Update app.py

app.py

@@ -68,57 +68,6 @@ def show_preds_image(image_path):
 
     return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
 
-def show_preds_webcam(pil_image):
-    image= np.array(pil_image)
-    outputs = model.predict(image)
-    results = outputs[0].cpu().numpy()
-
-    yolo_classes = list(model.names.values())
-    classes_ids = [yolo_classes.index(clas) for clas in yolo_classes]
-    colors = [random.choices(range(256), k=3) for _ in classes_ids]
-    
-    for result in outputs:
-        for mask, box in zip(result.masks.xy, result.boxes):
-            
-    #for r in results:
-        #boxes = r.boxes
-        #for box in boxes:
-            cls = box.cls[0]
-            conf = math.ceil((box.conf[0]*100))/100
-            if (int(cls)<3) and (conf > 0.70):
-                
-                points = np.int32([mask])
-                cv2.polylines(img, points, True, (255, 0, 0), 1)
-                color_number = classes_ids.index(int(box.cls[0]))
-                color = colors[color_number]
-                            
-                cv2.fillPoly(image, points, color)
-                
-                x1, y1, x2, y2 = box.xyxy[0]
-                x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
-
-                
-
-
-                name = yolo_classes[int(cls)]
-                # fontScale
-                fontScale = 0.5
-
-                color_number = classes_ids.index(int(box.cls[0]))
-                color = colors[color_number]
-
-                # Line thickness of 2 px
-                thickness = 1
-                font = cv2.FONT_HERSHEY_SIMPLEX
-
-                cv2.putText(image, str(name) + " " + str(conf), (max(0,x1), max(35,y1)), font,
-                       fontScale, color, thickness, cv2.LINE_AA)
-
-    
-    return image
-
-    
-
 inputs_image = [
     gr.components.Image(type="filepath", label="Input Image"),
 ]
@@ -132,22 +81,8 @@ interface_image = gr.Interface(
     title="Object segmentation",
     examples=path,
     cache_examples=False,
-)
+).launch()
 
-outputs_video = [
-    gr.components.Image(type="numpy", label="Output Image"),
-]
 
-interface_webcam =  gr.Interface(
-    fn=show_preds_webcam,
-    inputs=gr.Image(sources=["webcam"], streaming=True, type="pil"),
-    outputs=outputs_video,
-    live=True
-    )
-
-gr.TabbedInterface(
-    [  interface_webcam, interface_image],
-    tab_names=[  'Webcam', "Image"]
-).queue().launch()