Clean up project directory.

GestureRecognition/HandRecGray.py

@@ -1,79 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Thu Nov 22 14:16:46 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)
-
-min_seg_threshold = 1.2
-max_seg_threshold = 1.8
-
-# Need to make this get correct skin tones. 
-# img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-# img_gray[img_gray[:,:] > 90] = 255
-# img_gray[img_gray[:,:] < 90] = 0
-
-img_bin = np.zeros(shape=(img.shape[0], img.shape[1]), dtype=int)
-img = 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]
-
-# Threshold to binary.
-ret,img_thresh = cv2.threshold(img_bin, 127, 255, cv2.THRESH_BINARY)
-
-# 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
-
-# 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
-
-
-# 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))
-
-#x,y,k,xb,yb = 0,0,0,0,0
-#
-## 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))
-
-
-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)
-cv2.destroyAllWindows()

GestureRecognition/HandRecognition.py

@@ -1,34 +0,0 @@
-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)
-
-min_seg_threshold = 1.2
-max_seg_threshold = 3
-
-# 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.

GestureRecognition/SimpleHandRecogniser.py

@@ -30,7 +30,7 @@ class SimpleHandRecogniser(HandRecogniser):
 
         # Apply another blur to rmeove any small holes/noise
         self.img_cut = self.__denoise(self.img_cut)
-        ret, self.img_cut = cv2.threshold(self.img_cut, 50, 255, cv2.THRESH_BINARY)
+        _, self.img_cut = cv2.threshold(self.img_cut, 50, 255, cv2.THRESH_BINARY)
 
     def __denoise(self, image):
         """