changed when the input img is turned to greyscale

detector.py

@@ -11,7 +11,8 @@ def chop_img(img, step):
             # crop the image into step*rows and step*columns
             imgCrop = img[int(0 + height / step * i): int(height / step + height / step * i),
                       int(0 + width / step * j): int(width / step + width / step * j)]
-            imgResize = cv.resize(imgCrop, (640, 512))  # Resize image
+            imgGray = cv.cvtColor(imgCrop, cv.COLOR_BGR2GRAY)
+            imgResize = cv.resize(imgGray, (640, 512))  # Resize image
             imgAug = augment(imgResize)
             allImgs.append((imgResize, imgAug))
             j += 1
@@ -20,8 +21,7 @@ def chop_img(img, step):
 
 def augment(img):
     kernel = np.ones((5, 5), np.uint8)
-    imgGray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)  # convert to grayscale
-    imgBlur = cv.GaussianBlur(imgGray, (5, 5), 0)  # apply gaussian blur
+    imgBlur = cv.GaussianBlur(img, (5, 5), 0)  # apply gaussian blur
     imgThresh = cv.adaptiveThreshold(  # apply adaptive threshold
     imgBlur, 255, cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY_INV, 7, 5)
     imgDilation = cv.dilate(imgThresh, kernel, iterations=1)  # apply dilation to amplify threshold result