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import gradio as gr |
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import cv2 |
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import requests |
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import os |
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import torch |
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import numpy as np |
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from ultralytics import YOLO |
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model = torch.hub.load('ultralytics/yolov5', 'hi', pretrained=True) |
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area = [(48,430), (18, 515), (407,485), (750,425), (690,370)] |
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total_space = 12 |
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count=0 |
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def show_preds_video(): |
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cap = cv2.VideoCapture('V111.mp4') |
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count=0 |
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while(cap.isOpened()): |
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ret, frame = cap.read() |
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if not ret: |
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break |
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count += 1 |
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if count % 2 != 0: |
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continue |
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frame=cv2.resize(frame,(1020,600)) |
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frame_copy = frame.copy() |
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Vehicle_cnt = 0 |
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results=model(frame) |
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for index, row in results.pandas().xyxy[0].iterrows(): |
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x1 = int(row['xmin']) |
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y1 = int(row['ymin']) |
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x2 = int(row['xmax']) |
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y2 = int(row['ymax']) |
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d=(row['name']) |
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cx=int(x1+x2)//2 |
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cy=int(y1+y2)//2 |
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if ('car' or 'truck') in d: |
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results = cv2.pointPolygonTest(np.array(area, np.int32), ((cx,cy)), False) |
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if results >0: |
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cv2.rectangle(frame_copy,(x1,y1),(x2,y2),(0,0,255),2) |
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cv2.putText(frame_copy,str(d),(x1,y1),cv2.FONT_HERSHEY_PLAIN,2,(255,255,0),2) |
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Vehicle_cnt += 1 |
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free_space = total_space - Vehicle_cnt |
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cv2.putText(frame_copy, ("Free space: " + str(free_space)), (50,50) ,cv2.FONT_HERSHEY_PLAIN,2,(0,255,0),2) |
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cv2.putText(frame_copy, str(str("vehicles: ")+ str(Vehicle_cnt) ), (50,85) ,cv2.FONT_HERSHEY_PLAIN,2,(0,255,0),2) |
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cv2.polylines(frame_copy, [np.array(area, np.int32)], True, (0,255,0), 2) |
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yield cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB) |
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inputs_video = [ |
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] |
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outputs_video = [ |
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gr.components.Image(type="numpy", label="Output Image"), |
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] |
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interface_video = gr.Interface( |
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fn=show_preds_video, |
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inputs=inputs_video, |
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outputs=outputs_video, |
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title="Parking counter", |
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description="Click submit !!!'", |
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cache_examples=False, |
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) |
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gr.TabbedInterface( |
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[interface_video], |
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tab_names=['Video inference'] |
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).queue().launch() |
