def canny(imgray):
# zamagljivanje slike pomoćću Gaussian filtera, korisno za uklanjanje šuma
# zapravo uklnja visoke frekvencije (šumove, ivice) sa slike
img_gaussian = cv2.GaussianBlur(imgray, (5,5), 200)
my.display_image('3. gaussian', img_gaussian)
canny_low = 50 #5
canny_high = 200 #150
# pronalazi ivice na slici pomoću Canny algoritma
# pomoću 1. i 2. threshold-a za histesisnu proceduru
img_canny = cv2.Canny(img_gaussian,canny_low,canny_high)
my.display_image('4. canny', img_canny)
return img_canny
# -*- coding: utf-8 -*-
"""
Created on Wed Jun 22 16:38:27 2016
@author: Milos
"""
import notebookOperacije as my
# TODO - IMAGE PROCESSING
# ucitavanje digitalne slike
img_color = my.load_image('images/stop15.jpg')
my.plt.figure(1)
my.display_image('Ucitavanje digitalne slike', img_color)
# formiranje grayscale slike
img_grayscale = my.image_gray((img_color))
my.plt.figure(2)
my.display_image('Formiranje grayscale slike', img_grayscale)
# formiranje binarne slike
img_bin = my.image_bin_adaptive(img_grayscale)
my.plt.figure(3)
my.display_image('Formiranje binarne slike', img_bin)
# uklanjanje šuma sa binarne slike
img_no_noise = my.remove_noiseColor(img_bin)
my.plt.figure(4)
my.display_image('img_bin sa remove_noise', img_no_noise)
# pronalaženje regiona od interesa
Created on Sun Jun 26 18:01:03 2016
@author: Milos
"""
import notebookOperacije as my
import cv2
import numpy as np
# TODO - IMAGE PROCESSING
img_path = 'images/obucavajuciSkup13.jpg'
# ucitavanje digitalne slike
img_color = my.load_image(img_path)
my.plt.figure(1)
my.display_image('TRAIN_Ucitavanje digitalne slike', img_color)
# ucitavanje hsv slikes
img_hsv = my.load_imageHSV(img_path)
my.plt.figure(5)
my.display_image('TRAIN_img_hsv', img_hsv)
# define range of blue color in HSV
lower_blue = np.array([100,50,50])
upper_blue = np.array([130,255,255])
mask_blue1 = cv2.inRange(img_hsv, lower_blue, upper_blue)
# define range of red color in HSV
lower_red = np.array([0,50,50])
upper_red = np.array([10,255,255])
mask_red1 = cv2.inRange(img_hsv, lower_red, upper_red)
Created on Fri Jun 24 15:30:21 2016
@author: Milos
"""
import notebookOperacije as my
import neuralNetwork as nn
import numpy as np
# TODO - NEURAL NETWORK TRAIN
# obučavanje neuronske mreže
image_original_obucavanje = my.load_image('images/obucavajuciSkup12.jpg')
image_obucavanje = my.remove_noise(my.image_bin(my.image_gray(image_original_obucavanje)))
img_selected_regions_obucavanje, regions_obucavanje, regions_color_obucavanje, regions_signs_obucavnje = my.select_roiV3(image_original_obucavanje.copy(), image_obucavanje, 'TRAIN')
my.plt.figure(3)
my.display_image('img_selected_regions_obucavanje', img_selected_regions_obucavanje)
inputs_obucavanje = nn.prepare_for_ann(regions_obucavanje)
'''
#signs_alphabet = ['Obavezno zaustavljanje', 'Zabrana saobraćaja u oba smera']
signs_alphabet = {}
for x in range(0, 8):
signs_alphabet[x] = str((x+1)*10)
#print "We're on time %d" % (signs_alphabet[x])
'''
signs_alphabet = ['opasnost na putu', 'ogranicenje 30', 'obrnuti trougao', 'stop', 'zabrenjo oba smera', 'ogranicenje 40', 'zabranjeno polukruzno', 'obavezan smer desno', 'pesacki prelaz']
outputs_obucavanje = nn.convert_output(signs_alphabet)
print '\nlen(inputs_obucavanje)=', len(inputs_obucavanje), ' len(outputs_obucavanje)=', len(outputs_obucavanje)
ann = nn.create_ann()
ann = nn.train_ann(ann, inputs_obucavanje, outputs_obucavanje)
upper_red = np.array([180,255,255])
mask1 = cv2.inRange(img_hsv, lower_red, upper_red)
# define range of blue color in HSV
lower_blue = np.array([110,50,50])
upper_blue = np.array([130,255,255])
mask2 = cv2.inRange(img_hsv, lower_blue, upper_blue)
# define range of blue color in HSV
lower_white = np.array([20,100,100])
upper_white = np.array([40,255,255])
mask3 = cv2.inRange(img_hsv, lower_white, upper_white)
# join my masks
mask = mask0+mask1+mask2+mask3
# set my output img to zero everywhere except my mask
output_img = img.copy()
output_img[np.where(mask==0)] = 0
# or your HSV image, which I *believe* is what you want
output_hsv = img_hsv.copy()
output_hsv[np.where(mask==0)] = 0
my.display_image('output_hsv', output_hsv)
white = np.uint8([[[0,0,0 ]]])
hsv_green = cv2.cvtColor(white,cv2.COLOR_BGR2HSV)
print hsv_green
h = img_color.shape[0]/3
print 'h=',h
# računnje i iscrtravanje Hough-ovih linija
for i in range(0,3):
for rho,theta in lines[i]:
a = np.cos(theta)
b = np.sin(theta)
x0 = a*rho
y0 = b*rho
x1 = int(x0 + 1000*(-b))
y1 = int(y0 + 1000*(a))
x2 = int(x0 - 1000*(-b))
y2 = int(y0 - 1000*(a))
cv2.line(img_result,(x1,y1),(x2,y2),(255,0,0),5)
#*****************************************************
# TODO - run
img_color = cv2.imread('images_test/put11.jpg')
img_result = img_color.copy()
img_gray = cv2.cvtColor(img_color, cv2.COLOR_BGR2GRAY)
filtering(img_gray)
my.display_image('1. original', img_color)
my.display_image('2. gray', img_gray)
my.display_image('5. result', img_result)