Spaces:
Running
Running
File size: 2,506 Bytes
4597f1c 3b7916d 4597f1c 3b7916d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 |
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
@app.post("/predict/")
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]}
|