created a new project in opencv projects

OpenCV Projects/ParkingSpaceDetector/main.py

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+import cv2
+import pickle
+import cvzone
+import numpy as np
+
+# Video feed
+cap = cv2.VideoCapture('carPark.mp4')
+
+with open('CarParkPos', 'rb') as f:
+    posList = pickle.load(f)
+
+width, height = 107, 48
+
+
+def checkParkingSpace(imgPro):
+    spaceCounter = 0
+
+    for pos in posList:
+        x, y = pos
+
+        imgCrop = imgPro[y:y + height, x:x + width]
+        # cv2.imshow(str(x * y), imgCrop)
+        count = cv2.countNonZero(imgCrop)
+
+
+        if count < 900:
+            color = (0, 255, 0)
+            thickness = 5
+            spaceCounter += 1
+        else:
+            color = (0, 0, 255)
+            thickness = 2
+
+        cv2.rectangle(img, pos, (pos[0] + width, pos[1] + height), color, thickness)
+        cvzone.putTextRect(img, str(count), (x, y + height - 3), scale=1,
+                           thickness=2, offset=0, colorR=color)
+
+    cvzone.putTextRect(img, f'Free: {spaceCounter}/{len(posList)}', (100, 50), scale=3,
+                           thickness=5, offset=20, colorR=(0,200,0))
+while True:
+
+    if cap.get(cv2.CAP_PROP_POS_FRAMES) == cap.get(cv2.CAP_PROP_FRAME_COUNT):
+        cap.set(cv2.CAP_PROP_POS_FRAMES, 0)
+    success, img = cap.read()
+    imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    imgBlur = cv2.GaussianBlur(imgGray, (3, 3), 1)
+    imgThreshold = cv2.adaptiveThreshold(imgBlur, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
+                                         cv2.THRESH_BINARY_INV, 25, 16)
+    imgMedian = cv2.medianBlur(imgThreshold, 5)
+    kernel = np.ones((3, 3), np.uint8)
+    imgDilate = cv2.dilate(imgMedian, kernel, iterations=1)
+
+    checkParkingSpace(imgDilate)
+    cv2.imshow("Image", img)
+    # cv2.imshow("ImageBlur", imgBlur)
+    # cv2.imshow("ImageThres", imgMedian)
+    cv2.waitKey(10)