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