Upload app.py

app.py

@@ -163,9 +163,8 @@ class ResNetModel():
 
 faceRecognition = ResNetModel.load("resnet_v2")
 def predict(image):
-
     faceCascade = cv2.CascadeClassifier('Cascades/haarcascade_frontalface_default.xml')
-    frame = cv2.flip(image, 1)
+    frame = np.array(image)
     faces = faceCascade.detectMultiScale(
         frame,     
         scaleFactor=1.2,
@@ -173,21 +172,17 @@ def predict(image):
         minSize=(20, 20)
     )
     for (x,y,w,h) in faces:
-        cv2.rectangle(frame,(x,y),(x+w,y+h),(0,0,255),2)
+        cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),3)
         roi_color = frame[y:y+h, x:x+w]
         roi_color = cv2.flip(roi_color, 1)
         label, conf = faceRecognition.predict(roi_color)
         label = label[:12] + "..." if len(label) > 10 else label
         text = "{label} : {conf:.3f}".format(label = label, conf = conf)
         
-        cv2.putText(frame, text, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0,0,255), 2)
+        cv2.putText(frame, text, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255,0,0), 3)
     return frame
 
-css = """.my-group {max-width: 600px !important; max-height: 600px !important;}
-         .my-column {display: flex !important; justify-content: center !important; align-items: center !important;}"""
-rtc_configuration = {
-    "frameRate": {"ideal": 10}
-}
+css = """.my-column {max-width: 600px;}"""
 with gr.Blocks(css=css) as demo:
     gr.HTML(
         """
@@ -201,13 +196,15 @@ with gr.Blocks(css=css) as demo:
         </ul>
         """
     )
-    with gr.Column(elem_classes=["my-column"]):
-        with gr.Group(elem_classes=["my-group"]):
-            image = WebRTC(label="Stream", rtc_configuration=rtc_configuration)
+    with gr.Row():
+        with gr.Column(elem_classes=["my-column"]):
+            input_img = gr.Image(label="Input", sources="webcam")
+        with gr.Column(elem_classes=["my-column"]):
+            output_img = gr.Image(label="Output")
 
-        image.stream(
-            fn=predict, inputs=[image], outputs=[image], time_limit=60
+        input_img.stream(
+            fn=predict, inputs=[input_img], outputs=[output_img], time_limit=1, stream_every=0.1
         )
 
 if __name__ == "__main__":
-    demo.launch(share=True)
+    demo.launch()