Merge branch 'main' of https://github.com/shravya312/machine-learning-repos

Author: shravya312

OpenCV Projects/ParkingSpaceDetector/main.py

@@ -0,0 +1,57 @@
+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)

Website/css/styles.css

@@ -1246,8 +1246,6 @@ body.dark-mode {
 .dark-mode .milestone-card p{
     color:black;
 }
-
-
 .testimonial-section {
     background-color: #111151;
     padding: 60px 20px;
@@ -1274,6 +1272,7 @@ body.dark-mode {
     border-radius: 10px;
     box-shadow: 0 0 15px rgba(0, 0, 0, 0.5);
     text-align: center;
+    transition: background-color 0.3s, box-shadow 0.3s;
 }
 
 .testimonial img {
@@ -1295,6 +1294,18 @@ body.dark-mode {
     color: #ffd700;
 }
 
+/* Hover effect */
+.testimonial:hover {
+    background-color: #ffd700; /* Change to a contrasting color */
+    color: #111151; /* Change text color */
+    box-shadow: 0 0 20px rgba(255, 215, 0, 0.6); /* Brighter shadow */
+}
+
+.testimonial:hover p,
+.testimonial:hover h3 {
+    color: #111151; /* Ensure text is visible against new background */
+}
+
 /* Light Mode for Testimonial Section */
 .dark-mode .testimonial-section {
     background-color: #f9f9f9;
@@ -1322,6 +1333,18 @@ body.dark-mode {
     border: 2px solid #ddd;
 }
 
+/* Hover effect for dark mode */
+.dark-mode .testimonial:hover {
+    background-color: #555; /* A darker color for light mode */
+    color: #fff;
+    box-shadow: 0 0 20px rgba(0, 0, 0, 0.3);
+}
+
+.dark-mode .testimonial:hover p,
+.dark-mode .testimonial:hover h3 {
+    color: #fff; /* Ensure text remains visible */
+}
+
 body {
     font-family: Arial, sans-serif;
     background-color: #0a0266;