【Python】OpenCV-使用ResNet50进行图像分类

import os import cv2 import numpy as np from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input, decode_predictions from tensorflow.keras.preprocessing import image # 设置代理 os.environ["HTTP_PROXY"] = "http://127.0.0.1:1080" os.environ["HTTPS_PROXY"] = "http://127.0.0.1:1080" # 加载ResNet50模型 model = ResNet50(weights='imagenet') # 读取和预处理图像 def preprocess_image(img_path): # 加载图像并调整大小为(224, 224) img = image.load_img(img_path, target_size=(224, 224)) # 将图像转换为numpy数组 img_array = image.img_to_array(img) # 在第0轴上添加维度，将其变为(1, 224, 224, 3) img_array = np.expand_dims(img_array, axis=0) # 对图像进行预处理，以适应ResNet50模型的输入要求 img_array = preprocess_input(img_array) return img_array # 加载图像 img_path = 'pandas.jpg' img = preprocess_image(img_path) # 进行预测 predictions = model.predict(img) # 解码预测结果，获取前三个预测结果 decoded_predictions = decode_predictions(predictions, top=3)[0] # 打印结果 print("Predictions:") for i, (imagenet_id, label, score) in enumerate(decoded_predictions): print(f"{i + 1}: {label} ({score:.2f})") # 显示图像 img = cv2.imread(img_path) cv2.imshow('Image', img) cv2.waitKey(0) cv2.destroyAllWindows()