baseline

main.py

@@ -1,37 +1,42 @@
 import cv2
 import numpy as np
+from mobile_sam import sam_model_registry, SamAutomaticMaskGenerator, SamPredictor
+import torch 
 
+model_type = "vit_t"
+sam_checkpoint = "./models/mobile_sam.pt"
+
+
+backSub = cv2.createBackgroundSubtractorMOG2(varThreshold=16 )
 # Load the image
-image = cv2.imread('dirty.jpeg')
-gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+refimage = cv2.imread('clean2.jpeg')
+image = cv2.imread('dirty2.jpeg')
 
-# Apply Gaussian blur to reduce noise
-gray = cv2.GaussianBlur(gray, (9, 9), 2)
+res = cv2.absdiff(refimage, image)
 
-# Detect circles using the Hough Circles method
-circles = cv2.HoughCircles(
-    gray,
-    cv2.HOUGH_GRADIENT,
-    dp=1,  # Inverse ratio of accumulator resolution to image resolution
-    minDist=20,  # Minimum distance between the centers of detected circles
-    param1=50,  # Higher threshold for edge detection
-    param2=30,  # Accumulator threshold for circle detection
-    minRadius=10,  # Minimum radius
-    maxRadius=100  # Maximum radius
-)
+res = res.astype(np.uint8)
 
-if circles is not None:
-    circles = np.uint16(np.around(circles))
-    
-    for circle in circles[0, :]:
-        center = (circle[0], circle[1])
-        radius = circle[2]
-        
-        # Draw the circle and its center
-        cv2.circle(image, center, radius, (0, 255, 0), 2)
-        cv2.circle(image, center, 2, (0, 0, 255), 3)
+percentage = (np.count_nonzero(res) * 100)/ res.size
 
-# Display the result
-cv2.imshow('Hough Circles', image)
-cv2.waitKey(0)
-cv2.destroyAllWindows()
+print(percentage)
+#cv2.imwrite('/var/www/html/images/blurred.jpg', blurred)
+
+
+print("Converting to grey")
+gray_refimage = cv2.cvtColor(refimage, cv2.COLOR_BGR2GRAY)
+gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+
+blurred_refimage = cv2.GaussianBlur(gray_refimage, (9, 9), 2, 2)
+blurred_image = cv2.GaussianBlur(gray_image, (9, 9), 2, 2)
+
+#cv2.imwrite('/var/www/html/images/gray.jpg', gray)
+maska = backSub.apply(blurred_refimage)
+maskb = backSub.apply(blurred_image)
+
+cv2.imshow("maskb",maskb)
+
+cv2.waitKey(0) 
+  
+# closing all open windows 
+cv2.destroyAllWindows()