#!/usr/bin/env python3

from progressbar import ProgressBar
from pretty import *
import argparse
import numpy
import re
import os
import os.path
from scipy import misc
from scipy.ndimage.filters import gaussian_filter

parser = argparse.ArgumentParser()
parser.add_argument('-t', '--tagged', help='load tagged data from this directory', default='tagged/full')
parser.add_argument('-d', '--devkit', help='devkit directory containing categories.txt', default='mp-dev_kit')
parser.add_argument('-i', '--images', help='path to images/', default='mp-data/images')
parser.add_argument('-o', '--outdir', help='directory to save images', default=None)
parser.add_argument('-n', '--network', help='network for choosing outdir', default='base')
parser.add_argument('--serve', help='run http server', action='store_true')
parser.set_defaults(serve=False)
args = parser.parse_args()

if args.outdir is None:
    args.outdir = "exp-{}".format(args.network)

# A clipping rectifier, pegging values less than t1 to 0 and more than t2 to 1.
def peg(ar, t1, t2):
    return numpy.clip((ar - t1) / (t2 - t1), 0, 1)

# regular python argsort, from stack overflow
def argsort(seq):
    return sorted(range(len(seq)), key=seq.__getitem__)

# Category labels
categories = []
for line in open(os.path.join(args.devkit, 'categories.txt')).readlines():
    assert int(line.strip().split()[1]) == len(categories)
    categories.append(re.sub('^/[a-z]/', '', line.strip().split()[0]))
cats = len(categories)

def extract_resp_region(resp):
    avg = numpy.average(resp)
    std = numpy.std(resp)
    respmax = avg + std
    respmin = avg - std
    resp = numpy.minimum(
            numpy.maximum((respmax - resp) /
            (respmax - respmin + 1e-6), 0), 1)
    smeared = numpy.zeros([128, 128])
    smearedd = numpy.ones([128, 128]) * 0.01
    # Some geometric parameters
    res = 16
    pix = 23
    st = 7
    for x in range(res):
        for y in range(res):
            smeared[y*st:y*st+pix, x*st:x*st+pix] += resp[y][x]
            smearedd[y*st:y*st+pix, x*st:x*st+pix] += 1
    return peg(numpy.clip(gaussian_filter(
            numpy.divide(smeared, smearedd), sigma=st), 0, 1), 0.4, 0.6)

def create_response_images(name, subset):
    task("Generating images showing response regions for {}".format(name))
    labels = {}
    for line in open(os.path.join(args.devkit, '%s.txt' % subset)).readlines():
        name, label = line.strip().split()
        labels[name] = int(label)
    filenames = [line.strip() for line in open(os.path.join(args.tagged,
            '%s.filenames.txt' % subset)).readlines()]
    response = numpy.memmap(
            os.path.join(args.outdir, '%s.response.db' % subset),
            dtype=numpy.float32, mode='r')
    response.shape = (response.shape[0] / 256, 16, 16)
    topresponse = numpy.memmap(
            os.path.join(args.outdir, '%s.topresponse.db' % subset),
            dtype=numpy.float32, mode='r')
    topresponse.shape = (topresponse.shape[0] / 256, 16, 16)
    cases = response.shape[0]
    p = progress(cases)
    for index in range(cases):
        # Smear the measured response in the way it was collected:
        # over the image with 16 overlapping blocks with a 6-pixel stride.
        # the blocks are made larger here to fill the entire 128px image,
        # and additionally a gaussian blur is added at the end.
        r = extract_resp_region(response[index])
        tr = extract_resp_region(topresponse[index])
        # now apply the response to create two images.
        r3 = numpy.tile(r.reshape([128, 128, 1]), [1, 1, 3])
        tr3 = numpy.tile(tr.reshape([128, 128, 1]), [1, 1, 3])
        im = misc.imread(os.path.join(args.images, filenames[index]))
        # the "see" image shows where the response was high.
        seegim = numpy.multiply(im, r3)
        seegname = os.path.join(args.outdir, 'resp', 'seeg', filenames[index])
        os.makedirs(os.path.dirname(seegname), exist_ok=True)
        misc.imsave(seegname, seegim)
        # the "seec" image shows where the response was high.
        seecim = numpy.multiply(im, tr3)
        seecname = os.path.join(args.outdir, 'resp', 'seec', filenames[index])
        os.makedirs(os.path.dirname(seecname), exist_ok=True)
        misc.imsave(seecname, seecim)
        # the "ignoreg" image shows where the response was low.
        ignoregim = numpy.multiply(im, 1 - r3)
        ignoregname = os.path.join(
                args.outdir, 'resp', 'ignoregg', filenames[index])
        os.makedirs(os.path.dirname(ignoregname), exist_ok=True)
        misc.imsave(ignoregname, ignoregim)
        # the "tignore" image shows where the response was low.
        ignorecim = numpy.multiply(im, 1 - tr3)
        ignorecname = os.path.join(
                args.outdir, 'resp', 'ignorec', filenames[index])
        os.makedirs(os.path.dirname(ignorecname), exist_ok=True)
        misc.imsave(ignorecname, ignorecim)
        # use a blurred image for visualization
        blurred = numpy.zeros(im.shape)
        for c in range(3):
            blurred[:,:,c] = gaussian_filter(im[:,:,c], sigma=12)
        # the "good" image blurs the ignore image and shows the see image
        blurignore = numpy.multiply(blurred, 1 - r3)
        goalim = blurignore + seegim
        goalname = os.path.join(args.outdir, 'resp', 'goal', filenames[index])
        os.makedirs(os.path.dirname(goalname), exist_ok=True)
        misc.imsave(goalname, goalim)
        # the "notgoal" image shows where the image is confused
        blursee = numpy.multiply(blurred, r3)
        notgoalim = blursee + ignoregim
        notgoalname = os.path.join(
                args.outdir, 'resp', 'notgoal', filenames[index])
        os.makedirs(os.path.dirname(notgoalname), exist_ok=True)
        misc.imsave(notgoalname, notgoalim)
        # the "chosen" image shows where the image chosened a response
        blurtignore = numpy.multiply(blurred, 1 - tr3)
        chosenim = blurtignore + seecim
        chosenname = os.path.join(
                args.outdir, 'resp', 'chosen', filenames[index])
        os.makedirs(os.path.dirname(chosenname), exist_ok=True)
        misc.imsave(chosenname, chosenim)
        # the "notchosen" image shows where the image notchosened a response
        blurtignore = numpy.multiply(blurred, tr3)
        notchosenim = blurtignore + ignorecim
        notchosenname = os.path.join(
                args.outdir, 'resp', 'notchosen', filenames[index])
        os.makedirs(os.path.dirname(notchosenname), exist_ok=True)
        misc.imsave(notchosenname, notchosenim)

        p.update(index + 1)




def create_eval_html(name, subset):
    html = [
        '<!doctype html>',
        '<html>',
        '<style>td {text-align:center;}</style>',
        '<body>',
        '<table>',
        '<tr><th>PICKED</th><th>CORRECT</th></tr>'
    ]
    labels = {}
    for line in open(os.path.join(args.devkit, '%s.txt' % subset)).readlines():
        name, label = line.strip().split()
        labels[name] = int(label)
    filenames = [line.strip() for line in open(os.path.join(args.tagged,
            '%s.filenames.txt' % subset)).readlines()]
    predictions = numpy.memmap(os.path.join(args.outdir,
            '%s.confusion.db' % subset), dtype=numpy.float32, mode='r')
    cases = int(predictions.shape[0] / cats)
    predictions.shape = (cases, cats)
    topindex = numpy.argsort(-predictions, axis=1)
    confusion = []
    for index in range(cases):
        top = topindex[index]
        correct = labels[filenames[index]]
        confusion.append(numpy.where(top == correct)[0][0])
    worstfirst = reversed(argsort(confusion))
    for index in worstfirst:
        top = topindex[index]
        correct = labels[filenames[index]]
        score = predictions[index]
        html.append('<tr><td colspan=2>{} ({}) [{}]</td></tr>'.format(
                filenames[index],
                index,
                confusion[index]))
        html.append('<tr>')
        html.append('<td><img src="resp/chosen/' + filenames[index] + '">')
        html.append('<br>{} ({})<br>{}</td>'.format(
                categories[top[0]],
                top[0],
                score[top[0]]))
        html.append('<td><img src="resp/notchosen/' + filenames[index] + '">')
        html.append('<br>NOT {} ({})<br>{}</td>'.format(
                categories[top[0]],
                top[0],
                score[top[0]]))
        html.append('<td><img src="resp/goal/' + filenames[index] + '">')
        html.append('<br>{} ({})<br>{}</td>'.format(
                categories[correct],
                correct,
                score[correct]))
        html.append('<td><img src="resp/notgoal/' + filenames[index] + '">')
        html.append('<br>NOT {} ({})<br>{}</td>'.format(
                categories[correct],
                correct,
                score[correct]))
    html.extend(['</table>', '</body>', '</html>'])
    hfilename = os.path.join(args.outdir, '%s.eval.html' % subset)
    with open(hfilename, 'w') as htmlfile:
        htmlfile.write('\n'.join(html))


create_eval_html("validation set", "val")
create_eval_html("training set", "train")
create_response_images("validation set", "val")
create_response_images("training set", "train")

if args.serve:
    from http.server import HTTPServer
    from http.server import SimpleHTTPRequestHandler
    os.chdir(args.outdir)
    PORT = 8000
    httpd = HTTPServer(('', PORT), SimpleHTTPRequestHandler)
    print("serving at port", PORT)
    try:
      httpd.serve_forever()
    except KeyboardInterrupt:
      pass
    httpd.server_close()

#!/usr/bin/env python3

from progressbar import ProgressBar
from pretty import *
import argparse
import numpy
import re
import os
import os.path
from scipy import misc
from scipy.ndimage.filters import gaussian_filter

parser = argparse.ArgumentParser()
parser.add_argument('-t', '--tagged', help='load tagged data from this directory', default='tagged/full')
parser.add_argument('-d', '--devkit', help='devkit directory containing categories.txt', default='mp-dev_kit')
parser.add_argument('-i', '--images', help='path to images/', default='mp-data/images')
parser.add_argument('-o', '--outdir', help='directory to save images', default=None)
parser.add_argument('-n', '--network', help='network for choosing outdir', default='base')
parser.add_argument('--serve', help='run http server', action='store_true')
parser.set_defaults(serve=False)
args = parser.parse_args()

if args.outdir is None:
    args.outdir = "exp-{}".format(args.network)

# A clipping rectifier, pegging values less than t1 to 0 and more than t2 to 1.
def peg(ar, t1, t2):
    return numpy.clip((ar - t1) / (t2 - t1), 0, 1)

# regular python argsort, from stack overflow
def argsort(seq):
    return sorted(range(len(seq)), key=seq.__getitem__)

# Category labels
categories = []
for line in open(os.path.join(args.devkit, 'categories.txt')).readlines():
    assert int(line.strip().split()[1]) == len(categories)
    categories.append(re.sub('^/[a-z]/', '', line.strip().split()[0]))
cats = len(categories)

def extract_resp_region(resp):
    avg = numpy.average(resp)
    std = numpy.std(resp)
    respmax = avg + std
    respmin = avg - std
    resp = numpy.minimum(
            numpy.maximum((respmax - resp) /
            (respmax - respmin + 1e-6), 0), 1)
    smeared = numpy.zeros([128, 128])
    smearedd = numpy.ones([128, 128]) * 0.01
    # Some geometric parameters
    res = 16
    pix = 23
    st = 7
    for x in range(res):
        for y in range(res):
            smeared[y*st:y*st+pix, x*st:x*st+pix] += resp[y][x]
            smearedd[y*st:y*st+pix, x*st:x*st+pix] += 1
    return peg(numpy.clip(gaussian_filter(
            numpy.divide(smeared, smearedd), sigma=st), 0, 1), 0.4, 0.6)

def create_response_images(name, subset):
    task("Generating images showing response regions for {}".format(name))
    labels = {}
    for line in open(os.path.join(args.devkit, '%s.txt' % subset)).readlines():
        name, label = line.strip().split()
        labels[name] = int(label)
    filenames = [line.strip() for line in open(os.path.join(args.tagged,
            '%s.filenames.txt' % subset)).readlines()]
    response = numpy.memmap(
            os.path.join(args.outdir, '%s.response.db' % subset),
            dtype=numpy.float32, mode='r')
    response.shape = (response.shape[0] / 256, 16, 16)
    topresponse = numpy.memmap(
            os.path.join(args.outdir, '%s.topresponse.db' % subset),
            dtype=numpy.float32, mode='r')
    topresponse.shape = (topresponse.shape[0] / 256, 16, 16)
    cases = response.shape[0]
    p = progress(cases)
    for index in range(cases):
        # Smear the measured response in the way it was collected:
        # over the image with 16 overlapping blocks with a 6-pixel stride.
        # the blocks are made larger here to fill the entire 128px image,
        # and additionally a gaussian blur is added at the end.
        r = extract_resp_region(response[index])
        tr = extract_resp_region(topresponse[index])
        # now apply the response to create two images.
        r3 = numpy.tile(r.reshape([128, 128, 1]), [1, 1, 3])
        tr3 = numpy.tile(tr.reshape([128, 128, 1]), [1, 1, 3])
        im = misc.imread(os.path.join(args.images, filenames[index]))
        # the "see" image shows where the response was high.
        seegim = numpy.multiply(im, r3)
        seegname = os.path.join(args.outdir, 'resp', 'seeg', filenames[index])
        os.makedirs(os.path.dirname(seegname), exist_ok=True)
        misc.imsave(seegname, seegim)
        # the "seec" image shows where the response was high.
        seecim = numpy.multiply(im, tr3)
        seecname = os.path.join(args.outdir, 'resp', 'seec', filenames[index])
        os.makedirs(os.path.dirname(seecname), exist_ok=True)
        misc.imsave(seecname, seecim)
        # the "ignoreg" image shows where the response was low.
        ignoregim = numpy.multiply(im, 1 - r3)
        ignoregname = os.path.join(
                args.outdir, 'resp', 'ignoregg', filenames[index])
        os.makedirs(os.path.dirname(ignoregname), exist_ok=True)
        misc.imsave(ignoregname, ignoregim)
        # the "tignore" image shows where the response was low.
        ignorecim = numpy.multiply(im, 1 - tr3)
        ignorecname = os.path.join(
                args.outdir, 'resp', 'ignorec', filenames[index])
        os.makedirs(os.path.dirname(ignorecname), exist_ok=True)
        misc.imsave(ignorecname, ignorecim)
        # use a blurred image for visualization
        blurred = numpy.zeros(im.shape)
        for c in range(3):
            blurred[:,:,c] = gaussian_filter(im[:,:,c], sigma=12)
        # the "good" image blurs the ignore image and shows the see image
        blurignore = numpy.multiply(blurred, 1 - r3)
        goalim = blurignore + seegim
        goalname = os.path.join(args.outdir, 'resp', 'goal', filenames[index])
        os.makedirs(os.path.dirname(goalname), exist_ok=True)
        misc.imsave(goalname, goalim)
        # the "notgoal" image shows where the image is confused
        blursee = numpy.multiply(blurred, r3)
        notgoalim = blursee + ignoregim
        notgoalname = os.path.join(
                args.outdir, 'resp', 'notgoal', filenames[index])
        os.makedirs(os.path.dirname(notgoalname), exist_ok=True)
        misc.imsave(notgoalname, notgoalim)
        # the "chosen" image shows where the image chosened a response
        blurtignore = numpy.multiply(blurred, 1 - tr3)
        chosenim = blurtignore + seecim
        chosenname = os.path.join(
                args.outdir, 'resp', 'chosen', filenames[index])
        os.makedirs(os.path.dirname(chosenname), exist_ok=True)
        misc.imsave(chosenname, chosenim)
        # the "notchosen" image shows where the image notchosened a response
        blurtignore = numpy.multiply(blurred, tr3)
        notchosenim = blurtignore + ignorecim
        notchosenname = os.path.join(
                args.outdir, 'resp', 'notchosen', filenames[index])
        os.makedirs(os.path.dirname(notchosenname), exist_ok=True)
        misc.imsave(notchosenname, notchosenim)

        p.update(index + 1)




def create_eval_html(name, subset):
    html = [
        '<!doctype html>',
        '<html>',
        '<style>td {text-align:center;}</style>',
        '<body>',
        '<table>',
        '<tr><th>PICKED</th><th>CORRECT</th></tr>'
    ]
    labels = {}
    for line in open(os.path.join(args.devkit, '%s.txt' % subset)).readlines():
        name, label = line.strip().split()
        labels[name] = int(label)
    filenames = [line.strip() for line in open(os.path.join(args.tagged,
            '%s.filenames.txt' % subset)).readlines()]
    predictions = numpy.memmap(os.path.join(args.outdir,
            '%s.confusion.db' % subset), dtype=numpy.float32, mode='r')
    cases = int(predictions.shape[0] / cats)
    predictions.shape = (cases, cats)
    topindex = numpy.argsort(-predictions, axis=1)
    confusion = []
    for index in range(cases):
        top = topindex[index]
        correct = labels[filenames[index]]
        confusion.append(numpy.where(top == correct)[0][0])
    worstfirst = reversed(argsort(confusion))
    for index in worstfirst:
        top = topindex[index]
        correct = labels[filenames[index]]
        score = predictions[index]
        html.append('<tr><td colspan=2>{} ({}) [{}]</td></tr>'.format(
                filenames[index],
                index,
                confusion[index]))
        html.append('<tr>')
        html.append('<td><img src="resp/chosen/' + filenames[index] + '">')
        html.append('<br>{} ({})<br>{}</td>'.format(
                categories[top[0]],
                top[0],
                score[top[0]]))
        html.append('<td><img src="resp/notchosen/' + filenames[index] + '">')
        html.append('<br>NOT {} ({})<br>{}</td>'.format(
                categories[top[0]],
                top[0],
                score[top[0]]))
        html.append('<td><img src="resp/goal/' + filenames[index] + '">')
        html.append('<br>{} ({})<br>{}</td>'.format(
                categories[correct],
                correct,
                score[correct]))
        html.append('<td><img src="resp/notgoal/' + filenames[index] + '">')
        html.append('<br>NOT {} ({})<br>{}</td>'.format(
                categories[correct],
                correct,
                score[correct]))
    html.extend(['</table>', '</body>', '</html>'])
    hfilename = os.path.join(args.outdir, '%s.eval.html' % subset)
    with open(hfilename, 'w') as htmlfile:
        htmlfile.write('\n'.join(html))


create_eval_html("validation set", "val")
create_eval_html("training set", "train")
create_response_images("validation set", "val")
create_response_images("training set", "train")

if args.serve:
    from http.server import HTTPServer
    from http.server import SimpleHTTPRequestHandler
    os.chdir(args.outdir)
    PORT = 8000
    httpd = HTTPServer(('', PORT), SimpleHTTPRequestHandler)
    print("serving at port", PORT)
    try:
      httpd.serve_forever()
    except KeyboardInterrupt:
      pass
    httpd.server_close()

#!/usr/bin/env python3 # A clipping rectifier, pegging values less than t1 to 0 and more than t2 to 1. # regular python argsort, from stack overflow # Category labels # Some geometric parameters # Smear the measured response in the way it was collected: # over the image with 16 overlapping blocks with a 6-pixel stride. # the blocks are made larger here to fill the entire 128px image, # and additionally a gaussian blur is added at the end. # now apply the response to create two images. # the "see" image shows where the response was high. # the "seec" image shows where the response was high. # the "ignoreg" image shows where the response was low. # the "tignore" image shows where the response was low. # use a blurred image for visualization # the "good" image blurs the ignore image and shows the see image # the "notgoal" image shows where the image is confused # the "chosen" image shows where the image chosened a response # the "notchosen" image shows where the image notchosened a response