330 lines
14 KiB
Python
330 lines
14 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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离线测试脚本：直接复用 detect_circle 逻辑进行测试
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运行环境：MaixPy (Sipeed MAIX)
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"""
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import sys
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import os
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# import time
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from maix import image, time
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import cv2
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import numpy as np
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import math
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# ==================== 全局配置 (与 test_main.py 保持一致) ====================
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REAL_RADIUS_CM = 20 # 靶心实际半径（厘米）
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def detect_circle_v3(frame, laser_point=None, img_cv=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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优化：
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1. 缩图到 MAX_DET_DIM 后再做 HSV/形态学，最长边 640->320 可获得 ~4x 加速
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2. 红色掩码在黄色轮廓循环外只计算一次，避免 N 次重复计算
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3. img_cv 可由外部传入（与其他线程共享转换结果），为 None 时自动转换
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Args:
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frame: 图像帧（img_cv 为 None 时使用）
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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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img_cv: 已转换的 numpy BGR/RGB 图像；不为 None 时跳过 image2cv 转换
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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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if img_cv is None:
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img_cv = image.image2cv(frame, False, False)
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from datetime import datetime
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print(f"[detect_circle_v3] begin {datetime.now()}")
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# -- 1. 缩图加速（与三角形路径保持一致）
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h_orig, w_orig = img_cv.shape[:2]
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MAX_DET_DIM = 320
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long_side = max(h_orig, w_orig)
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if long_side > MAX_DET_DIM:
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det_scale = MAX_DET_DIM / long_side
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img_det = cv2.resize(img_cv, (int(w_orig * det_scale), int(h_orig * det_scale)),
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interpolation=cv2.INTER_LINEAR)
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inv_scale = 1.0 / det_scale # 检测坐标 -> 原始坐标的倍率
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else:
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img_det = img_cv
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inv_scale = 1.0
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# 激光点映射到检测分辨率
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lp_det = None
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if laser_point is not None:
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lp_det = (laser_point[0] / inv_scale, laser_point[1] / inv_scale)
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best_center = best_radius = best_radius1 = method = None
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ellipse_params = None
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print(f"[detect_circle_v3] step 1 fin {datetime.now()}")
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# -- 2. HSV + 黄色掩码
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hsv = cv2.cvtColor(img_det, cv2.COLOR_RGB2HSV)
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h, s, v = cv2.split(hsv)
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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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lower_yellow = np.array([7, 80, 0])
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upper_yellow = np.array([32, 255, 255])
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mask_yellow = cv2.inRange(hsv, lower_yellow, upper_yellow)
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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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print(f"[detect_circle_v3] step 2 fin {datetime.now()}")
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# -- 3. 红色掩码：在循环外只算一次
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mask_red = cv2.bitwise_or(
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cv2.inRange(hsv, np.array([0, 50, 40]), np.array([10, 255, 255])),
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cv2.inRange(hsv, np.array([170, 50, 40]), np.array([180, 255, 255])),
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)
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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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# 预先把红色轮廓筛选成 (center, radius) 列表，后续直接查表
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red_candidates = []
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for cnt_r in contours_red:
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ar = cv2.contourArea(cnt_r)
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if ar <= 30:
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continue
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pr = cv2.arcLength(cnt_r, True)
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if pr <= 0 or (4 * np.pi * ar) / (pr * pr) <= 0.4:
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continue
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if len(cnt_r) >= 5:
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(xr, yr), (wr, hr), _ = cv2.fitEllipse(cnt_r)
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red_candidates.append({"center": (int(xr), int(yr)), "radius": int(min(wr, hr) / 2)})
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else:
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(xr, yr), rr = cv2.minEnclosingCircle(cnt_r)
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red_candidates.append({"center": (int(xr), int(yr)), "radius": int(rr)})
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print(f"[detect_circle_v3] step 3 fin {datetime.now()}")
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# -- 4. 黄色轮廓循环（复用上面的红色候选列表）
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contours_yellow, _ = cv2.findContours(mask_yellow, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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valid_targets = []
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for cnt_yellow in contours_yellow:
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area = cv2.contourArea(cnt_yellow)
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if area <= 50:
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continue
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perimeter = cv2.arcLength(cnt_yellow, True)
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if perimeter <= 0:
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continue
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circularity = (4 * np.pi * area) / (perimeter * perimeter)
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if circularity <= 0.7:
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continue
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print(f"[target] -> 面积:{area:.1f}, 圆度:{circularity:.2f}")
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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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yellow_center = (int(x), int(y))
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yellow_radius = int(min(width, height) / 2)
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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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# 在预筛好的红色候选中匹配
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matched = False
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for rc in red_candidates:
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ddx = yellow_center[0] - rc["center"][0]
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ddy = yellow_center[1] - rc["center"][1]
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dist_centers = math.hypot(ddx, ddy)
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if dist_centers < yellow_radius * 1.5 and rc["radius"] > yellow_radius * 0.7:
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print(f"[target] -> 找到匹配的红圈: 黄心({yellow_center}), "
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f"红心({rc['center']}), 距离:{dist_centers:.1f}, "
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f"黄半径:{yellow_radius}, 红半径:{rc['radius']}")
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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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matched = True
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break
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if not matched :
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print("Debug -> 未找到匹配的红色圆圈，可能是误识别")
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print(f"[detect_circle_v3] step 4 fin {datetime.now()}")
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# -- 5. 选最佳目标，坐标还原到原始分辨率
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if valid_targets:
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if lp_det:
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best_target = min(valid_targets,
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key=lambda t: (t["center"][0] - lp_det[0]) ** 2
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+ (t["center"][1] - lp_det[1]) ** 2)
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method = "v3_ellipse_red_validated_laser_selected"
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else:
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best_target = max(valid_targets, key=lambda t: t["area"])
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method = "v3_ellipse_red_validated"
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bc = best_target["center"]
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br = best_target["radius"]
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be = best_target["ellipse"]
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if inv_scale != 1.0:
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best_center = (int(bc[0] * inv_scale), int(bc[1] * inv_scale))
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best_radius = int(br * inv_scale)
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if be is not None:
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(ex, ey), (ew, eh), ea = be
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be = ((ex * inv_scale, ey * inv_scale),
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(ew * inv_scale, eh * inv_scale), ea)
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else:
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best_center = bc
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best_radius = br
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ellipse_params = be
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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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print(f"[detect_circle_v3] step 5 fin {datetime.now()}")
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return result_img, best_center, best_radius, method, best_radius1, ellipse_params
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def run_offline_test(image_path):
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"""读取图片，检测圆，绘制结果，保存图片"""
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# 1. 检查文件是否存在
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if not os.path.exists(image_path):
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print(f"[ERROR] 找不到图片文件: {image_path}")
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return
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# 2. 使用 maix.image 读取图片 (适配 MaixPy v4)
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try:
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# 使用 image.load 读取文件，返回 Image 对象
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img = image.load(image_path)
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print(f"[INFO] 成功读取图片: {image_path} (尺寸: {img.width()}x{img.height()})")
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except Exception as e:
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print(f"[ERROR] 读取图片失败: {e}")
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print("提示：请确认 MaixPy 版本是否为 v4，且图片路径正确。")
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return
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# 3. 调用 detect_circle_v3 函数
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print("[INFO] 正在调用 detect_circle_v3 进行检测...")
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start_time = time.ticks_ms()
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result_img, center, radius, method, radius1, ellipse_params = detect_circle_v3(img)
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cost_time = time.ticks_ms() - start_time
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print(f"[INFO] 检测完成，耗时: {cost_time}ms")
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print(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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print(
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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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# 4. 绘制辅助线（可选，用于调试）
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if center and radius:
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# 为了绘制椭圆，需要转换回 cv2 图像
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img_cv = image.image2cv(result_img, False, False)
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cx, cy = center
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# 如果有椭圆参数，绘制椭圆
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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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# 确定长轴和短轴
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if width >= height:
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# width 是长轴，height 是短轴
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axes_major = width
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axes_minor = height
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major_angle = angle # 长轴角度就是 angle
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minor_angle = angle + 90 # 短轴角度 = 长轴角度 + 90度
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else:
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# height 是长轴，width 是短轴
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axes_major = height
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axes_minor = width
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major_angle = angle + 90 # 长轴角度 = width角度 + 90度
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minor_angle = angle # 短轴角度就是 angle
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# 使用 OpenCV 绘制椭圆（绿色，线宽2）
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cv2.ellipse(img_cv,
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(cx_ell, cy_ell), # 中心点
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(int(width / 2), int(height / 2)), # 半宽、半高
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angle, # 旋转角度（OpenCV需要原始angle）
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0, 360, # 起始和结束角度
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(0, 255, 0), # 绿色 (RGB格式)
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2) # 线宽
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# 绘制椭圆中心点（红色）
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cv2.circle(img_cv, (cx_ell, cy_ell), 3, (255, 0, 0), -1)
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import math
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# 绘制短轴（蓝色线条）
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minor_length = axes_minor / 2
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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) # 蓝色 (RGB格式)
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else:
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# 如果没有椭圆参数，绘制圆形（红色）
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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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# 转换回 maix image
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result_img = image.cv2image(img_cv, False, False)
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# 定义颜色对象用于文字
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try:
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color_black = image.Color.from_rgb(0, 0, 0)
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except AttributeError:
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color_black = image.Color(0, 0, 0)
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# D. 添加文字信息
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FOCAL_LENGTH_PIX = 1900
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d = (REAL_RADIUS_CM * FOCAL_LENGTH_PIX) / radius1 / 100.0
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info_str = f"R:{radius} M:{method} D:{d:.2f}"
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print(info_str)
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# 计算文字位置，防止超出图片边界
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r_outer = int(radius * 11.0) if radius else 100
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text_y = cy - r_outer - 20 if cy > r_outer + 20 else cy + r_outer + 20
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# 调用 draw_string
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result_img.draw_string(0, 0, info_str, color=color_black, scale=1.0)
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# 5. 保存结果图片
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base, ext = os.path.splitext(image_path)
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output_path = f"{base}_result{ext}"
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try:
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result_img.save(output_path, quality=100)
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print(f"[SUCCESS] 结果已保存至: {output_path}")
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except Exception as e:
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print(f"[ERROR] 保存图片失败: {e}")
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if __name__ == "__main__":
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# ================= 配置区域 =================
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# 1. 设置要测试的图片路径
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# 建议将图片放在与脚本同级目录，或者使用绝对路径
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TARGET_IMAGE = "/root/phot/None_314_258_0_0041.bmp"
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TARGET_DIR = "/root/phot" # 修改为你想要读取的目录路径
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# 支持的图片格式
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IMAGE_EXTENSIONS = ['.jpg', '.jpeg', '.png', '.bmp']
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# ================= 执行区域 =================
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if 'TARGET_DIR' in locals():
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# 读取目录下所有图片文件，过滤掉 _result.jpg 后缀的文件
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image_files = []
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if os.path.exists(TARGET_DIR) and os.path.isdir(TARGET_DIR):
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for filename in os.listdir(TARGET_DIR):
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# 检查文件扩展名
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if any(filename.lower().endswith(ext) for ext in IMAGE_EXTENSIONS):
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# 过滤掉 _result.jpg 后缀的文件
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if not filename.endswith('_result.jpg'):
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filepath = os.path.join(TARGET_DIR, filename)
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if os.path.isfile(filepath):
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image_files.append(filepath)
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# 按文件名排序（可选）
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image_files.sort()
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print(f"[INFO] 在目录 {TARGET_DIR} 中找到 {len(image_files)} 张图片")
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# 处理每张图片
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for img_path in image_files:
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print(f"\n{'=' * 10} 开始处理: {img_path} {'=' * 10}")
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run_offline_test(img_path)
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else:
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print(f"[ERROR] 目录不存在或不是有效目录: {TARGET_DIR}")
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else:
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run_offline_test(TARGET_IMAGE) |