Upload 6 files
Browse files- .gitattributes +1 -0
- V1.mp4 +3 -0
- app.py +80 -0
- hi.pt +3 -0
- requirements.txt +8 -0
- yolov5l.pt +3 -0
- yolov5n.pt +3 -0
.gitattributes
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@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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V1.mp4 filter=lfs diff=lfs merge=lfs -text
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V1.mp4
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version https://git-lfs.github.com/spec/v1
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oid sha256:cca303b2cea37a6a850ff933cf95e449210cd6101ab7446bb4eaa51124dd42f1
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size 12962985
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app.py
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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', 'yolov5l', 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('V1.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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#gr.components.Video(type="filepath", label="Input 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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#examples=video_path,
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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()
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hi.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:6421d3f2ffb28969f96f3db64a407fe83ae49f2345dfdc85540fecc5b17aa769
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size 3780605
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requirements.txt
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torch
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gradio==3.4.0
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opencv-python
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numpy<1.24
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ultralytics
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yolov5
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yolov5l.pt
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version https://git-lfs.github.com/spec/v1
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+
oid sha256:2f603b7354c25454d1270663a14d8ddc1eea98e5eebc1d84ce0c6e3150fa155f
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size 93622629
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yolov5n.pt
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:4f180cf23ba0717ada0badd6c685026d73d48f184d00fc159c2641284b2ac0a3
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size 4062133
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