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| import io | |
| from fastapi import FastAPI, File, UploadFile | |
| import torch | |
| import torchvision.transforms as transforms | |
| from PIL import Image | |
| import torch.nn.functional as F | |
| import torch.nn as nn | |
| num_classes = 10 | |
| # Class definition for the model (same as in your code) | |
| class FingerprintRecognitionModel(nn.Module): | |
| def __init__(self, num_classes): | |
| super(FingerprintRecognitionModel, self).__init__() | |
| self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1) | |
| self.pool = nn.MaxPool2d(kernel_size=2, stride=2) | |
| self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1) | |
| self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1) | |
| self.fc1 = nn.Linear(128 * 28 * 28, 256) | |
| self.fc2 = nn.Linear(256, num_classes) | |
| def forward(self, x): | |
| x = self.pool(F.relu(self.conv1(x))) | |
| x = self.pool(F.relu(self.conv2(x))) | |
| x = self.pool(F.relu(self.conv3(x))) | |
| x = x.view(-1, 128 * 28 * 28) | |
| x = F.relu(self.fc1(x)) | |
| x = F.softmax(self.fc2(x), dim=1) | |
| return x | |
| app = FastAPI() | |
| # Load the model | |
| model_path = 'fingerprint_recognition_model.pt' | |
| model = FingerprintRecognitionModel(num_classes) | |
| model.load_state_dict(torch.load(model_path)) | |
| model.eval() | |
| def preprocess_image(image_bytes): | |
| # Convert bytes to PIL Image | |
| image = Image.open(io.BytesIO(image_bytes)).convert('L') # Convert to grayscale | |
| # Resize to 224x224 | |
| img_resized = image.resize((224, 224)) | |
| transform = transforms.Compose([ | |
| transforms.ToTensor(), | |
| transforms.Normalize((0.5,), (0.5,)) | |
| ]) | |
| # Apply transforms and add batch dimension | |
| img_tensor = transform(img_resized).unsqueeze(0) | |
| return img_tensor | |
| def predict_class(image_bytes): | |
| img_tensor = preprocess_image(image_bytes) | |
| with torch.no_grad(): | |
| outputs = model(img_tensor) | |
| _, predicted = torch.max(outputs.data, 1) | |
| predicted_class = int(predicted.item()) | |
| return predicted_class | |
| async def predict_endpoint(file: UploadFile = File(...)): | |
| contents = await file.read() | |
| predicted_class = predict_class(contents) | |
| class_labels = {0: 'Left_ring_fingers', 1: 'Left_thumb_fingers', 2: 'Right_index_fingers', 3: 'Right_little_fingers', 4: 'Right_middle_fingers', 5: 'Right_ring_fingers', 6: 'Right_thumb_fingers', 7: 'left_index_fingers', 8: 'left_little_fingers', 9: 'left_middle_fingers'} | |
| return {"predicted_class": predicted_class, "class_label": class_labels[predicted_class]} | |