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功能

实现对形似宝马标的黄黑四象限光学识别标定位

背景

大学同学遇到了这个场景，琢磨了下，以备不时之需。

代码

所用opencv版本：4.12

numpy==2.2.4
scikit_learn==1.6.1

import time
import cv2
import numpy as np
import math
from sklearn.cluster import KMeansdef calculate_tilt_angle(a, b):   # 确保 a >= bif a < b:a, b = b, a# 计算倾斜角度（弧度）theta_rad = math.acos(b / a)# 转为角度theta_deg = math.degrees(theta_rad)return theta_degdef compute_intersection(line1, line2):"""计算两条直线的交点"""(x1, y1), (x2, y2) = line1(x3, y3), (x4, y4) = line2# 计算分母den = (x1 - x2) * (y3 - y4) - (y1 - y2) * (x3 - x4)if den == 0:  # 直线平行return None# 计算交点坐标t = ((x1 - x3) * (y3 - y4) - (y1 - y3) * (x3 - x4)) / denu = -((x1 - x2) * (y1 - y3) - (y1 - y2) * (x1 - x3)) / den# 判断交点是否在线段内if 0 <= t <= 1 and 0 <= u <= 1:x = x1 + t * (x2 - x1)y = y1 + t * (y2 - y1)return (int(x), int(y))else:return Nonedef calculate_angle(line1, line2):"""计算两条线段之间的夹角（度数）"""# 提取线段端点坐标(x1, y1), (x2, y2) = line1(x3, y3), (x4, y4) = line2# 计算方向向量vec1 = (x2 - x1, y2 - y1)vec2 = (x4 - x3, y4 - y3)# 计算向量模长mod1 = np.sqrt(vec1[0]**2 + vec1[1]**2)mod2 = np.sqrt(vec2[0]**2 + vec2[1]**2)if mod1 == 0 or mod2 == 0:return None  # 无效向量（线段长度为0）# 计算点积和夹角余弦dot_product = vec1[0] * vec2[0] + vec1[1] * vec2[1]cos_theta = dot_product / (mod1 * mod2)cos_theta = np.clip(cos_theta, -1.0, 1.0)  # 处理浮点误差# 计算角度（0°~180°）angle = np.degrees(np.arccos(cos_theta))return angleif __name__ == '__main__':use_camera_flag = 1fps_list = []prev_time = 0if use_camera_flag:cap = cv2.VideoCapture(2)   # 自己修改为摄像头对应IDwhile True:if use_camera_flag:current_time = time.time()ret, image = cap.read()else:# 读取图像image = cv2.imread("label.jpeg")# image = cv2.imread("label0.png")# image = cv2.imread("label1.png")# image = cv2.imread("label2.jpeg")hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)# 定义黄色和黑色的HSV阈值范围（需根据实际图像调整）lower_yellow = np.array([20, 100, 100])upper_yellow = np.array([40, 255, 255])lower_black_h = np.array([35,10,0])upper_black_h = np.array([120,230,255])lower_black_l = np.array([0,0,0])upper_black_l = np.array([120,120,60])lower_white = np.array([0,0,125])upper_white = np.array([180,50,255])# 创建黄色和黑色的掩模mask_yellow = cv2.inRange(hsv, lower_yellow, upper_yellow)mask_black = cv2.bitwise_or(cv2.inRange(hsv, lower_black_h, upper_black_h), cv2.inRange(hsv, lower_black_l, upper_black_l))mask_white = cv2.inRange(hsv, lower_white, upper_white)# 合并黑黄区域的掩模并减去白色部分的掩模mask_combined = cv2.bitwise_or(mask_yellow, mask_black) & ~mask_white# 形态学操作（去噪+连接区域）kernel = np.ones((5,5), np.uint8)mask_processed = cv2.morphologyEx(mask_combined, cv2.MORPH_CLOSE, kernel)mask_yellow = cv2.morphologyEx(mask_yellow, cv2.MORPH_CLOSE, kernel)mask_black = cv2.morphologyEx(mask_black, cv2.MORPH_CLOSE, kernel)# 查找轮廓contours, _ = cv2.findContours(mask_processed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)image_height, image_width = image.shape[:2]# 筛选最大轮廓（假设标记是图像中最显著的物体）if contours:for cnt in contours:if len(cnt) >= 5:  # 至少需要5个点才能拟合椭圆ellipse = cv2.fitEllipse(cnt)(center, (width, height), angle) = ellipse# 过滤条件：长宽均大于阈值if width > image_width / 50 and height > image_height / 50:# 创建空白掩膜（与原图同尺寸）ellipse_mask = np.zeros_like(image[:, :, 0], dtype=np.uint8)# 绘制填充的椭圆（白色）cv2.ellipse(ellipse_mask, ellipse, 255, -1)# --- 计算椭圆区域内的黄色和黑色占比 ---# 提取椭圆区域内的黄色部分yellow_in_ellipse = cv2.bitwise_and(mask_yellow, mask_yellow, mask=ellipse_mask)yellow_area = cv2.countNonZero(yellow_in_ellipse)# 提取椭圆区域内的黑色部分black_in_ellipse = cv2.bitwise_and(mask_black, mask_black, mask=ellipse_mask)black_area = cv2.countNonZero(black_in_ellipse)# 计算椭圆总面积ellipse_area = cv2.countNonZero(ellipse_mask)# 计算比例（避免除零错误）if ellipse_area > 0:yellow_ratio = yellow_area / ellipse_areablack_ratio = black_area / ellipse_area# 过滤条件：黄黑区域各占至少30%if yellow_ratio >= 0.3 and black_ratio >= 0.3:# 使用椭圆掩膜提取原图label_in_image = cv2.bitwise_and(image, image, mask=ellipse_mask)gray = cv2.cvtColor(label_in_image, cv2.COLOR_BGR2GRAY)                      edges = cv2.Canny(gray, threshold1=50, threshold2=150)# 直线检测与交点计算lines = cv2.HoughLinesP(edges, rho=1,                  # 距离分辨率（像素单位）theta=np.pi/180,        # 角度分辨率（弧度单位）threshold=50,           # 检测阈值（累加器投票数阈值）minLineLength=((width + height) / 2) / 4,       # 最小线段长度（短于此长度的线段被丢弃）maxLineGap=10           # 允许线段间的最大间隔（小于此间隔的线段合并）)# 延长线段extended_lines = []if lines is not None:for line in lines:x1, y1, x2, y2 = line[0]# 延长线段，例如延长比例t=1.0t=1.0dx = x2 - x1dy = y2 - y1new_x1 = int(x1 - t * dx)new_y1 = int(y1 - t * dy)new_x2 = int(x2 + t * dx)new_y2 = int(y2 + t * dy)extended_lines.append([(new_x1, new_y1), (new_x2, new_y2)])# 计算交点intersections = []if extended_lines:for i in range(len(extended_lines)):for j in range(i+1, len(extended_lines)):line1 = extended_lines[i]line2 = extended_lines[j]# 计算夹角并过滤5°~175°以外的结果angle = calculate_angle(line1, line2)delta_angle = 85if angle is None or not (90 - delta_angle <= angle <= 90 + delta_angle):continuept = compute_intersection(line1, line2)if pt:# 检查交点是否在椭圆内# 椭圆参数来自ellipse变量# ellipse的格式是((h, k), (a, b), theta)(h, k), (a, b), theta_deg = ellipsetheta = np.deg2rad(theta_deg)x, y = pt# 转换到椭圆的标准坐标系x_trans = x - hy_trans = y - k# 旋转x_rot = x_trans * np.cos(theta) + y_trans * np.sin(theta)y_rot = -x_trans * np.sin(theta) + y_trans * np.cos(theta)# 判断是否在椭圆内if (x_rot**2)/(a**2) + (y_rot**2)/(b**2) <= 1:intersections.append(pt)# 聚类确定中心if intersections:X = np.array(intersections)kmeans = KMeans(n_clusters=1).fit(X)center_x, center_y = kmeans.cluster_centers_[0].astype(int)(h, k), (a, b), theta_deg = ellipse# (center_x, center_y) 即为目标点坐标cv2.circle(image, (center_x, center_y), int(((width + height) / 2) / 20 + 0.5), (0, 0, 255), int(((width + height) / 2) / 50 + 0.5))cv2.ellipse(image, ((float(center_x), float(center_y)), (a, b), theta_deg), (0, 255, 0), int(((width + height) / 2) / 50 + 0.5))  print("angle： {}, {}".format(calculate_tilt_angle(a, b), (a, b)))if use_camera_flag:fps = 1 / (current_time - prev_time)fps_list.append(fps)if len(fps_list) > 5:fps_list.pop(0)avg_fps = sum(fps_list) / len(fps_list)# 将帧率文本绘制到左上角cv2.putText(image,f"FPS: {fps:.2f}",  # 显示两位小数(10, 30),  # 左上角坐标 (x, y)cv2.FONT_HERSHEY_SIMPLEX,  # 字体1,  # 字体大小(255, 255, 255),  # 颜色 (BGR格式，白色)2,  # 字体厚度)# 显示结果cv2.namedWindow("Result", cv2.WINDOW_NORMAL)cv2.resizeWindow("Result", 500, 500)cv2.imshow("Result", image)if not use_camera_flag:cv2.waitKey(0)cv2.destroyAllWindows()cv2.imwrite("output.jpg", image)breakelse:prev_time = current_timeif cv2.waitKey(1) == ord('q'):cap.release()breakcv2.destroyAllWindows()

效果

部分图片取自c++识别象限标 —— 灯火~

具有一定的抗倾斜能力