Delete app.py

app.py

@@ -1,79 +0,0 @@
-
-import gradio as gr
-import cv2
-import requests
-import os
-import torch 
-import numpy as np
-from ultralytics import YOLO
-
-model = torch.hub.load('ultralytics/yolov5', 'yolov5x', pretrained=True)
-path  = [['image_0.jpg'], ['image_1.jpg']]
-video_path = [['TresPass_Detection_1.mp4']]
-# area =  [(215, 180), (110, 75), (370, 55), (520, 140), (215, 180) ]
-# area =  [(190, 180), (100, 75), (360, 55), (510, 140), (190, 180) ]
-area =  [(215, 180), (110, 80), (360, 55), (510, 140), (215, 180) ]
-# def show_preds_video(video_path):
-def show_preds_video():
-    cap = cv2.VideoCapture('TresPass_Detection_1.mp4')
-    count=0
-    while(cap.isOpened()):
-        ret, frame = cap.read()
-        if not ret:
-            break
-        count += 1
-        if count % 8 != 0:
-            continue
-            # frame = cv2.imread(video_path)
-
-        frame=cv2.resize(frame,(1020,600))
-        frame_copy = frame.copy()
-
-        cv2.polylines(frame_copy, [np.array(area, np.int32)], True, (0,255,0), 2) 
-
-        results=model(frame)
-        for index, row in results.pandas().xyxy[0].iterrows():
-            x1 = int(row['xmin'])
-            y1 = int(row['ymin'])
-            x2 = int(row['xmax'])
-            y2 = int(row['ymax'])
-            d=(row['name'])
-
-            cx=int(x1+x2)//2
-            cy=int(y1+y2)//2
-
-            if ('person') in d:
-                results = cv2.pointPolygonTest(np.array(area, np.int32), ((cx,cy)), False)
-                # results = cv2.pointPolygonTest(np.array(area, np.int32), ((x2,y1)), False)
-                # results = cv2.pointPolygonTest(np.array(area, np.int32), ((x2,y2)), False)
-                if results >0:
-                        cv2.rectangle(frame_copy,(x1,y1),(x2,y2),(0,0,255),2)
-                        cv2.putText(frame_copy,str(d),(x1,y1),cv2.FONT_HERSHEY_SIMPLEX,1,(0,0,255),1)
-                        cv2.putText(frame_copy,str("Alert !!! Trespasser detected !!!"),(50,300),cv2.FONT_HERSHEY_PLAIN,2,(0,0,255),3)
-
-       
-
-        yield cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB)
-
-inputs_video = [  #gr.components.Video(type="filepath", label="Input Video", visible =False), 
-               ]
-
-outputs_video = [
-    gr.components.Image(type="numpy", label="Output Image"),
-]
-
-interface_video = gr.Interface(
-    fn=show_preds_video,
-    inputs=inputs_video,
-    outputs=outputs_video,
-    title="Security - Trespasser monitoring  ",
-    examples=video_path,
-    cache_examples=False,
-
-)
-
-gr.TabbedInterface(
-    [interface_video],
-    # [interface_image, interface_video],
-    tab_names=['Video inference']
-).queue().launch()