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