Update to working greyscale recognition

GestureRecognition/HandRecHSV.py

@@ -0,0 +1,61 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Nov 22 10:51:21 2018
+
+@author: pivatom
+"""
+
+import numpy as np
+import cv2
+
+img = cv2.imread('H:\car\GestureRecognition\IMG_0818.png', 1)
+
+# Downscale the image
+img = cv2.resize(img, None, fx=0.1, fy=0.1, interpolation = cv2.INTER_AREA)
+
+img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+img_gray[img_gray[:,:] > 90] = 255
+img_gray[img_gray[:,:] < 90] = 0
+
+# Threshold to binary.
+ret,img_thresh = cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY)
+
+x,y,k,xb,yb = 0,0,0,0,0
+
+# this is inherently slow...
+for pix in img_thresh:
+    for j in pix:
+        if j == 255:
+            k += 1
+            xb += x
+            yb += y
+        x += 1
+    y += 1
+    x = 0
+    
+centre = (int(xb/k), int(yb/k))
+print(centre)
+
+cv2.rectangle(img_thresh, centre, (centre[0] + 20, centre[1] + 20), (0,0,255), 3)
+cv2.circle(img_thresh, centre, 140, (0,0,0), 3)
+
+# Now need to trace around the circle to figure out where the fingers are.
+
+cv2.imshow("Binary-cot-out", img_thresh)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
+
+
+
+#img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+
+#lower_skin = np.array([2, 102, 153])
+#upper_skin = np.array([7.5, 153, 255])
+#
+## Only need mask, as we can just use this to calculate the 
+#mask = cv2.inRange(img_hsv, lower_skin, upper_skin)
+#
+#cv2.imshow("Mask", mask)
+#cv2.waitKey(0)
+#cv2.destroyAllWindows()