clicking on face

app.py

@@ -5,6 +5,7 @@ import PIL
 from PIL import Image, ImageDraw, ImageFont
 import time
 
+
 dbackends = [
   ['Haar Cascade (OpenCV)','opencv'], 
   ['Single Shot MultiBox Detector (OpenCV)','ssd'], 
@@ -12,44 +13,59 @@ dbackends = [
   ['RetinaFace','retinaface'],
   ['You Only Look Once v8','yolov8'],
   ['YuNet','yunet'],
-  ['Multi-task Cascade Convolutional Neural Network (TensorFlow) ','mtcnn'], 
+#   ['Multi-task Cascade Convolutional Neural Network (TensorFlow) ','mtcnn'], 
   ['Fast Multi-task Cascade Convolutional Neural Network (PyTorch)','fastmtcnn']
 ]
 
-annotated_image = gr.AnnotatedImage()
-jsontext =  gr.Text(label= "deepface extract_faces results",)
-
-def findFaces(imgfile,dbackend):
-	start_time = time.time()
-	print(start_time)
-
-	face_objs = DeepFace.extract_faces(img_path = imgfile, enforce_detection = False, detector_backend = dbackend)
-	numberoffaces = len(face_objs)
-	jsontext = ''
-	faceannotations = []
-	for face_obj in face_objs:
-		face_coordinates = (face_obj["facial_area"]["x"],face_obj["facial_area"]["y"], (face_obj["facial_area"]["x"] + face_obj["facial_area"]["w"]),(face_obj["facial_area"]["y"] + face_obj["facial_area"]["h"]))
-		face_confidence =  "{:.0%}".format(face_obj["confidence"])
-		face_result=[face_coordinates,face_confidence]
-		faceannotations.append(face_result)
-	jsontext=faceannotations
-	run_time = str(round((time.time() - start_time),2))
-	results = gr.AnnotatedImage(
-        label= "Detected " + str(numberoffaces) + " faces via " + dbackend + ' in ' + run_time + ' seconds.',
-        value=(imgfile, faceannotations)
-    )
+
+with gr.Blocks() as demo:
+
+
+	annotated_image = gr.AnnotatedImage()
+
+	jsontext =  gr.Text(label= "deepface extract_faces results")
+	selected_face = gr.Textbox(label="Selected Face")
+
+		
 	
-	print(run_time)
-	return(results,jsontext,numberoffaces,run_time)
-
-dbackendchoice = gr.Radio(choices=dbackends,label='Detector Backend:',value='retinaface')
-demo = gr.Interface(
-	allow_flagging = "never",
-	fn=findFaces,
-	inputs=[gr.Image(value="8428_26_SM.jpg"), dbackendchoice],
-	outputs=[annotated_image,jsontext],
+	def findFaces(imgfile,dbackend):
+		start_time = time.time()
+		print(start_time)
 	
-)
+		face_objs = DeepFace.extract_faces(img_path = imgfile, enforce_detection = False, detector_backend = dbackend)
+
+		numberoffaces = len(face_objs)
+		jsontext = ''
+		global faceannotations
+		faceannotations = []
+		for face_obj in face_objs:
+			face_coordinates = (face_obj["facial_area"]["x"],face_obj["facial_area"]["y"], (face_obj["facial_area"]["x"] + face_obj["facial_area"]["w"]),(face_obj["facial_area"]["y"] + face_obj["facial_area"]["h"]))
+			face_confidence =  "{:.0%}".format(face_obj["confidence"])
+			face_result=[face_coordinates,face_confidence]
+			faceannotations.append(face_result)
+		
+		jsontext=faceannotations
+		#jsontext=face_objs
+		run_time = str(round((time.time() - start_time),2))
+		results = gr.AnnotatedImage(
+			label= "Detected " + str(numberoffaces) + " faces via " + dbackend + ' in ' + run_time + ' seconds.',
+			value=(imgfile, faceannotations)
+		)
+
+		print(run_time)
+		return(results,jsontext,numberoffaces,run_time)
+
+	dbackendchoice = gr.Radio(choices=dbackends,label='Detector Backend:',value='retinaface')
+	gr.Interface(
+		allow_flagging = "never",
+		fn=findFaces,
+		inputs=[gr.Image(value="8428_26_SM.jpg"), dbackendchoice],
+		outputs=[annotated_image,jsontext,selected_face],
+	)
+	def select_section(evt: gr.SelectData):
+		return faceannotations[evt.index]
+
+	annotated_image.select(select_section, None, selected_face)
 
 demo.launch(show_error=True)