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app.py

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+
+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()