| 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', 'yolov5l', pretrained=True) |
|
|
| area = [(48,430), (18, 515), (407,485), (750,425), (690,370)] |
| total_space = 12 |
| count=0 |
|
|
| def show_preds_video(): |
| cap = cv2.VideoCapture('V1.mp4') |
| count=0 |
| while(cap.isOpened()): |
| ret, frame = cap.read() |
| if not ret: |
| break |
| count += 1 |
| if count % 2 != 0: |
| continue |
|
|
| frame=cv2.resize(frame,(1020,600)) |
| frame_copy = frame.copy() |
| Vehicle_cnt = 0 |
|
|
| 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 ('car' or 'truck') in d: |
| results = cv2.pointPolygonTest(np.array(area, np.int32), ((cx,cy)), 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_PLAIN,2,(255,255,0),2) |
| Vehicle_cnt += 1 |
|
|
|
|
| |
| free_space = total_space - Vehicle_cnt |
| cv2.putText(frame_copy, ("Free space: " + str(free_space)), (50,50) ,cv2.FONT_HERSHEY_PLAIN,2,(0,255,0),2) |
|
|
| cv2.putText(frame_copy, str(str("vehicles: ")+ str(Vehicle_cnt) ), (50,85) ,cv2.FONT_HERSHEY_PLAIN,2,(0,255,0),2) |
| |
| cv2.polylines(frame_copy, [np.array(area, np.int32)], True, (0,255,0), 2) |
|
|
| yield cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB) |
|
|
|
|
| inputs_video = [ |
| |
|
|
| ] |
| 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="Parking counter", |
| description="Click submit !!!'", |
| |
| cache_examples=False, |
| ) |
|
|
| gr.TabbedInterface( |
| [interface_video], |
| tab_names=['Video inference'] |
| ).queue().launch() |
