Spaces:
Sleeping
Sleeping
| import cv2 | |
| import matplotlib.pyplot as plt | |
| import streamlit as st | |
| from deepface import DeepFace | |
| import mediapipe | |
| import os | |
| import tempfile | |
| backends = [ | |
| 'opencv', | |
| 'ssd', | |
| 'dlib', | |
| 'mtcnn', | |
| 'fastmtcnn', | |
| 'retinaface', | |
| 'mediapipe', | |
| 'yolov8', | |
| 'yunet', | |
| 'centerface', | |
| ] | |
| metrics = ["cosine", "euclidean", "euclidean_l2"] | |
| models = [ | |
| "VGG-Face", | |
| "Facenet", | |
| "Facenet512", | |
| "OpenFace", | |
| "DeepFace", | |
| "DeepID", | |
| "ArcFace", | |
| "Dlib", | |
| "SFace", | |
| "GhostFaceNet", | |
| ] | |
| def verify(img1, img2, model_name, backend, metric): | |
| # Save the uploaded images to temporary files | |
| with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_img1: | |
| temp_img1.write(img1.read()) | |
| temp_img1_path = temp_img1.name | |
| with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_img2: | |
| temp_img2.write(img2.read()) | |
| temp_img2_path = temp_img2.name | |
| img1p = cv2.imread(temp_img1_path) | |
| img2p = cv2.imread(temp_img2_path) | |
| img1p = cv2.cvtColor(img1p, cv2.COLOR_BGR2RGB) | |
| img2p = cv2.cvtColor(img2p, cv2.COLOR_BGR2RGB) | |
| face_detect = mediapipe.solutions.face_detection | |
| face_detector = face_detect.FaceDetection(min_detection_confidence=0.6) | |
| width1, height1 = img1p.shape[1], img1p.shape[0] | |
| width2, height2 = img2p.shape[1], img2p.shape[0] | |
| result1 = face_detector.process(img1p) | |
| result2 = face_detector.process(img2p) | |
| if result1.detections is not None: | |
| for face in result1.detections: | |
| if face.score[0] > 0.80: | |
| bounding_box = face.location_data.relative_bounding_box | |
| x = int(bounding_box.xmin * width1) | |
| w = int(bounding_box.width * width1) | |
| y = int(bounding_box.ymin * height1) | |
| h = int(bounding_box.height * height1) | |
| cv2.rectangle(img1p, (x, y), (x+w, y+h), color=(126, 133, 128), thickness=10) | |
| if result2.detections is not None: | |
| for face in result2.detections: | |
| if face.score[0] > 0.80: | |
| bounding_box = face.location_data.relative_bounding_box | |
| x = int(bounding_box.xmin * width2) | |
| w = int(bounding_box.width * width2) | |
| y = int(bounding_box.ymin * height2) | |
| h = int(bounding_box.height * height2) | |
| cv2.rectangle(img2p, (x, y), (x+w, y+h), color=(126, 133, 128), thickness=10) | |
| st.image([img1p, img2p], caption=["Image 1", "Image 2"], width=200) | |
| face = DeepFace.verify(img1p, img2p, model_name=model_name, detector_backend=backend, distance_metric=metric) | |
| verification = face["verified"] | |
| if verification: | |
| st.write("Matched") | |
| else: | |
| st.write("Not Matched") | |
| # Streamlit app | |
| def main(): | |
| st.title("Face Verification App") | |
| tab_selection = st.sidebar.selectbox("Select Functionality", ["Face Verification", "Face Recognition", "Celebrity Lookalike", "Age and Emotions Detection"]) | |
| if tab_selection == "Face Verification": | |
| st.header("Face Verification") | |
| model_name = st.selectbox("Select Model", models) | |
| backend = st.selectbox("Select Backend", backends) | |
| metric = st.selectbox("Select Metric", metrics) | |
| uploaded_img1 = st.file_uploader("Upload Image 1", type=["jpg", "png"]) | |
| uploaded_img2 = st.file_uploader("Upload Image 2", type=["jpg", "png"]) | |
| if uploaded_img1 and uploaded_img2: | |
| if st.button("Verify Faces"): | |
| verify(uploaded_img1, uploaded_img2, model_name, backend, metric) | |
| # Run the app | |
| if __name__ == "__main__": | |
| main() | |