360 lines
16 KiB
Python
360 lines
16 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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视觉检测模块
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提供靶心检测、距离估算、图像保存等功能
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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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import math
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from maix import image
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import config
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from logger_manager import logger_manager
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def detect_circle_v3(frame, laser_point=None):
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"""检测图像中的靶心（优先清晰轮廓，其次黄色区域）- 返回椭圆参数版本
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增加红色圆圈检测，验证黄色圆圈是否为真正的靶心
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如果提供 laser_point，会选择最接近激光点的目标
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Args:
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frame: 图像帧
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laser_point: 激光点坐标 (x, y)，用于多目标场景下的目标选择
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Returns:
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(result_img, best_center, best_radius, method, best_radius1, ellipse_params)
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"""
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img_cv = image.image2cv(frame, False, False)
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best_center = best_radius = best_radius1 = method = None
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ellipse_params = None
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# HSV 黄色掩码检测（模糊靶心）
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hsv = cv2.cvtColor(img_cv, cv2.COLOR_RGB2HSV)
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h, s, v = cv2.split(hsv)
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# 调整饱和度策略：稍微增强，不要过度
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s = np.clip(s * 1.1, 0, 255).astype(np.uint8)
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hsv = cv2.merge((h, s, v))
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# 放宽 HSV 阈值范围（针对模糊图像的关键调整）
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lower_yellow = np.array([7, 80, 0]) # 饱和度下限降低，捕捉淡黄色
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upper_yellow = np.array([32, 255, 255]) # 亮度上限拉满
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mask_yellow = cv2.inRange(hsv, lower_yellow, upper_yellow)
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# 调整形态学操作
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kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
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mask_yellow = cv2.morphologyEx(mask_yellow, cv2.MORPH_CLOSE, kernel)
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contours_yellow, _ = cv2.findContours(mask_yellow, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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# 存储所有有效的黄色-红色组合
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valid_targets = []
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if contours_yellow:
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for cnt_yellow in contours_yellow:
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area = cv2.contourArea(cnt_yellow)
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perimeter = cv2.arcLength(cnt_yellow, True)
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# 计算圆度
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if perimeter > 0:
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circularity = (4 * np.pi * area) / (perimeter * perimeter)
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else:
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circularity = 0
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logger = logger_manager.logger
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if area > 50 and circularity > 0.7:
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if logger:
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logger.info(f"[target] -> 面积:{area}, 圆度:{circularity:.2f}")
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# 尝试拟合椭圆
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yellow_center = None
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yellow_radius = None
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yellow_ellipse = None
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if len(cnt_yellow) >= 5:
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(x, y), (width, height), angle = cv2.fitEllipse(cnt_yellow)
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yellow_ellipse = ((x, y), (width, height), angle)
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axes_minor = min(width, height)
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radius = axes_minor / 2
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yellow_center = (int(x), int(y))
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yellow_radius = int(radius)
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else:
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(x, y), radius = cv2.minEnclosingCircle(cnt_yellow)
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yellow_center = (int(x), int(y))
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yellow_radius = int(radius)
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yellow_ellipse = None
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# 如果检测到黄色圆圈，再检测红色圆圈进行验证
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if yellow_center and yellow_radius:
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# HSV 红色掩码检测（红色在HSV中跨越0度，需要两个范围）
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# 红色范围1: 0-10度（接近0度的红色）
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lower_red1 = np.array([0, 80, 0])
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upper_red1 = np.array([10, 255, 255])
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mask_red1 = cv2.inRange(hsv, lower_red1, upper_red1)
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# 红色范围2: 170-180度（接近180度的红色）
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lower_red2 = np.array([170, 80, 0])
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upper_red2 = np.array([180, 255, 255])
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mask_red2 = cv2.inRange(hsv, lower_red2, upper_red2)
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# 合并两个红色掩码
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mask_red = cv2.bitwise_or(mask_red1, mask_red2)
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# 形态学操作
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kernel_red = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
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mask_red = cv2.morphologyEx(mask_red, cv2.MORPH_CLOSE, kernel_red)
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contours_red, _ = cv2.findContours(mask_red, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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found_valid_red = False
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if contours_red:
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# 找到所有符合条件的红色圆圈
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for cnt_red in contours_red:
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area_red = cv2.contourArea(cnt_red)
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perimeter_red = cv2.arcLength(cnt_red, True)
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if perimeter_red > 0:
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circularity_red = (4 * np.pi * area_red) / (perimeter_red * perimeter_red)
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else:
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circularity_red = 0
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# 红色圆圈也应该有一定的圆度
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if area_red > 50 and circularity_red > 0.6:
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# 计算红色圆圈的中心和半径
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if len(cnt_red) >= 5:
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(x_red, y_red), (w_red, h_red), angle_red = cv2.fitEllipse(cnt_red)
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radius_red = min(w_red, h_red) / 2
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red_center = (int(x_red), int(y_red))
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red_radius = int(radius_red)
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else:
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(x_red, y_red), radius_red = cv2.minEnclosingCircle(cnt_red)
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red_center = (int(x_red), int(y_red))
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red_radius = int(radius_red)
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# 计算黄色和红色圆心的距离
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if red_center:
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dx = yellow_center[0] - red_center[0]
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dy = yellow_center[1] - red_center[1]
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distance = np.sqrt(dx*dx + dy*dy)
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# 圆心距离阈值：应该小于黄色半径的某个倍数（比如1.5倍）
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max_distance = yellow_radius * 1.5
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# 红色圆圈应该比黄色圆圈大（外圈）
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if distance < max_distance and red_radius > yellow_radius * 0.8:
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found_valid_red = True
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logger = logger_manager.logger
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if logger:
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logger.info(f"[target] -> 找到匹配的红圈: 黄心({yellow_center}), 红心({red_center}), 距离:{distance:.1f}, 黄半径:{yellow_radius}, 红半径:{red_radius}")
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# 记录这个有效目标
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valid_targets.append({
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'center': yellow_center,
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'radius': yellow_radius,
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'ellipse': yellow_ellipse,
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'area': area
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})
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break
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if not found_valid_red:
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logger = logger_manager.logger
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if logger:
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logger.debug("Debug -> 未找到匹配的红色圆圈，可能是误识别")
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# 从所有有效目标中选择最佳目标
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if valid_targets:
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if laser_point:
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# 如果有激光点，选择最接近激光点的目标
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best_target = None
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min_distance = float('inf')
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for target in valid_targets:
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dx = target['center'][0] - laser_point[0]
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dy = target['center'][1] - laser_point[1]
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distance = np.sqrt(dx*dx + dy*dy)
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if distance < min_distance:
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min_distance = distance
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best_target = target
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if best_target:
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best_center = best_target['center']
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best_radius = best_target['radius']
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ellipse_params = best_target['ellipse']
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method = "v3_ellipse_red_validated_laser_selected"
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best_radius1 = best_radius * 5
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else:
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# 如果没有激光点，选择面积最大的目标
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best_target = max(valid_targets, key=lambda t: t['area'])
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best_center = best_target['center']
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best_radius = best_target['radius']
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ellipse_params = best_target['ellipse']
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method = "v3_ellipse_red_validated"
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best_radius1 = best_radius * 5
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result_img = image.cv2image(img_cv, False, False)
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return result_img, best_center, best_radius, method, best_radius1, ellipse_params
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def estimate_distance(pixel_radius):
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"""根据像素半径估算实际距离（单位：米）"""
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if not pixel_radius:
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return 0.0
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return (config.REAL_RADIUS_CM * config.FOCAL_LENGTH_PIX) / pixel_radius / 100.0
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def compute_laser_position(circle_center, laser_point, radius, method):
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"""计算激光相对于靶心的偏移量（单位：厘米）"""
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if not all([circle_center, radius, method]):
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return None, None
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cx, cy = circle_center
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lx, ly = laser_point
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# 根据检测方法动态调整靶心物理半径（简化模型）
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circle_r = (radius / 4.0) * 20.0 if method == "模糊" else (68 / 16.0) * 20.0
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dx = lx - cx
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dy = ly - cy
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return dx / (circle_r / 100.0), -dy / (circle_r / 100.0)
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def save_shot_image(result_img, center, radius, method, ellipse_params,
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laser_point, distance_m, photo_dir=None):
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"""
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保存射击图像（带标注）
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即使没有检测到靶心也会保存图像，文件名会标注 "no_target"
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确保保存的图像总是包含激光十字线
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Args:
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result_img: 处理后的图像对象（可能已经包含激光十字线或检测标注）
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center: 靶心中心坐标 (x, y)，可能为 None（未检测到靶心）
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radius: 靶心半径，可能为 None（未检测到靶心）
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method: 检测方法，可能为 None（未检测到靶心）
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ellipse_params: 椭圆参数 ((center, (width, height), angle)) 或 None
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laser_point: 激光点坐标 (x, y)
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distance_m: 距离（米），可能为 None（未检测到靶心）
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photo_dir: 照片存储目录，如果为None则使用 config.PHOTO_DIR
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Returns:
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str: 保存的文件路径，如果保存失败或未启用则返回 None
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"""
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# 检查是否启用图像保存
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if not config.SAVE_IMAGE_ENABLED:
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return None
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if photo_dir is None:
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photo_dir = config.PHOTO_DIR
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try:
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# 确保照片目录存在
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try:
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if photo_dir not in os.listdir("/root"):
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os.mkdir(photo_dir)
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except:
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pass
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# 生成文件名
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# 统计所有图片文件（包括 .bmp 和 .jpg）
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try:
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all_images = [f for f in os.listdir(photo_dir) if f.endswith(('.bmp', '.jpg', '.jpeg'))]
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img_count = len(all_images)
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except:
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img_count = 0
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x, y = laser_point
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# 如果未检测到靶心，在文件名中标注
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if center is None or radius is None:
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method_str = "no_target"
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distance_str = "000"
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else:
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method_str = method or "unknown"
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distance_str = str(round((distance_m or 0.0) * 100))
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filename = f"{photo_dir}/{method_str}_{int(x)}_{int(y)}_{distance_str}_{img_count:04d}.bmp"
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logger = logger_manager.logger
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if logger:
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if center and radius:
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logger.info(f"结果 -> 圆心: {center}, 半径: {radius}, 方法: {method}")
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if ellipse_params:
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(ell_center, (width, height), angle) = ellipse_params
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logger.info(f"椭圆 -> 中心: ({ell_center[0]:.1f}, {ell_center[1]:.1f}), 长轴: {max(width, height):.1f}, 短轴: {min(width, height):.1f}, 角度: {angle:.1f}°")
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else:
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logger.info(f"结果 -> 未检测到靶心，保存原始图像（激光点: ({x}, {y})）")
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# 转换图像为 OpenCV 格式以便绘制
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img_cv = image.image2cv(result_img, False, False)
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# 确保激光十字线被绘制（使用OpenCV在图像上绘制，确保可见性）
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laser_color = (config.LASER_COLOR[0], config.LASER_COLOR[1], config.LASER_COLOR[2])
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thickness = max(config.LASER_THICKNESS, 2) # 至少2像素宽，确保可见
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length = max(config.LASER_LENGTH, 10) # 至少10像素长
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# 绘制激光十字线（水平线）
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cv2.line(img_cv,
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(int(x - length), int(y)),
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(int(x + length), int(y)),
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laser_color, thickness)
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# 绘制激光十字线（垂直线）
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cv2.line(img_cv,
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(int(x), int(y - length)),
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(int(x), int(y + length)),
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laser_color, thickness)
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# 绘制激光点
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cv2.circle(img_cv, (int(x), int(y)), max(thickness, 3), laser_color, -1)
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# 如果检测到靶心，绘制靶心标注
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if center and radius:
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cx, cy = center
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if ellipse_params:
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(ell_center, (width, height), angle) = ellipse_params
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cx_ell, cy_ell = int(ell_center[0]), int(ell_center[1])
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# 绘制椭圆
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cv2.ellipse(img_cv,
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(cx_ell, cy_ell),
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(int(width/2), int(height/2)),
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angle,
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0, 360,
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(0, 255, 0),
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2)
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cv2.circle(img_cv, (cx_ell, cy_ell), 3, (255, 0, 0), -1)
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# 绘制短轴
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minor_length = min(width, height) / 2
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minor_angle = angle + 90 if width >= height else angle
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minor_angle_rad = math.radians(minor_angle)
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dx_minor = minor_length * math.cos(minor_angle_rad)
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dy_minor = minor_length * math.sin(minor_angle_rad)
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pt1_minor = (int(cx_ell - dx_minor), int(cy_ell - dy_minor))
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pt2_minor = (int(cx_ell + dx_minor), int(cy_ell + dy_minor))
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cv2.line(img_cv, pt1_minor, pt2_minor, (0, 0, 255), 2)
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else:
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# 绘制圆形靶心
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cv2.circle(img_cv, (cx, cy), radius, (0, 0, 255), 2)
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cv2.circle(img_cv, (cx, cy), 2, (0, 0, 255), -1)
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# 如果检测到靶心，绘制从激光点到靶心的连线（可选，用于可视化偏移）
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cv2.line(img_cv, (int(x), int(y)), (cx, cy), (255, 255, 0), 1)
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# 转换回 MaixPy 图像格式并保存
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result_img = image.cv2image(img_cv, False, False)
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result_img.save(filename)
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if logger:
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if center and radius:
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logger.debug(f"图像已保存（含靶心标注）: {filename}")
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else:
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logger.debug(f"图像已保存（无靶心，含激光十字线）: {filename}")
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return filename
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except Exception as e:
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logger = logger_manager.logger
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if logger:
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logger.error(f"保存图像失败: {e}")
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import traceback
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logger.error(traceback.format_exc())
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return None
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