系统简介:
这是一款基于深度学习技术的植物草药智能识别系统。系统通过分析植物草药的图像,能够准确地识别出不同种类的草药,并提供相关的信息和用途。用户只需将植物草药的图像上传至系统,即可快速获得识别结果。
系统利用先进的深度学习算法,对植物草药图像进行特征提取和模式识别,从而实现高精度的分类和识别。同时系统具备良好的用户界面和友好的交互体验,使得用户能够轻松使用并获得所需的识别结果。
系统界面:
部分代码(完整代码在最后):
python
def main(img_path):
import os
os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
img_size = 224
data_transform = transforms.Compose(
[transforms.Resize(int(img_size * 1.143)),
transforms.CenterCrop(img_size),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
assert os.path.exists(img_path), "file: '{}' dose not exist.".format(img_path)
img = Image.open(img_path)
plt.imshow(img)
img = data_transform(img)
img = torch.unsqueeze(img, dim=0)
json_path = './class_indices.json'
assert os.path.exists(json_path), "file: '{}' dose not exist.".format(json_path)
json_file = open(json_path, "r")
class_indict = json.load(json_file)
# create model
model = create_model(num_classes=67).to(device)
# load model weights
model_weight_path = "model-90.pth"
model.load_state_dict(torch.load(model_weight_path, map_location=device))
model.eval()
with torch.no_grad():
# predict class
output = torch.squeeze(model(img.to(device))).cpu()
predict = torch.softmax(output, dim=0)
predict_cla = torch.argmax(predict).numpy()
for i in range(len(predict)):
print("class: {:10} prob: {:.3}".format(class_indict[str(i)],
predict[i].numpy()))
# plt.show()
res = class_indict[str(list(predict.numpy()).index(max(predict.numpy())))]
num= "%.2f" % (max(predict.numpy()) * 100) + "%"
print(res,num)
return res,max(predict.numpy())