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import gradio as gr |
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from mtcnn import MTCNN |
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import cv2 |
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import numpy as np |
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import time |
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import concurrent.futures |
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ff = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') |
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ff_alt = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_alt.xml') |
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ff_alt2 = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_alt2.xml') |
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pf = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_profileface.xml') |
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mtcnn = MTCNN() |
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global_start = time.perf_counter() |
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haar_start = 0 |
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mtcnn_start = 0 |
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def get_unique_face_locations(all_face_locations): |
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unique_detected_faces = [] |
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for (x1, y1, w1, h1) in all_face_locations: |
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unique = True |
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for (x2, y2, w2, h2) in unique_detected_faces: |
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if abs(x1 - x2) < 50 and abs(y1 - y2) < 50: |
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unique = False |
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break |
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if unique: |
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unique_detected_faces.append((x1, y1, w1, h1)) |
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return unique_detected_faces |
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def detect_haar(gray): |
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global haar_start |
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haar_start = time.perf_counter() |
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ff_faces = ff.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=10, minSize=(25, 25)) |
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ff_alt2_faces = ff_alt2.detectMultiScale(gray, scaleFactor=1.05, minNeighbors=10, minSize=(20, 20)) |
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pf_faces = pf.detectMultiScale(gray, scaleFactor=1.05, minNeighbors=5, minSize=(20, 20)) |
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return ff_faces, ff_alt2_faces, pf_faces |
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def detect_mtcnn(frame): |
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global mtcnn_start |
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mtcnn_start = time.perf_counter() |
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faces = mtcnn.detect_faces(frame) |
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mt_faces = [face['box'] for face in faces] |
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return mt_faces |
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def detect_faces(image): |
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frame = image |
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gray = cv2.cvtColor(np.array(image), cv2.COLOR_BGR2GRAY) |
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with concurrent.futures.ThreadPoolExecutor() as executor: |
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haar_detections = executor.submit(detect_haar, gray) |
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mtcnn_detections = executor.submit(detect_mtcnn, frame) |
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ff_faces, ff_alt2_faces, pf_faces = haar_detections.result() |
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mt_faces = mtcnn_detections.result() |
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all_faces = [*ff_faces, *ff_alt2_faces, *pf_faces, *mt_faces] |
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unique_detected_faces = get_unique_face_locations(all_faces) |
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for (x, y, w, h) in unique_detected_faces: |
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cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 3) |
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frame = cv2.putText(frame, f"{len(unique_detected_faces)} Faces", (20, 650), cv2.FONT_HERSHEY_SIMPLEX, 1.6, (0, 0, 0), 5) |
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print('\n\n') |
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print(f"Haar Took - {time.perf_counter() - haar_start:.2f}s") |
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print(f"MTCNN Took - {time.perf_counter() - mtcnn_start:.2f}s") |
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print(f"Total Time - {time.perf_counter() - global_start:.2f}s") |
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print('\n\n') |
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return frame |
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iface = gr.Interface( |
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fn=detect_faces, |
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inputs=gr.components.Image(sources="webcam"), |
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outputs=[gr.components.Image(type="numpy", label="Processed Image")], |
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live=True |
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) |
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iface.launch() |
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