# -*- coding: utf-8 -*-

"""

Created on Sun Jun 19 01:56:14 2016

@author: Sailesh Mohanty

"""

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

import sklearn

import glob, os

import seaborn as sns

sns.set(style="whitegrid", color_codes=True)

import matplotlib as mpl

import matplotlib.pyplot as plt

from subprocess import check_output

from skimage.transform import resize

from skimage import io

from skimage.color import rgb2gray

from skimage import data, img_as_float

from skimage import exposure

from skimage import color

from scipy import ndimage as ndi

from skimage import feature

from skimage.filters import roberts, sobel, scharr, prewitt

from skimage.filters import gaussian

from skimage.segmentation import active_contour

from matplotlib import cm

from skimage.transform import (hough_line, hough_line_peaks,

probabilistic_hough_line)

from skimage.feature import canny

from skimage.feature import daisy

from skimage import transform as tf

from skimage.feature import CENSURE

mpl.rcParams['font.size'] = 8

def colorize(image, hue, saturation=1):

return color.hsv2rgb(hsv)

def plot_img_and_hist(img, axes, bins=256):

return ax_img, ax_hist, ax_cdf

train = pd.read_csv('/media/sajal/47DE7E8B160B07C9/dsg/id_train.csv')

train_id = train['Id'].astype(str)

train['label'] = train['label'].astype(str)

train['length'] = 0

train['breadth'] = 0

'''

for i in range(len(train_id)):

img_name = "DSG/train_resize/"+train_id.iloc[i]+".jpg"

img = plt.imread(img_name)

dim = np.shape(img)

train['length'][i] = dim[0]

train['breadth'][i] = dim[1]

'''

print("1")

img_name = io.imread("DSG/train_resize/"+train_id.iloc[5000]+".jpg")

img_name_gs = rgb2gray(img_name)

plt.imshow(img_name)

plt.imshow(img_name_gs)

# Load an example image

img = img_name_gs

# Contrast stretching

p2, p98 = np.percentile(img, (2, 98))

img_rescale = exposure.rescale_intensity(img, in_range=(p2, p98))

# Equalization

img_eq = exposure.equalize_hist(img)

# Adaptive Equalization

img_adapteq = exposure.equalize_adapthist(img, clip_limit=0.03)

# Display results

fig = plt.figure(figsize=(8, 5))

axes = np.zeros((2,4), dtype=np.object)

axes[0,0] = fig.add_subplot(2, 4, 1)

for i in range(1,4):

axes[0,i] = fig.add_subplot(2, 4, 1+i, sharex=axes[0,0], sharey=axes[0,0])

for i in range(0,4):

axes[1,i] = fig.add_subplot(2, 4, 5+i)

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img, axes[:, 0])

ax_img.set_title('Low contrast image')

y_min, y_max = ax_hist.get_ylim()

ax_hist.set_ylabel('Number of pixels')

ax_hist.set_yticks(np.linspace(0, y_max, 5))

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_rescale, axes[:, 1])

ax_img.set_title('Contrast stretching')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_eq, axes[:, 2])

ax_img.set_title('Histogram equalization')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_adapteq, axes[:, 3])

ax_img.set_title('Adaptive equalization')

ax_cdf.set_ylabel('Fraction of total intensity')

ax_cdf.set_yticks(np.linspace(0, 1, 5))

# prevent overlap of y-axis labels

fig.tight_layout()

plt.show()

hue_rotations = np.linspace(0, 1, 6)

fig, axes = plt.subplots(nrows=2, ncols=3, sharex=True, sharey=True)

for ax, hue in zip(axes.flat, hue_rotations):

# Turn down th e saturation to give it that vintage look.

tinted_image = colorize(img_name, hue, saturation=0.3)

ax.imshow(tinted_image, vmin=0, vmax=1)

ax.set_axis_off()

ax.set_adjustable('box-forced')

fig.tight_layout()

# Compute the Canny filter for two values of sigma

edges1 = feature.canny(img_name_gs)

edges2 = feature.canny(img_name_gs, sigma=3.2)

# display results

fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3, figsize=(8, 3),

sharex=True, sharey=True)

ax1.imshow(img_name_gs, cmap=plt.cm.gray)

ax1.axis('off')

ax1.set_title('noisy image', fontsize=20)

ax2.imshow(edges1, cmap=plt.cm.gray)

ax2.axis('off')

ax2.set_title('Canny filter, $\sigma=1$', fontsize=20)

ax3.imshow(edges2, cmap=plt.cm.gray)

ax3.axis('off')

ax3.set_title('Canny filter, $\sigma=3$', fontsize=20)

fig.tight_layout()

plt.show()

edge_roberts = roberts(img_name_gs)

edge_sobel = sobel(img_name_gs)

fig, (ax0, ax1) = plt.subplots(ncols=2, sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})

ax0.imshow(edge_roberts, cmap=plt.cm.gray)

ax0.set_title('Roberts Edge Detection')

ax0.axis('off')

ax1.imshow(edge_sobel, cmap=plt.cm.gray)

ax1.set_title('Sobel Edge Detection')

ax1.axis('off')

plt.tight_layout()

edge_sobel = sobel(img_name_gs)

edge_scharr = scharr(img_name_gs)

edge_prewitt = prewitt(img_name_gs)

diff_scharr_prewitt = edge_scharr - edge_prewitt

diff_scharr_sobel = edge_scharr - edge_sobel

max_diff = np.max(np.maximum(diff_scharr_prewitt, diff_scharr_sobel))

fig, ((ax0, ax1), (ax2, ax3)) = plt.subplots(nrows=2, ncols=2, sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})

ax0.imshow(img_name_gs, cmap=plt.cm.gray)

ax0.set_title('Original image')

ax0.axis('off')

ax1.imshow(edge_scharr, cmap=plt.cm.gray)

ax1.set_title('Scharr Edge Detection')

ax1.axis('off')

ax2.imshow(diff_scharr_prewitt, cmap=plt.cm.gray, vmax=max_diff)

ax2.set_title('Scharr - Prewitt')

ax2.axis('off')

ax3.imshow(diff_scharr_sobel, cmap=plt.cm.gray, vmax=max_diff)

ax3.set_title('Scharr - Sobel')

ax3.axis('off')

plt.tight_layout()

plt.show()