Make segmentation more efficient.

GestureRecognition/HandRecGray.py

@@ -21,41 +21,51 @@ img_gray[img_gray[:,:] < 90] = 0
 # Threshold to binary.
 ret,img_thresh = cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY)
 
-# Doesn't take too long.
+# Following method is much faster -> 0.00143s
+# Still want to speed up further by lowering reliance on memory, which is quite heavy..
 k = np.sum(img_thresh) / 255
 
-x_ind = np.indices(img_thresh.shape[1])
-coords = np.zeros(img_thresh.shape)
+# Taking indices for num of rows.
+x_ind = np.arange(0,img_thresh.shape[1])
+y_ind = np.arange(0,img_thresh.shape[0])
+coords = np.zeros((img_thresh.shape[0], img_thresh.shape[1], 2))
+coords_x = np.zeros((img_thresh.shape[0], img_thresh.shape[1]))
+coords_y = np.zeros((img_thresh.shape[0], img_thresh.shape[1]))
+coords_x[:,:] = x_ind
 
 
-# generate individual coordinates for x then transpose the matrix o
+# Even this is extremely quick as it goes through rows in the numpy array.
+for element in y_ind:
+    coords_y[element,:] = element
+    
+# Now need to get the average x value and y value for centre of gravity
+xb = int(np.sum(coords_x[img_thresh == 255])/k)
+yb = int(np.sum(coords_y[img_thresh == 255])/k)
+
+centre = (int(np.sum(coords_x[img_thresh == 255])/k), int(np.sum(coords_y[img_thresh == 255])/k))
+
+#x,y,k,xb,yb = 0,0,0,0,0
 #
-# First sum x coordinates.
-#xb = int(img_ind[img_thresh == 255].sum(axis=1).sum()/k)
-#print(xb)
-# Then sum y coordinates
-#yb = int(img_ind[img_thresh == 255].sum(axis=0).sum()/k)
-#print(yb)
+## this is inherently slow...like very very slow...0.114s
+#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))
 
-x,y,k,xb,yb = 0,0,0,0,0
-
-# this is inherently slow...like very very 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))
 
 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.
+# First get equation of the circle:
+# y = sart(r2 - (x-c)2 + c)
 
 cv2.imshow("Binary-cot-out", img_thresh)
 cv2.waitKey(0)