# Files

import os

import pickle

import sys

# Basic

import numpy as np

# Image Processing

import skimage as ski

from skimage import io, feature, morphology, filter, exposure, color, transform, measure

import scipy.signal as sig

from scipy.ndimage import maximum_filter, minimum_filter, binary_fill_holes

# Stats

import scipy.stats as st

# Visualisation

#import matplotlib

#import matplotlib.pyplot as plt

#import seaborn

# Results : sheep ID, entropy, entropic variance, lacunarity, directionality

# IDs

name = []

imageid = []

# Entropy

ent = []

entstd = []

# Lacunarity

lac = []

nonlac = []

# Gabor filtering

direc = []

scale = []

# Inflammatory foci count and size

inflcount = []

inflsize = []

inflstd = []

# Tissue to sinusoid ratio

ratio = []

# Image files to process

# If we're running on all files

if len(sys.argv) == 1 :

    # Read files

    files = []

    directory = "data/"

    for i in os.listdir(directory) :

        if i.endswith(".jpg") : # if it's a jpg

            if not i.endswith("_processed.jpg") : # and isn't a processed image

                files.append(directory + i) # then add it to the list to be processed

# Otherwise, we ran launcher.py and need to do select images only

else :

    F = open("filelist%i.p" % int(sys.argv[1]), "r")

    files = pickle.load(F)

    F.close()

# Iterate over files

for f in files :

    print "Thread %s. Opening file : %s" % (sys.argv[1], f)

    # If it's not been processed before

    if not os.path.exists(f + "_processed.jpg") :

        ### PROCESSING

        # Read the image

        A = io.imread(f)

        # Constrast enhancement

        print "Thread %s. Sigmoid transform for contrast." % sys.argv[1]

        B = exposure.adjust_sigmoid(A, gain=12)

        # Extract luminosity

        print "Thread %s. Generating luminosity." % sys.argv[1]

        C = color.rgb2xyz(B)[:, :, 1]

        # Apply adaptive thresholding

        print "Thread %s. Performing adaptive thresholding." % sys.argv[1]

        D = filter.threshold_adaptive(C, 301)

        # Clean

        print "Thread %s. Cleaning image." % sys.argv[1]

        E = morphology.remove_small_objects(~morphology.remove_small_objects(~D, 100), 100)

        # Save to disk

        io.imsave(f + "_processed.jpg", ski.img_as_float(E))

        # Downsample for Gabor filtering

        print "Thread %s. Downsampling image." % sys.argv[1]

        Es = ski.img_as_float(transform.rescale(E, 0.25))

    else :

        # Otherwise, we've processed it before, so read it in for speed

        A = io.imread(f)

        E = ski.img_as_float(io.imread(f + "_processed.jpg"))

        Es = ski.img_as_float(transform.rescale(E, 0.25))

    print "Thread %s. Image already processed, downsampled." % sys.argv[1]

    ## ID

    name.append(int(f.split("data/Sheep")[1].split("-")[0].split(".jpg")[0]))

    imageid.append(int(f.split("data/Sheep")[1].split("-")[2].split(".jpg")[0]))

    ### GABOR FILTERING

    # Define scales and orientations to compute over

    pixelscales = np.arange(15, 55, 2)

    gaborscales = 4. / pixelscales # 2 to 20 pixels

    orientations = np.linspace(0, np.pi * 11./12., 12) # 0 to 180 degrees in 15 degree increments

    # Results array

    gabor = np.zeros((len(orientations), len(gaborscales)))

    # Perform Gabor filtering

    for i, iv in enumerate(orientations) :

        for j, jv in enumerate(gaborscales) :

            gaborReal, gaborImag = filter.gabor_filter(Es, jv, iv)

            gabor[i, j] = np.sqrt(np.sum(np.abs(gaborReal) ** 2) + np.sum(np.abs(gaborImag) ** 2)) # Return energy

        print "Thread %s. Gabor filtering. Completion : %f" % (sys.argv[1], (i / float(len(orientations))))

    # Determine orientation-independent scale which fits best

    optimalscale = np.argmax(np.sum(gabor, axis = 0))

    # At this scale, calculate directionality coefficient

    g = gabor[:, optimalscale]

    directionality = (g.max() - g.min()) / g.max()

    # Results

    scale.append(pixelscales[optimalscale])

    direc.append(directionality)

    ### LACUNARITY AND ENTROPY

    # Define scales over which to compute lacunarity and entropy

    scaleent = []

    scaleentstd = []

    #scalelac = []

    roylac = []

    # Characteristic scale

    s = pixelscales[optimalscale]

    # Generate a disk at this scale

    circle = morphology.disk(s)

    circlesize = circle.sum()

    # Convolve with image

    Y = sig.fftconvolve(E, circle, "valid")

    # Compute information entropy

    px = Y.ravel() / circlesize

    #px = np.reshape(Y[int(i) : int(E.shape[0]-i), int(i) : int(E.shape[1]-i)] / circlesize, (E.shape[0]-2*i)*(E.shape[1]-2*i),)

    py = 1. - px

    idx = np.logical_and(px > 1. / circlesize, px < 1.)

    entropy = - ( np.mean(px[idx] * np.log(px[idx])) + np.mean(py[idx] * np.log(py[idx])) )

    entropystd = np.std(px[idx] * np.log(px[idx]) + py[idx] * np.log(py[idx]))

    # Compute normalised lacunarity

    lx = np.var(Y) / (np.mean(Y) ** 2) + 1

    ly = np.var(1. - Y) / (np.mean(1. - Y) ** 2) + 1

    # lacunarity = 2. - (1. / lx + 1. / ly)

    # Roy et al, J. Struct. Geol. 2010

    # Results

    roylac.append((lx - 1.) / (1./np.mean(E) - 1.))

    scaleent.append(entropy)

    scaleentstd.append(entropystd)

    # scalelac.append(lacunarity)

    print "Thread %s. Calculated entropy and lacunarity." % sys.argv[1]

    # Old code evaluates lacunarity and entropy at all scales

    # We only need it at the image's characteristic scale

    """

    # Iterate over scales

    for i, s in enumerate(lacscales) :

        # Generate a disk at this scale

        circle = morphology.disk(s)

        circlesize = circle.sum()

        # Convolve with image

        Y = sig.fftconvolve(E, circle, "valid")

        # Compute information entropy

        px = Y.ravel() / circlesize

        #px = np.reshape(Y[int(i) : int(E.shape[0]-i), int(i) : int(E.shape[1]-i)] / circlesize, (E.shape[0]-2*i)*(E.shape[1]-2*i),)

        py = 1. - px

        idx = np.logical_and(px > 1. / circlesize, px < 1.)

        entropy = - ( np.mean(px[idx] * np.log(px[idx])) + np.mean(py[idx] * np.log(py[idx])) )

        entropystd = np.std(px[idx] * np.log(px[idx]) + py[idx] * np.log(py[idx]))

        # Compute normalised lacunarity

        lx = np.var(Y) / (np.mean(Y) ** 2) + 1

        ly = np.var(1. - Y) / (np.mean(1. - Y) ** 2) + 1

        # lacunarity = 2. - (1. / lx + 1. / ly)

        # Roy et al, J. Struct. Geol. 2010

        # Results

        roylac.append((lx - 1.) / (1./np.mean(E) - 1.))

        scaleent.append(entropy)

        scaleentstd.append(entropystd)

        # scalelac.append(lacunarity)

        print "Thread %s. Calculating entropy and lacunarity. Completion : %f" % (sys.argv[1], (float(i) / len(lacscales)))

    """

    nonlac.append(roylac)

    ent.append(scaleent)

    entstd.append(scaleentstd)

    # lac.append(scalelac)

    # Inflammatory focus count

    qstain = np.array([[.26451728, .5205347, .81183386], [.9199094, .29797825, .25489032], [.28947765, .80015373, .5253158]])

    deconv = ski.img_as_float(color.separate_stains(transform.rescale(A, 0.25), np.linalg.inv(qstain)))

    subveins1 = \

    morphology.remove_small_objects(

        filter.threshold_adaptive(

            filter.gaussian_filter(

                deconv[:, :, 2] / deconv[:, :, 0],

                11),

            250, offset = -0.13),

        60)

    subveins2 = \

    morphology.remove_small_objects(

        filter.threshold_adaptive(

            filter.gaussian_filter(

                    maximum_filter(

                    deconv[:, :, 2] / deconv[:, :, 0],

                    5),

                11),

            250, offset = -0.13),

        60)

    veins = \

    maximum_filter(

        morphology.remove_small_objects(

            binary_fill_holes(

                morphology.binary_closing(

                    np.logical_or(subveins1, subveins2),

                    morphology.disk(25)),

                ),

            250),

        27)

    rawinflammation = \

    morphology.remove_small_objects(

    filter.threshold_adaptive(

        exposure.adjust_sigmoid(

            filter.gaussian_filter(

                exposure.equalize_adapthist(

                    exposure.rescale_intensity(

                        deconv[:, :, 1],

                        out_range = (0, 1)),

                    ntiles_y = 1),

                    5),

            cutoff = 0.6),

            75, offset = -0.12),

        250)

    inflammation = \

    maximum_filter(

        rawinflammation,

        29)

    print "Thread %s. Image segmentation complete." % sys.argv[1]

    total = veins + inflammation

    coloured = np.zeros_like(deconv)

    coloured[:, :, 1] = veins

    coloured[:, :, 2] = inflammation

    labelled, regions = measure.label(total, return_num = True)

    inflammationcount = 0

    inflammationsize = []

    for b in range(1, regions) :

        if (inflammation * (labelled == b)).sum() / ((veins * (labelled == b)).sum() + 1) > 0.5 :

            inflammationcount += 1

            inflammationsize.append((rawinflammation * labelled == b).sum())

    inflcount.append(inflammationcount)

    inflsize.append(np.mean(inflammationsize))

    inflstd.append(np.std(inflammationsize))

    # Tissue to sinusoid ratio

    tissue = np.sum(ski.img_as_bool(Es) * (1 - ski.img_as_bool(total)))

    sinusoids = np.sum(Es.shape[0] * Es.shape[1] - np.sum(ski.img_as_bool(total)))

    ratio.append(tissue.astype(float) / sinusoids)

# Pickle results

results = {}

results["ratio"] = ratio

results["inflcount"] = inflcount

results["inflsize"] = inflsize

results["inflstd"] = inflstd

results["entropy"] = ent

results["entstd"] = entstd

results["directionality"] = direc

results["scale"] = scale

results["name"] = name

results["ID"] = imageid

results["roylac"] = nonlac

F = open("results/thread_%s" % sys.argv[1], "w")

pickle.dump(results, F)

F.close()