create app.py

app.py

@@ -0,0 +1,88 @@
+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()