import cv2
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import time
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import numpy as np
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import onnxruntime
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from scipy.special import softmax
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# 加载ONNX模型
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session = onnxruntime.InferenceSession("model/classify/s.onnx")
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# 摄像头索引号，通常为0表示第一个摄像头
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camera_index = 0
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# 打开摄像头
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cap = cv2.VideoCapture(camera_index, cv2.CAP_DSHOW)
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# 设置分辨率
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cap.set(cv2.CAP_PROP_FRAME_WIDTH, 3840) # 宽度
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cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 2160) # 高度
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# 检查摄像头是否成功打开
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if not cap.isOpened():
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print("无法打开摄像头")
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exit()
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width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
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height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
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print("摄像头分辨率:", width, "x", height)
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# 从res.json中读取类别
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with open("res1-2.json", "r") as f:
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classes = eval(f.read())
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# 目标图像尺寸
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target_width = 1024
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target_height = 768
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# 计时器
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start_time = time.time()
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# 循环读取摄像头画面
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while True:
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ret, frame = cap.read()
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if not ret:
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print("无法读取摄像头画面")
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break
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# 1920*1080的图像，中心裁剪640*480的区域
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cropped_frame = frame[int(height / 2 - target_height / 2):int(height / 2 + target_height / 2),
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int(width / 2 - target_width / 2):int(width / 2 + target_width / 2)]
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# 调整图像尺寸
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resized_frame = cv2.resize(cropped_frame, (target_width, target_height))
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# 获取当前时间
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current_time = time.time()
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#如果距离上一次保存已经过去1秒，则保存当前画面
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# if current_time - start_time >= 3.0:
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# # 生成保存文件名，以当前时间命名
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# save_name = time.strftime("%Y%m%d%H%M%S", time.localtime()) + ".jpg"
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# # 保存调整尺寸后的图片
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# cv2.imwrite(save_path + save_name, frame)
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# print("保存图片:", save_name)
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# # 重置计时器
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# start_time = time.time()
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# 预处理
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blob = cv2.dnn.blobFromImage(resized_frame, 1 / 255.0, (640, 640), swapRB=True, crop=False)
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# 模型推理
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outputs = session.run(None, {session.get_inputs()[0].name: blob})
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# print(outputs)
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# 应用softmax函数
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probabilities = outputs[0]
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# 找到最大概率的类别
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predicted_class = np.argmax(probabilities, axis=1)[0]
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max_probability = np.max(probabilities, axis=1)[0]
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# 找到概率较高的前十个类别
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top_ten_classes = np.argsort(probabilities, axis=1)[0][-5:]
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# 输出前十个类别
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print("Top 5 Classes:")
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for i in top_ten_classes:
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print(f"{classes[i]}: {probabilities[0][i]}")
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# 显示画面
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cv2.imshow("Camera", resized_frame)
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# 检测按键，如果按下q键则退出循环
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if cv2.waitKey(1) & 0xFF == ord('q'):
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break
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# 关闭摄像头
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cap.release()
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# 关闭所有窗口
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cv2.destroyAllWindows()
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