Remove print statements to optimise code

GestureRecognition/HandRecHSV.py

@@ -8,14 +8,15 @@ Created on Thu Nov 22 10:51:21 2018
 import numpy as np
 import cv2
 
-img = cv2.imread('H:\car\GestureRecognition\IMG_0818.png', 1)
+img = cv2.imread('H:\car\GestureRecognition\IMG_0825.jpg', 1)
+img = cv2.imread('H:\car\GestureRecognition\IMG_0818.jpg', 1)
 
 # Downscale the image
 img = cv2.resize(img, None, fx=0.1, fy=0.1, interpolation = cv2.INTER_AREA)
 
 e1 = cv2.getTickCount()
 
-# Hand Localization...
+# Hand Localization... possibly with YOLOv3? v2 is faster though...
 
 
 img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
@@ -27,7 +28,7 @@ img_hsv[:,:,0] = np.where(img_hsv[:,:,0] > 179, img_hsv[:,:,0] - 179, img_hsv[:,
 img_hsv = cv2.GaussianBlur(img_hsv,(5,5),0)
 
 lower_skin = (0, 0, 153)
-upper_skin = (50, 153, 255)
+upper_skin = (45, 153, 255)
 
 # Only need mask, as we can just use this to do the hand segmentation.
 mask = cv2.inRange(img_hsv, lower_skin, upper_skin)
@@ -36,7 +37,8 @@ mask = cv2.inRange(img_hsv, lower_skin, upper_skin)
 blur = cv2.GaussianBlur(mask,(5,5),0)
 ret, img_thresh = cv2.threshold(blur, 50, 255, cv2.THRESH_BINARY)
 
-img_thresh = mask
+# Uncomment if not using blur and threshold.
+# img_thresh = mask
 
 k = np.sum(img_thresh) / 255
 
@@ -76,17 +78,14 @@ for pt in candidate_pts:
     if new_distance > radius:
         radius = new_distance
 
-radius = int(radius * 0.55)
+radius = int(radius * 0.52)
 
 # 140 needs to be replaced with a predicted value. i.e. not be a magic number.
-cv2.circle(img_thresh, centre, 140, (120,0,0), 3)
+# cv2.circle(img_thresh, centre, radius, (120,0,0), 3)
 
 def calc_pos_y(x):
     return int((radius**2 - (x - centre[0])**2)**(1/2) + centre[1])
 
-print(img_thresh.shape)
-print(centre)
-print(radius)
 # Now go around the circle to calculate num of times going 0->255 or vice-versa.
 # First just do it the naive way with loops.
 # Equation of the circle:
@@ -94,15 +93,14 @@ print(radius)
 # Will just increment x to check, no need to loop y as well.
 # This is extremely slow, need to speed it up by removing for loop.
 # Brings speed down to 20 fps.
+# This is actually fast, it was just the print debug statements that made it slow, takes just 6ms...
 # Could try a kerel method? 
 prev_x = centre[0] - radius
 prev_y = [calc_pos_y(centre[0] - radius), calc_pos_y(centre[0] - radius)]
-print(prev_y)
 num_change = 0
 for x in range(centre[0] - radius + 1, centre[0] + radius):
     ypos = calc_pos_y(x)
     y = [ypos, centre[1] - (ypos-centre[1])]
-    print(y)
     if(img_thresh[y[0], x] != img_thresh[prev_y[0], prev_x]):
         num_change += 1
     if img_thresh[y[1], x] != img_thresh[prev_y[1], prev_x] and y[0] != y[1]: