Update to working greyscale recognition

GestureRecognition/HandRecognition.py

@@ -1,15 +1,34 @@
-from PIL import Image
-from PIL import ImageDraw
+import numpy as np
+import cv2
 
-img = Image.open('/Users/piv/Desktop/IMG_0818.png')
+img = cv2.imread('H:\car\GestureRecognition\IMG_0818.png', 1)
 
-# Create a new image of the cutout.
-blkimg = Image.new('1', (img.width, img.height)
-blkdraw = ImageDraw.Draw(blkimg)
+# Downscale the image
+img = cv2.resize(img, None, fx=0.1, fy=0.1, interpolation = cv2.INTER_AREA)
 
-for i in range(1, img.width):
-	for j in range(1, img.height):
-		# getpixel returns tuple (r,g,b,a)
-		pixel = img.getpixel((i, j))
-		if (pixel[0]/pixel[1]) > 1.05 and (pixel[0]/pixel[1]) < 4:
+min_seg_threshold = 1.2
+max_seg_threshold = 1.8
 
+# prevent divide by zero, by just forcing pixel to be ignored.
+np.where(img[:,:,1] == 0, 0, img[:,:,1])
+img[(img[:,:,2]/img[:,:,1] > min_seg_threshold) & (img[:,:,2]/img[:,:,1] < max_seg_threshold)] = [255,255,255]
+
+# Try removing image noise.
+#img = cv2.fastNlMeansDenoising(img)
+
+cv2.imshow('image', img)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
+
+# Remove non-hand parts
+
+# Find centre of the hand
+# Hand parts are white pixels.
+# Find sum of each col/row to find the left/rightmost and top/bottommost white pixels.
+# Have used a for loop but obviously that is going to be slow.
+
+# Draw appropriate circle
+
+# Calculate number of different peaks.
+# Article just traced around the circle and counted number of times switched from
+# zero to one.