FaceGNN Updated v1.1

19ea92a over 1 year ago

3.03 kB

from fastapi import FastAPI
from pydantic import BaseModel
import uvicorn
import requests
from PIL import Image
from io import BytesIO
import numpy as np
import cv2 as cv
from keras_facenet import FaceNet
from mtcnn import MTCNN
import pickle
from sklearn.preprocessing import LabelEncoder

embedder = FaceNet()
detector = MTCNN()

# Load the face recognition model and labels
data = np.load('/home/shanin/Desktop/SHANIN/MAIN/ALL_CODE/Face_Recognition/Face_Embedding.npz')
Y = data['labels']
encoder = LabelEncoder()
encoder.fit(Y)

with open('/home/shanin/Desktop/SHANIN/MAIN/ALL_CODE/Face_Recognition/Face_Model.pkl', 'rb') as file:
    model = pickle.load(file)

app = FastAPI()

# Function to get the face embedding
def get_embedding(face_img):
    face_img = face_img.astype('float32') 
    face_img = np.expand_dims(face_img, axis=0)
    yhat = embedder.embeddings(face_img)
    return yhat[0] 
    
# Function to resize large images
def resize_image(image, max_size=1024):
    """
    Resize image to prevent excessive memory usage for large images
    """
    h, w = image.shape[:2]
    if max(h, w) > max_size:
        scale_factor = max_size / max(h, w)
        new_dim = (int(w * scale_factor), int(h * scale_factor))
        image = cv.resize(image, new_dim)
    return image

# Function to process the image URL and make predictions
def get_result(img_url, code):
    response = requests.get(img_url)
    img_data = BytesIO(response.content)
    image = Image.open(img_data)
    if image.mode != "RGB":
        image = image.convert("RGB")

    if code == 'CM_370009':  
        # Rotate the image 90 degrees (one time) clockwise
        image = image.rotate(-90, expand=True)
    
    # Convert PIL image to a NumPy array
    image_np = np.array(image)
    
    # Resize large images to avoid excessive memory usage
    image_np = resize_image(image_np)
    
    # Use the resized image for face detection
    detection_results = detector.detect_faces(image_np)
    if not detection_results:
        raise ValueError("No face detected in the image.")
    
    # Extract face from the detected bounding box
    x, y, w, h = detection_results[0]['box']
    face_image = image_np[y:y+h, x:x+w]
    
    # Resize face image to the required input size for the model
    face_image_resized = cv.resize(face_image, (160, 160))
    
    # Get embedding for face and make prediction
    test_im = get_embedding(face_image_resized)
    test_im = [test_im]
    
    ypreds = model.predict(test_im)
    predicted_label = encoder.inverse_transform(ypreds)[0]
    return predicted_label

# FastAPI Request Model
class ImageRequest(BaseModel):
    code: str
    img: str

@app.post("/face")
async def face_recognition(request_data: ImageRequest):
    code = request_data.code
    img_url = request_data.img
    
    result = get_result(img_url, code)
    print(result)
    
    if result == code:
       return True
    else:
       return False
        
if __name__ == "__main__":
    uvicorn.run(app, host="127.0.0.1", port=4444)


	from fastapi import FastAPI
	from pydantic import BaseModel
	import uvicorn
	import requests
	from PIL import Image
	from io import BytesIO
	import numpy as np
	import cv2 as cv
	from keras_facenet import FaceNet
	from mtcnn import MTCNN
	import pickle
	from sklearn.preprocessing import LabelEncoder

	embedder = FaceNet()
	detector = MTCNN()

	# Load the face recognition model and labels
	data = np.load('/home/shanin/Desktop/SHANIN/MAIN/ALL_CODE/Face_Recognition/Face_Embedding.npz')
	Y = data['labels']
	encoder = LabelEncoder()
	encoder.fit(Y)

	with open('/home/shanin/Desktop/SHANIN/MAIN/ALL_CODE/Face_Recognition/Face_Model.pkl', 'rb') as file:
	model = pickle.load(file)

	app = FastAPI()

	# Function to get the face embedding
	def get_embedding(face_img):
	face_img = face_img.astype('float32')
	face_img = np.expand_dims(face_img, axis=0)
	yhat = embedder.embeddings(face_img)
	return yhat[0]

	# Function to resize large images
	def resize_image(image, max_size=1024):
	"""
	Resize image to prevent excessive memory usage for large images
	"""
	h, w = image.shape[:2]
	if max(h, w) > max_size:
	scale_factor = max_size / max(h, w)
	new_dim = (int(w * scale_factor), int(h * scale_factor))
	image = cv.resize(image, new_dim)
	return image

	# Function to process the image URL and make predictions
	def get_result(img_url, code):
	response = requests.get(img_url)
	img_data = BytesIO(response.content)
	image = Image.open(img_data)
	if image.mode != "RGB":
	image = image.convert("RGB")

	if code == 'CM_370009':
	# Rotate the image 90 degrees (one time) clockwise
	image = image.rotate(-90, expand=True)

	# Convert PIL image to a NumPy array
	image_np = np.array(image)

	# Resize large images to avoid excessive memory usage
	image_np = resize_image(image_np)

	# Use the resized image for face detection
	detection_results = detector.detect_faces(image_np)
	if not detection_results:
	raise ValueError("No face detected in the image.")

	# Extract face from the detected bounding box
	x, y, w, h = detection_results[0]['box']
	face_image = image_np[y:y+h, x:x+w]

	# Resize face image to the required input size for the model
	face_image_resized = cv.resize(face_image, (160, 160))

	# Get embedding for face and make prediction
	test_im = get_embedding(face_image_resized)
	test_im = [test_im]

	ypreds = model.predict(test_im)
	predicted_label = encoder.inverse_transform(ypreds)[0]
	return predicted_label

	# FastAPI Request Model
	class ImageRequest(BaseModel):
	code: str
	img: str

	@app.post("/face")
	async def face_recognition(request_data: ImageRequest):
	code = request_data.code
	img_url = request_data.img

	result = get_result(img_url, code)
	print(result)

	if result == code:
	return True
	else:
	return False

	if __name__ == "__main__":
	uvicorn.run(app, host="127.0.0.1", port=4444)