102 lines
3.7 KiB
Python
102 lines
3.7 KiB
Python
# vision/detector.py
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import os
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import cv2
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from vision.angle_detector import get_current_door_angle
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from vision.overflow_detector import detect_overflow_from_image
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from vision.alignment_detector import detect_vehicle_alignment
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from config.settings import app_set_config
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class VisionDetector:
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def __init__(self):
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pass
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#model路径在对应的模型里面
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# self.alignment_model = os.path.join(current_dir, "align_model/yolov11_cls_640v6.rknn")
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def load_models(self):
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"""
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加载所有视觉检测模型
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"""
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from ultralytics import YOLO
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success = True
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# 加载夹角检测模型
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try:
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if not os.path.exists(app_set_config.angle_model_path):
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print(f"夹角检测模型不存在: {app_set_config.angle_model_path}")
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success = False
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else:
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# 注意：angle.pt模型通过predict_obb_best_angle函数使用，不需要预加载
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print(f"夹角检测模型路径: {app_set_config.angle_model_path}")
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except Exception as e:
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print(f"检查夹角检测模型失败: {e}")
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success = False
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# 加载堆料检测模型
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try:
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if not os.path.exists(app_set_config.overflow_model_path):
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print(f"堆料检测模型不存在: {app_set_config.overflow_model_path}")
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success = False
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else:
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self.overflow_model = YOLO(app_set_config.overflow_model_path)
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print(f"成功加载堆料检测模型: {app_set_config.overflow_model_path}")
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except Exception as e:
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print(f"加载堆料检测模型失败: {e}")
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success = False
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# 加载对齐检测模型
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try:
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if not os.path.exists(app_set_config.alignment_model_path):
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print(f"对齐检测模型不存在: {app_set_config.alignment_model_path}")
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success = False
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else:
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self.alignment_model = YOLO(app_set_config.alignment_model_path)
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print(f"成功加载对齐检测模型: {app_set_config.alignment_model_path}")
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except Exception as e:
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print(f"加载对齐检测模型失败: {e}")
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success = False
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return success
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def detect_angle(self, image=None):
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"""
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通过视觉系统获取当前出砼门角度
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"""
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# image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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image=cv2.flip(image, 0)
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return get_current_door_angle(
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model=app_set_config.angle_model_path,
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image=image
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)
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def detect_overflow(self, image_array):
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"""
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通过图像检测是否溢料
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"""
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image_array=cv2.flip(image_array, 0)
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# cv2.imwrite('test.jpg', image_array)
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# cv2.namedWindow("Alignment", cv2.WINDOW_NORMAL)
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# cv2.resizeWindow("Alignment", 640, 480)
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# cv2.imshow("Alignment", image_array)
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# cv2.waitKey(1)
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# print('path:', app_set_config.overflow_model_path)
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# print('roi:', app_set_config.roi_file_path)
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return detect_overflow_from_image(
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app_set_config.overflow_model_path,
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app_set_config.roi_file_path,
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image_array
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)
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def detect_vehicle_alignment(self, image_array)->bool:
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"""
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通过图像检测模具车是否对齐
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"""
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image_array = cv2.cvtColor(image_array, cv2.COLOR_BGR2RGB)
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image_array=cv2.flip(image_array, 0)
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# cv2.namedWindow("Alignment", cv2.WINDOW_NORMAL)
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# cv2.resizeWindow("Alignment", 640, 480)
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# cv2.imshow("Alignment", image_array)
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# cv2.waitKey(1)
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return detect_vehicle_alignment(image_array)
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