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model_predict.py

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+from PIL import Image
+import numpy as np
+import paddle.fluid as fluid
+from 口罩检测.util import train_parameters
+from 口罩检测.VGGNet import VGGNet
+def load_image(img_path):
+    img =Image.open(img_path)
+    if img.mode !='RGB':
+        img = img.covert('RGB')
+    img = img.resize((244,244),Image.BILINEAR)
+    img = np.array(img).astype('float32')
+    img = img.transpose((2,0,1))
+    img = img/255.0
+    return img
+
+label_dict = train_parameters['label_dict']
+
+#模型预测
+with fluid.dygraph.guard():
+    model,_ = fluid.dygraph.load_dygraph('vgg')
+    vgg = VGGNet()
+    vgg.eval()
+    infer_path='./unmask.jpg'
+    img = Image.open(infer_path)
+
+    x_data = load_image(infer_path)
+    x_data = np.array(x_data)
+    x_data = x_data[np.newaxis,:,:,:]
+    x_data= fluid.dygraph.to_variable(x_data)
+    out = vgg(x_data)
+    result = np.argmax(out.numpy())
+    print(label_dict)
+    print("被预测的图片为：{}".format(label_dict[str(result)]))