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.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Video_1.mp4 filter=lfs diff=lfs merge=lfs -text

Video_1.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cca303b2cea37a6a850ff933cf95e449210cd6101ab7446bb4eaa51124dd42f1
+size 12962985

app.py

@@ -0,0 +1,89 @@
+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)
+path  = [['image_0.jpg'], ['image_1.jpg']]
+video_path = [['video_test.mp4']]
+# area = [(25,430), (10, 515), (407,485), (750,425), (690,370)]
+area = [(48,430), (18, 515), (407,485), (750,425), (690,370)]
+total_space = 12
+count=0
+
+def show_preds_video():
+    cap = cv2.VideoCapture('Video_1.mp4')
+    count=0
+    while(cap.isOpened()):
+        ret, frame = cap.read()
+        if not ret:
+            break
+        count += 1
+        if count % 2 != 0:
+            continue
+            # frame = cv2.imread(video_path)
+        frame=cv2.resize(frame,(1020,600))
+        frame_copy = frame.copy()
+        car_cnt = 0
+        truck_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') 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)
+                        car_cnt += 1
+
+            elif ('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,(255,0,0),2)
+                    truck_cnt += 1       
+        
+        free_space = total_space - (car_cnt + truck_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("  car: ")+ str(car_cnt) + str("  truck: ") +str(truck_cnt)), (50,75) ,cv2.FONT_HERSHEY_PLAIN,2,(0,255,0),2)
+        
+        cv2.polylines(frame_copy, [np.array(area, np.int32)], True, (0,255,0), 2) 
+
+        fps = cap.get(cv2.CAP_PROP_FPS)
+        cv2.putText(frame_copy,str("fps:  ") + str(np.round(fps,0)),(50,100),cv2.FONT_HERSHEY_PLAIN,2,(0,255,0),2)
+
+        yield cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB)
+
+
+inputs_video = [
+    #gr.components.Video(type="filepath", label="Input 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 space counter",
+    # examples=video_path,
+    cache_examples=False,
+)
+
+gr.TabbedInterface(
+    [interface_video],
+    tab_names=['Video inference']
+).queue().launch()

requirements.txt

@@ -0,0 +1,8 @@
+torch
+gradio==3.4.0
+opencv-python
+numpy<1.24
+ultralytics
+yolov5
+
+

yolov5l.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2f603b7354c25454d1270663a14d8ddc1eea98e5eebc1d84ce0c6e3150fa155f
+size 93622629