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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 % 10 != 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() | |