%matplotlib inline

from utils import *

train_fs = build_feature_vecture(train)
test_fs = build_feature_vecture(test)

save_pickle(train_fs, 'train_fa.pickle')

from sklearn import datasets, svm, metrics, cross_validation

print "Start learning"
start_time = time.time()

clf = svm.SVC(gamma=0.001)
clf.fit(train_fs, train_labels[:len(train_fs)])

end_time = time.time()
print "Finished learning"

print("Elapsed time was %g seconds" % (end_time - start_time))

fname = get_image_by_class(foods,1)

plt.figure(1)
plt.imshow(imread(fname))

classify(clf, fname)

img = imread(foods[4][0])
print foods[4][1]
print img.shape

#fig, axs = plt.subplots(1,3)

plt.figure(1)
plt.imshow(img)
plt.colorbar()

plt.figure(2)
plt.imshow(color.rgb2hsv(img))
plt.colorbar()

orientations = 9
cell = 3
block = 1

f, hog = feature.hog(color.rgb2gray(img),
                    orientations=orientations,
                    pixels_per_cell=(cell, cell),
                    cells_per_block=(block, block),
                    visualise =True)

plt.figure(3)
plt.imshow(hog)
plt.colorbar()


from skimage.util import img_as_float
from skimage import exposure
from skimage.morphology import disk
from skimage.filter import rank

def plot_img_and_hist(img, axes, bins=256):
    """Plot an image along with its histogram and cumulative histogram.

    """
    img = img_as_float(img)
    ax_img, ax_hist = axes
    ax_cdf = ax_hist.twinx()

    # Display image
    ax_img.imshow(img, cmap=plt.cm.gray)
    #ax_img.set_axis_off()

    # Display histogram
    ax_hist.hist(img.ravel(), bins=bins, histtype='step', color='black')
    ax_hist.ticklabel_format(axis='y', style='scientific', scilimits=(0, 0))
    ax_hist.set_xlabel('Pixel intensity')
    ax_hist.set_xlim(0, 1)
    ax_hist.set_yticks([])

    # Display cumulative distribution
    img_cdf, bins = exposure.cumulative_distribution(img, bins)
    ax_cdf.plot(bins, img_cdf, 'r')
    ax_cdf.set_yticks([])

    return ax_img, ax_hist, ax_cdf

plt.rcParams['font.size'] = 9

fig, axes = plt.subplots(2, 3, figsize=(16,8))

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img, axes[:, 0])
ax_img.set_title('Low contrast image')
ax_hist.set_ylabel('Number of pixels')

fig.subplots_adjust(wspace=0.4)


img = imread(foods[4][0])
print foods[4][1]
print img.shape

f, hog = feature.hog(color.rgb2gray(img),
                    orientations=orientations,
                    pixels_per_cell=(cell, cell),
                    cells_per_block=(block, block),
                    visualise =True)

plt.figure(1)
plt.imshow(hog)
plt.colorbar()