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Copy pathrough.py
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rough.py
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# -*- coding: utf-8 -*-
"""
Created on Sun Nov 25 01:36:48 2018
@author: NaSiF
"""
# import the necessary packages
from sklearn.svm import SVC
import cv2
import numpy as np
import glob
from scipy import misc
import matplotlib.pyplot as plt
def get_pixel(img, center, x, y):
new_value = 0
try:
if img[x][y] >= center:
new_value = 1
except:
pass
return new_value
def lbp_calculated_pixel(img, x, y):
'''
64 | 128 | 1
----------------
32 | 0 | 2
----------------
16 | 8 | 4
'''
center = img[x][y]
val_ar = []
val_ar.append(get_pixel(img, center, x-1, y+1)) # top_right
val_ar.append(get_pixel(img, center, x, y+1)) # right
val_ar.append(get_pixel(img, center, x+1, y+1)) # bottom_right
val_ar.append(get_pixel(img, center, x+1, y)) # bottom
val_ar.append(get_pixel(img, center, x+1, y-1)) # bottom_left
val_ar.append(get_pixel(img, center, x, y-1)) # left
val_ar.append(get_pixel(img, center, x-1, y-1)) # top_left
val_ar.append(get_pixel(img, center, x-1, y)) # top
power_val = [1, 2, 4, 8, 16, 32, 64, 128]
val = 0
for i in range(len(val_ar)):
val += val_ar[i] * power_val[i]
return val
def show_output(output_list):
output_list_len = len(output_list)
figure = plt.figure()
for i in range(output_list_len):
current_dict = output_list[i]
current_img = current_dict["img"]
current_xlabel = current_dict["xlabel"]
current_ylabel = current_dict["ylabel"]
current_xtick = current_dict["xtick"]
current_ytick = current_dict["ytick"]
current_title = current_dict["title"]
current_type = current_dict["type"]
current_plot = figure.add_subplot(1, output_list_len, i+1)
if current_type == "gray":
current_plot.imshow(current_img, cmap = plt.get_cmap('gray'))
current_plot.set_title(current_title)
current_plot.set_xticks(current_xtick)
current_plot.set_yticks(current_ytick)
current_plot.set_xlabel(current_xlabel)
current_plot.set_ylabel(current_ylabel)
elif current_type == "histogram":
current_plot.plot(current_img, color = "black")
current_plot.set_xlim([0,260])
current_plot.set_ylim([0,10000])
current_plot.set_title(current_title)
current_plot.set_xlabel(current_xlabel)
current_plot.set_ylabel(current_ylabel)
ytick_list = [int(i) for i in current_plot.get_yticks()]
current_plot.set_yticklabels(ytick_list,rotation = 90)
plt.show()
data=[]
labels=[]
def main():
n=0
while 1 :
n=n+1
filename = input("Enter the file name in which images are present = ")
for img in glob.glob(filename+'/*.*'):
try :
img_rgb = misc.imresize(cv2.imread(img),(256,256))
height, width, channel = img_rgb.shape
img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)
img_lbp = np.zeros((height, width,3), np.uint8)
for i in range(0, height):
for j in range(0, width):
img_lbp[i, j] = lbp_calculated_pixel(img_gray, i, j)
hist_lbp = cv2.calcHist([img_lbp], [0], None, [256], [0, 256])
output_list = []
output_list.append({
"img": img_gray,
"xlabel": "",
"ylabel": "",
"xtick": [],
"ytick": [],
"title": "Gray Image",
"type": "gray"
})
output_list.append({
"img": img_lbp,
"xlabel": "",
"ylabel": "",
"xtick": [],
"ytick": [],
"title": "LBP Image",
"type": "gray"
})
output_list.append({
"img": hist_lbp,
"xlabel": "Bins",
"ylabel": "Number of pixels",
"xtick": None,
"ytick": None,
"title": "Histogram(LBP)",
"type": "histogram"
})
data.append(hist_lbp.ravel())
labels.append(n)
show_output(output_list)
except Exception as e:
print (e)
user_input = input("do you want to read another folder = ")
if user_input == 'no':
break
print(data)
clf = SVC(gamma = 0.00001, C=100)
clf.fit(data,labels)
print('\ndata:' , data)
print('\nlabels:' , labels)
while 1:
im=str(input('Enter Image name: '))
img_rgb = misc.imresize(cv2.imread(im),(256,256))
height, width, channel = img_rgb.shape
img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)
img_lbp = np.zeros((height, width,3), np.uint8)
for i in range(0, height):
for j in range(0, width):
img_lbp[i, j] = lbp_calculated_pixel(img_gray, i, j)
hist_lbp = cv2.calcHist([img_lbp], [0], None, [256], [0, 256])
print('Prediction:',clf.predict(hist_lbp.reshape(1,-1)))
if __name__ == '__main__':
main()