Add 'car/' from commit 'eee0e8dc445691e600680f4abc77f2814b20b054'

git-subtree-dir: car
git-subtree-mainline: 1d29a5526c
git-subtree-split: eee0e8dc44

car/GestureRecognition/HandRecHSV.py

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+# -*- 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_0825.jpg', 1)
+# 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)
+
+e1 = cv2.getTickCount()
+
+# Hand Localization... possibly with YOLOv3? v2 is faster though...
+
+
+img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+
+# Need to shift red pixels so they can be 0-20 rather than 250-~20
+img_hsv[:,:,0] = img_hsv[:,:,0] + 30
+img_hsv[:,:,0] = np.where(img_hsv[:,:,0] > 179, img_hsv[:,:,0] - 179, img_hsv[:,:,0])
+
+img_hsv = cv2.GaussianBlur(img_hsv,(5,5),0)
+
+lower_skin = (0, 0, 153)
+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)
+
+# This takes a whole millisecond (approx), and does not seem very worth the cost. 
+blur = cv2.GaussianBlur(mask,(5,5),0)
+ret, img_thresh = cv2.threshold(blur, 50, 255, cv2.THRESH_BINARY)
+
+# Uncomment if not using blur and threshold.
+# img_thresh = mask
+
+k = np.sum(img_thresh) / 255
+
+# 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_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
+
+
+# Even this is extremely quick as it goes through rows in the numpy array, which in python is much faster than columns
+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))
+
+# Calculate radius of circle:
+# May need to calculate diameter as well.
+# Just take min/max x values and y values 
+x_min = np.min(coords_x[img_thresh == 255])
+x_max = np.max(coords_x[img_thresh == 255])
+y_min = np.min(coords_y[img_thresh == 255])
+y_max = np.max(coords_y[img_thresh == 255])
+
+candidate_pts = [(x_min, y_min), (x_min, y_max), (x_max, y_min), (x_max, y_max)]
+radius = 0
+
+# Check with each point to see which is furthest from the centre.
+for pt in candidate_pts:
+    # Calculate Euclydian Distance
+    new_distance = ((pt[0] - centre[0])**2 + (pt[1] - centre[1])**2)**(1/2)
+    if new_distance > radius:
+        radius = new_distance
+
+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, radius, (120,0,0), 3)
+
+def calc_pos_y(x):
+    return int((radius**2 - (x - centre[0])**2)**(1/2) + centre[1])
+
+# 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:
+# y = sqrt(r2 - (x-c)2) + c
+# 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)]
+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])]
+    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]:
+        num_change += 1
+    prev_x = x
+    prev_y = y
+
+fingers = num_change / 2 - 1
+
+print("Num Fingers: " + str(fingers))
+
+e2 = cv2.getTickCount()
+t = (e2 - e1)/cv2.getTickFrequency()
+print( t )
+
+cv2.imshow("Threshold", img_thresh)
+cv2.waitKey(0)
+cv2.destroyAllWindows()