Simple CAPTCHA detection using PyTorch

conda install pytorch torchvision torchaudio cpuonly -c pytorch

sudo apt-get install libgl1  # for opencv
pip install requests matplotlib opencv-python

CAPTCHA_URL = 'https://captcha.tomo.wang'
r = requests.get(CAPTCHA_URL)
captcha = r.headers['X-Captcha']
with open('{}.png'.format(captcha), 'wb') as f:
    f.write(r.content)

import os
import re
import sys
import argparse
import glob
from io import BytesIO

import requests
import numpy as np
import cv2
import matplotlib.pyplot as plt

import torch
from torch.autograd import Variable
import torch.nn.functional as F

NUM_CHARS = 4
CAPTCHA_WIDTH = 200
CAPTCHA_HEIGHT = 62
CH_WIDTH = 20
CH_HEIGHT = 28

CAPTCHA_DIR = './images'
TORCH_NET_PATH = 'captcha.torch'
BG_COLOR = (243, 251, 254)  # captcha backgroud color
BG_THRESHOLD = 245

BLANK_THRESHHOLD = 1
DOTS_THRESHOLD = 3
CH_MIN_WIDTH = 8

def get_captcha():
    CAPTCHA_URL = 'https://captcha.tomo.wang'
    r = requests.get(CAPTCHA_URL)
    return r.content

img = get_captcha()
plt.imshow(plt.imread(BytesIO(img)))

img_array = np.asarray(bytearray(img), dtype=np.uint8)
img = cv2.imdecode(img_array, cv2.IMREAD_GRAYSCALE)
assert img.shape == (CAPTCHA_HEIGHT, CAPTCHA_WIDTH)
img = cv2.threshold(img, BG_THRESHOLD, 255, cv2.THRESH_BINARY_INV)[1]
plt.imshow(cv2.cvtColor(img, cv2.COLOR_GRAY2BGR))

def _denoise(img):
    img = cv2.threshold(img, BG_THRESHOLD, 255, cv2.THRESH_BINARY_INV)[1]
    return img


def _preprocess(img):
    img = img.copy()
    img = _denoise(img)
    return img


def find_filled_row(rows):
    for i, row in enumerate(rows):
        dots = np.sum(row) // 255
        if dots >= DOTS_THRESHOLD:
            return i
    assert False, 'cannot find filled row'


def pad_ch(ch):
    pad_w = CH_WIDTH - ch.shape[1]
    assert pad_w >= 0, 'bad char width'
    pad_w1 = pad_w // 2
    pad_w2 = pad_w - pad_w1
    pad_h = CH_HEIGHT - ch.shape[0]
    assert pad_h >= 0, 'bad char height'
    pad_h1 = pad_h // 2
    pad_h2 = pad_h - pad_h1
    return np.pad(ch, ((pad_h1, pad_h2), (pad_w1, pad_w2)), 'constant')


def segment(img):
    # Search blank intervals.
    img = _preprocess(img)
    dots_per_col = np.apply_along_axis(lambda row: np.sum(row) // 255, 0, img)
    blanks = []
    was_blank = False
    first_ch_x = None
    prev_x = 0
    x = 0
    while x < CAPTCHA_WIDTH:
        if dots_per_col[x] >= DOTS_THRESHOLD:
            if first_ch_x is None:
                first_ch_x = x
            if was_blank:
                # Skip first blank.
                if prev_x:
                    blanks.append((prev_x, x))
                # Don't allow too tight chars.
                x += CH_MIN_WIDTH
                was_blank = False
        elif not was_blank:
            was_blank = True
            prev_x = x
        x += 1
    blanks = [b for b in blanks if b[1] - b[0] >= BLANK_THRESHHOLD]
    # Add last (imaginary) blank to simplify following loop.
    blanks.append((prev_x if was_blank else CAPTCHA_WIDTH, 0))

    # Get chars.
    chars = []
    x1 = first_ch_x
    widest = 0, 0
    for i, (x2, next_x1) in enumerate(blanks):
        width = x2 - x1
        # Don't allow more than CH_WIDTH * 2.
        extra_w = width - CH_WIDTH * 2
        extra_w1 = extra_w // 2
        extra_w2 = extra_w - extra_w1
        x1 = max(x1, x1 + extra_w1)
        x2 = min(x2, x2 - extra_w2)
        ch = img[:CAPTCHA_HEIGHT, x1:x2]

        y1 = find_filled_row(ch[::])
        y2 = CAPTCHA_HEIGHT - find_filled_row(ch[::-1])
        ch = ch[y1:y2]

        chars.append(ch)
        if width > widest[0]:
            widest = x2 - x1, i
        x1 = next_x1

    # Fit chars into boxes.
    chars2 = []
    for i, ch in enumerate(chars):
        widest_w, widest_i = widest
        # Split glued chars.
        if len(chars) < NUM_CHARS and i == widest_i:
            ch1 = ch[:, 0:widest_w // 2]
            ch2 = ch[:, widest_w // 2:widest_w]
            chars2.append(pad_ch(ch1))
            chars2.append(pad_ch(ch2))
        else:
            ch = ch[:, 0:CH_WIDTH]
            chars2.append(pad_ch(ch))

    assert len(chars2) == NUM_CHARS, 'bad number of chars'
    return chars2

chars2 = segment(cv2.imdecode(img_array, cv2.IMREAD_GRAYSCALE))
fig = plt.figure()
for i, char in enumerate(chars2):
    a = fig.add_subplot(1, 4, i+1)
    plt.imshow(cv2.cvtColor(char, cv2.COLOR_GRAY2BGR))

def check_image(img):
    assert img is not None, 'cannot read image'
    assert img.shape == (CAPTCHA_HEIGHT, CAPTCHA_WIDTH), 'bad image dimensions'


def read_image_file(fpath):
    with open(fpath, 'rb') as f:
        return decode_image(f.read())


def decode_image(data):
    data = np.frombuffer(data, np.uint8)
    img = cv2.imdecode(data, cv2.IMREAD_GRAYSCALE)
    check_image(img)
    return img


def get_ch_data(img):
    data = img.flatten() & 1
    assert len(data) == NUM_INPUT, 'bad data size'
    return data

# nn net define
NUM_INPUT = CH_WIDTH * CH_HEIGHT
NUM_NEURONS_HIDDEN = NUM_INPUT // 3
NUM_OUTPUT = 10


class Net(torch.nn.Module):
    def __init__(self, n_feature, n_hidden, n_output):
        super(Net, self).__init__()
        self.hidden = torch.nn.Linear(n_feature, n_hidden)   # hidden layer
        self.out = torch.nn.Linear(n_hidden, n_output)   # output layer

    def forward(self, x):
        x = F.relu(self.hidden(x))      # activation function for hidden layer
        x = self.out(x)
        return x

def train(captchas_dir):
    net = Net(n_feature=NUM_INPUT, n_hidden=NUM_NEURONS_HIDDEN, n_output=NUM_OUTPUT)

    optimizer = torch.optim.SGD(net.parameters(), lr=0.02, momentum=0.9)
    loss_func = torch.nn.CrossEntropyLoss()

    captchas_dir = os.path.abspath(captchas_dir)
    captchas = glob.glob(captchas_dir + '/*.png')

    x, y = [], []
    for i, name in enumerate(captchas):
        answer = re.match(r'.*(\d{4})\.png$', name)
        if not answer:
            continue
        answer = answer.group(1)
        fpath = os.path.join(captchas_dir, name)
        try:
            img = read_image_file(fpath)
            ch_imgs = segment(img)
            for ch_img, digit in zip(ch_imgs, answer):
                x.append(get_ch_data(ch_img))
                y.append(int(digit))
        except Exception as e:
            print('Error occured while processing {}: {}'.format(name, e))
        else:
            if (i + 1) % 25 == 0:
                print('{}/{}'.format(i + 1, len(captchas)))

    x, y = torch.from_numpy(np.array(x)).type(torch.FloatTensor), torch.from_numpy(np.array(y)).type(torch.LongTensor)
    x, y = Variable(x), Variable(y)

    for t in range(100):
        out = net(x)                 # input x and predict based on x
        loss = loss_func(out, y)     # must be (1. nn output, 2. target), the target label is NOT one-hotted

        optimizer.zero_grad()   # clear gradients for next train
        loss.backward()         # backpropagation, compute gradients
        optimizer.step()        # apply gradients

    return net

net = train(CAPTCHA_DIR)

print(net)

def predict(net, img_content):
    def get_digit(ch_img):
        x = torch.from_numpy(get_ch_data(ch_img)).type(torch.FloatTensor)
        output = net(Variable(x))
        _, predicted = torch.max(output.data, 0)
        # return str(Variable(predicted).data[0])
        return str(predicted.item())

    img = decode_image(img_content)
    ch_imgs = segment(img)
    return ''.join(map(get_digit, ch_imgs))

img_content = get_captcha()
plt.imshow(plt.imread(BytesIO(img_content)))
result = predict(net, img_content)
plt.title(result)