# 计算凸缺陷
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if len(max_contour) > 3:
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defects = cv2.convexityDefects(max_contour, cv2.convexHull(max_contour, returnPoints=False))
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else:
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if observe:
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print("轮廓点太少，无法准确判断")
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return "unknown" # 轮廓点太少，无法准确判断
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if defects is None:
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if observe:
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print("未找到凸缺陷")
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return "unknown"
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if observe:
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print(f"步骤9: 计算凸缺陷，找到 {defects.shape[0]} 个缺陷点")
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defect_img = img.copy()
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for i in range(defects.shape[0]):
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s, e, f, d = defects[i, 0]
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start = tuple(max_contour[s][0])
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end = tuple(max_contour[e][0])
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far = tuple(max_contour[f][0])
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cv2.circle(defect_img, far, 5, (0, 0, 255), -1)
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cv2.imshow("凸缺陷", defect_img)
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cv2.waitKey(delay)
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# 获取轮廓的最小外接矩形
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rect = cv2.minAreaRect(max_contour)
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box = cv2.boxPoints(rect)
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box = np.int0(box)
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if observe:
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print("步骤10: 获取最小外接矩形")
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rect_img = img.copy()
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cv2.drawContours(rect_img, [box], 0, (255, 0, 0), 2)
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cv2.imshow("最小外接矩形", rect_img)
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cv2.waitKey(delay)
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# 获取中心点和角度
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center = rect[0]
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angle = rect[2]
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if observe:
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print(f"矩形中心: {center}, 角度: {angle}")
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# 计算轮廓的矩，用于确定箭头方向
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M = cv2.moments(max_contour)
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# 避免除以零
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if M["m00"] != 0:
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cx = int(M["m10"] / M["m00"])
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cy = int(M["m01"] / M["m00"])
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else:
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cx, cy = int(center[0]), int(center[1])
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if observe:
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print(f"步骤11: 计算质心 - 坐标: ({cx}, {cy})")
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center_img = img.copy()
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cv2.circle(center_img, (cx, cy), 5, (255, 0, 0), -1)
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cv2.imshow("质心", center_img)
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cv2.waitKey(delay)
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# 改进的箭头尖端检测算法
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# 1. 找到所有凸缺陷点
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defect_points = []
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for i in range(defects.shape[0]):
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s, e, f, d = defects[i, 0]
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start = tuple(max_contour[s][0])
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end = tuple(max_contour[e][0])
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far = tuple(max_contour[f][0])
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# 计算缺陷点到中心的距离
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dist = np.sqrt((far[0] - cx) ** 2 + (far[1] - cy) ** 2)
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if observe:
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print(f"缺陷点 {i}: 位置 {far}, 到中心距离: {dist:.2f}")
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# 记录凸缺陷点及其距离
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defect_points.append({
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'point': far,
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'distance': dist,
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'start': start,
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'end': end
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})
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# 没有缺陷点，使用矩形判断
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if not defect_points:
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# 判断逻辑将在后面处理
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arrow_tip = None
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else:
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# 2. 按距离对缺陷点排序
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defect_points.sort(key=lambda x: x['distance'], reverse=True)
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# 3. 获取最远的几个缺陷点（可能的尖端候选）
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top_n = min(3, len(defect_points))
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candidates = defect_points[:top_n]
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# 4. 分析这些点的位置分布
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left_candidates = [p for p in candidates if p['point'][0] < cx]
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right_candidates = [p for p in candidates if p['point'][0] >= cx]
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# 5. 根据候选点的分布判断箭头朝向
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if len(left_candidates) > len(right_candidates):
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# 左侧候选点更多，箭头可能指向左边
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arrow_tip = max(left_candidates, key=lambda x: x['distance'])['point']
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arrow_direction = "left"
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elif len(right_candidates) > len(left_candidates):
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# 右侧候选点更多，箭头可能指向右边
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arrow_tip = max(right_candidates, key=lambda x: x['distance'])['point']
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arrow_direction = "right"
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else:
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# 候选点分布均衡，使用最远的点
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arrow_tip = candidates[0]['point']
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# 根据最远点的位置判断
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if arrow_tip[0] < cx:
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arrow_direction = "left"
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else:
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arrow_direction = "right"
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if observe and arrow_tip:
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print(f"步骤12: 找到可能的箭头尖端 - 位置: {arrow_tip}")
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tip_img = img.copy()
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cv2.circle(tip_img, arrow_tip, 8, (0, 255, 255), -1)
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cv2.line(tip_img, (cx, cy), arrow_tip, (255, 0, 255), 2)
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cv2.imshow("箭头尖端", tip_img)
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cv2.waitKey(delay)
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# 使用多种特征综合判断箭头方向
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if arrow_tip is None:
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# 如果没有找到明显的箭头尖端，使用最小外接矩形来判断
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width = rect[1][0]
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height = rect[1][1]
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if observe:
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print(f"未找到明显尖端，使用矩形判断 - 宽: {width}, 高: {height}")
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# 矩形的方向向量
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if width > height: # 水平箭头
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# 考虑角度的定义
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if angle > 45:
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arrow_direction = "left"
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else:
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arrow_direction = "right"
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else: # 垂直箭头，不在我们的考虑范围内
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if observe:
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print("垂直箭头，不在考虑范围")
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return "unknown"
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else:
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# 已经在上面的代码中确定了方向
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pass
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# 附加检查：计算轮廓边界上的点，确认箭头的形状特征
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x, y, w, h = cv2.boundingRect(max_contour)
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aspect_ratio = float(w) / h
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# 计算轮廓周长和面积
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perimeter = cv2.arcLength(max_contour, True)
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area = cv2.contourArea(max_contour)
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# 计算轮廓的形状复杂度
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complexity = perimeter / (4 * np.sqrt(area))
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if observe:
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print(f"轮廓分析 - 宽高比: {aspect_ratio:.2f}, 复杂度: {complexity:.2f}")
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# 箭头形状特征检查
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# 一般来说，箭头的复杂度会在一定范围内
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if 1.1 < complexity < 3.0:
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# 根据形状特征进一步确认方向判断
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# 使用质心位置相对于轮廓边界的偏移
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relative_cx = (cx - x) / w
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if observe:
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print(f"质心相对位置: {relative_cx:.2f}")
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# 如果质心偏向左侧，箭头可能指向右侧
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# 如果质心偏向右侧，箭头可能指向左侧
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# 这是一个启发式规则，可能需要根据具体箭头形状调整
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if relative_cx < 0.4:
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# 质心在左侧，可能是右箭头
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if arrow_direction == "left":
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# 当前方法判断为左，但质心位置特征表明可能是右
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# 增加额外的检查
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if aspect_ratio > 1.5: # 宽大于高
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arrow_direction = "right"
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elif relative_cx > 0.6:
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# 质心在右侧，可能是左箭头
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if arrow_direction == "right":
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# 当前方法判断为右，但质心位置特征表明可能是左
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if aspect_ratio > 1.5: # 宽大于高
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arrow_direction = "left"
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if observe:
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print(f"基于综合特征判断: {arrow_direction}箭头")
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return arrow_direction
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def visualize_arrow_detection(image, save_path=None, observe=False, delay=500):
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"""
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可视化箭头检测过程，显示中间结果
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参数:
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image: 输入图像，可以是文件路径或者已加载的图像数组
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save_path: 保存结果图像的路径（可选）
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observe: 是否输出中间状态信息和可视化结果，默认为False
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delay: 展示每个步骤的等待时间(毫秒)，默认为500ms
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"""
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# 如果输入是字符串（文件路径），则加载图像
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if isinstance(image, str):
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img = cv2.imread(image)
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else:
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img = image.copy()
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if img is None:
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print("无法加载图像")
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return
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if observe:
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print("\n开始可视化箭头检测过程")
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# 转换到HSV颜色空间
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hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
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# 绿色的HSV范围
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lower_green = np.array([40, 50, 50])
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upper_green = np.array([80, 255, 255])
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# 创建绿色的掩码
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mask = cv2.inRange(hsv, lower_green, upper_green)
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# 应用掩码，只保留绿色部分
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green_only = cv2.bitwise_and(img, img, mask=mask)
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# 查找轮廓
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contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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# 创建输出图像
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output = img.copy()
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# 如果找到轮廓，绘制最大轮廓
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if contours:
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max_contour = max(contours, key=cv2.contourArea)
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cv2.drawContours(output, [max_contour], -1, (0, 0, 255), 2)
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# 获取轮廓的最小外接矩形
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rect = cv2.minAreaRect(max_contour)
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box = cv2.boxPoints(rect)
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box = np.int0(box)
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cv2.drawContours(output, [box], 0, (255, 0, 0), 2)
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# 计算轮廓的矩
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M = cv2.moments(max_contour)
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# 避免除以零
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if M["m00"] != 0:
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cx = int(M["m10"] / M["m00"])
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cy = int(M["m01"] / M["m00"])
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# 绘制中心点
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cv2.circle(output, (cx, cy), 5, (255, 0, 0), -1)
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# 绘制凸包
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hull = cv2.convexHull(max_contour)
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cv2.drawContours(output, [hull], 0, (0, 255, 0), 2)
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# 绘制凸缺陷
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if len(max_contour) > 3:
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defects = cv2.convexityDefects(max_contour, cv2.convexHull(max_contour, returnPoints=False))
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if defects is not None:
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for i in range(defects.shape[0]):
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s, e, f, d = defects[i, 0]
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start = tuple(max_contour[s][0])
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end = tuple(max_contour[e][0])
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far = tuple(max_contour[f][0])
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cv2.circle(output, far, 5, (0, 0, 255), -1) # 在凸缺陷点绘制红色圆点
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# 获取箭头方向
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direction = detect_arrow_direction(img, observe=observe, delay=delay)
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# 在图像上添加方向文本
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cv2.putText(output, f"Direction: {direction}", (10, 30),
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cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
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# 如果提供了保存路径，保存结果图像
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if save_path:
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cv2.imwrite(save_path, output)
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if observe:
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print(f"结果已保存到: {save_path}")
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# 创建一个包含所有图像的窗口
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result = np.hstack((img, green_only, output))
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# 调整大小以便查看
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scale_percent = 50 # 缩放到原来的50%
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width = int(result.shape[1] * scale_percent / 100)
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height = int(result.shape[0] * scale_percent / 100)
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dim = (width, height)
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resized = cv2.resize(result, dim, interpolation=cv2.INTER_AREA)
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# 显示结果
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cv2.imshow('Arrow Detection Process', resized)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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# 用法示例
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if __name__ == "__main__":
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# 替换为实际图像路径
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image_path = "path/to/arrow/image.png"
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# 检测箭头方向，使用较长的延迟时间（1500毫秒）
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direction = detect_arrow_direction(image_path, observe=True, delay=1500)
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print(f"检测到的箭头方向: {direction}")
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# 可视化检测过程，使用较长的延迟时间
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visualize_arrow_detection(image_path, observe=True, delay=1500)
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