mirror of
https://github.com/NanjingForestryUniversity/supermachine--tomato-passion_fruit.git
synced 2025-11-08 22:34:00 +00:00
106 lines
3.3 KiB
Python
106 lines
3.3 KiB
Python
import cv2
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import numpy as np
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from sklearn import svm
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from sklearn.preprocessing import StandardScaler
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import time
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import os
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import joblib
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def load_model(model_path):
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# 加载模型和标准化器
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model, scaler = joblib.load(model_path)
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return model, scaler
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def predict_image_array(image_array, model_path):
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# 加载模型和标准化器
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model, scaler = load_model(model_path)
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# 将图像转换为像素数组
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test_pixels = image_array.reshape(-1, 3)
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# 标准化
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test_pixels_scaled = scaler.transform(test_pixels)
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# 预测
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predictions = model.predict(test_pixels_scaled)
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# 转换预测结果为图像
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mask_predicted = predictions.reshape(image_array.shape[0], image_array.shape[1])
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return mask_predicted
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def prepare_data(image_dir, mask_dir):
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# 初始化像素和标签列表
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all_pixels = []
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all_labels = []
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# 获取图像和掩码文件名列表
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image_files = sorted(os.listdir(image_dir))
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mask_files = sorted(os.listdir(mask_dir))
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# 遍历所有图像和掩码文件
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for image_file, mask_file in zip(image_files, mask_files):
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# 读取原始图像和掩码图像
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image = cv2.imread(os.path.join(image_dir, image_file))
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mask = cv2.imread(os.path.join(mask_dir, mask_file), cv2.IMREAD_GRAYSCALE)
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# 提取像素
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pixels = image.reshape(-1, 3) # 将图像转换为(n_pixels, 3)
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labels = (mask.reshape(-1) > 128).astype(int) # 标记为0或1
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# 添加到列表
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all_pixels.append(pixels)
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all_labels.append(labels)
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# 将列表转换为NumPy数组
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all_pixels = np.concatenate(all_pixels, axis=0)
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all_labels = np.concatenate(all_labels, axis=0)
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return all_pixels, all_labels
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# 加载数据
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train_pixels, train_labels = prepare_data('/Users/xs/PycharmProjects/super-tomato/datasets_green/train-2/img',
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'/Users/xs/PycharmProjects/super-tomato/datasets_green/train-2/label')
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# 数据标准化
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scaler = StandardScaler()
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train_pixels_scaled = scaler.fit_transform(train_pixels)
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# 创建SVM模型
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# model = svm.SVC(kernel='linear', C=1.0)
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# model.fit(train_pixels_scaled, train_labels)
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# # 在训练模型后保存模型
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# joblib.dump((model, scaler), '/Users/xs/PycharmProjects/super-tomato/svm_green.joblib') # 替换为你的模型文件路径
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print('模型训练完成！')
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def predict_image(image_path, model, scaler):
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# 读取图像
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image = cv2.imread(image_path)
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test_pixels = image.reshape(-1, 3)
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# 标准化
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test_pixels_scaled = scaler.transform(test_pixels)
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# 预测
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predictions = model.predict(test_pixels_scaled)
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# 转换预测结果为图像
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mask_predicted = predictions.reshape(image.shape[0], image.shape[1])
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return mask_predicted
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# 对一个新的图像进行预测
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time1 = time.time()
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model, scaler = load_model('/Users/xs/PycharmProjects/super-tomato/svm_green.joblib')
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predicted_mask = predict_image('/Users/xs/PycharmProjects/super-tomato/defect_big.bmp', model, scaler)
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cv2.imwrite('/Users/xs/PycharmProjects/super-tomato/defect_mask.bmp', (predicted_mask * 255).astype('uint8'))
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cv2.imshow('Predicted Mask', (predicted_mask * 255).astype('uint8'))
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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time2 = time.time()
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print(f'预测时间: {time2 - time1:.2f}秒')
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