PassportEye does not working with the new version of numpy (2.0)

image.py

''' PassportEye::MRZ: Machine-readable zone extraction and parsing. Image processing for MRZ extraction.

Author: Konstantin Tretyakov License: MIT ''' import io import numpy as np from skimage import transform, morphology, filters, measure from skimage import io as skimage_io # So as not to clash with builtin io from ..util.pdf import extract_first_jpeg_in_pdf from ..util.pipeline import Pipeline from ..util.geometry import RotatedBox from ..util.ocr import ocr from .text import MRZ

class Loader(object): """Loads file to img."""

__depends__ = []
__provides__ = ['img']

def __init__(self, file, as_gray=True, pdf_aware=True):
    self.file = file
    self.as_gray = as_gray
    self.pdf_aware = pdf_aware

def _imread(self, file):
    """Proxy to skimage.io.imread with some fixes."""
    img = skimage_io.imread(file, as_gray=self.as_gray, plugin='imageio')
    if img is not None and len(img.shape) != 2:
        img = skimage_io.imread(file, as_gray=self.as_gray, plugin='matplotlib')
    return img

def __call__(self):
    if isinstance(self.file, str):
        if self.pdf_aware and self.file.lower().endswith('.pdf'):
            with open(self.file, 'rb') as f:
                img_data = extract_first_jpeg_in_pdf(f)
            if img_data is None:
                return None
            return self._imread(img_data)
        else:
            return self._imread(self.file)
    elif isinstance(self.file, (bytes, io.IOBase)):
        return self._imread(self.file)
    return None

class Scaler(object): """Scales image down to img_scaled so that its width is at most 250."""

__depends__ = ['img']
__provides__ = ['img_small', 'scale_factor']

def __init__(self, max_width=250):
    self.max_width = max_width

def __call__(self, img):
    scale_factor = self.max_width / float(img.shape[1])
    if scale_factor <= 1:
        img_small = transform.rescale(img, scale_factor, mode='constant', channel_axis=None, anti_aliasing=True)
    else:
        scale_factor = 1.0
        img_small = img
    return img_small, scale_factor

class BooneTransform(object): """Processes img_small according to Hans Boone's method (http://www.pyimagesearch.com/2015/11/30/detecting-machine-readable-zones-in-passport-images/) Outputs a img_binary - a result of threshold_otsu(closing(sobel(black_tophat(img_small)))"""

__depends__ = ['img_small']
__provides__ = ['img_binary']

def __init__(self, square_size=5):
    self.square_size = square_size

def __call__(self, img_small):
    m = morphology.square(self.square_size)
    img_th = morphology.black_tophat(img_small, m)
    img_sob = abs(filters.sobel_v(img_th))
    img_closed = morphology.closing(img_sob, m)
    threshold = filters.threshold_otsu(img_closed)
    return img_closed > threshold

class MRZBoxLocator(object): """Extracts putative MRZs as RotatedBox instances from the contours of img_binary"""

__depends__ = ['img_binary']
__provides__ = ['boxes']

def __init__(self, max_boxes=4, min_points_in_contour=50, min_area=500, min_box_aspect=5, angle_tol=0.1,
             lineskip_tol=1.5, box_type='bb'):
    self.max_boxes = max_boxes
    self.min_points_in_contour = min_points_in_contour
    self.min_area = min_area
    self.min_box_aspect = min_box_aspect
    self.angle_tol = angle_tol
    self.lineskip_tol = lineskip_tol
    self.box_type = box_type

def __call__(self, img_binary):
    cs = measure.find_contours(img_binary, 0.5)

    # Collect contours into RotatedBoxes
    results = []
    for c in cs:
        ll, ur = np.min(c, 0), np.max(c, 0)
        wh = ur - ll
        if wh[0] * wh[1] < self.min_area:
            continue

        rb = RotatedBox.from_points(c, self.box_type)
        if rb.height == 0 or rb.width / rb.height < self.min_box_aspect:
            continue

        results.append(rb)

    results.sort(key=lambda x: -x.area)
    return self._fixup_boxes(self._merge_boxes(results[0:self.max_boxes]))

def _are_aligned_angles(self, b1, b2):
    return abs(b1 - b2) <= self.angle_tol or abs(np.pi - abs(b1 - b2)) <= self.angle_tol

def _are_nearby_parallel_boxes(self, b1, b2):
    if not self._are_aligned_angles(b1.angle, b2.angle):
        return False
    angle = min(b1.angle, b2.angle)
    return abs(np.dot(b1.center - b2.center, [-np.sin(angle), np.cos(angle)])) < self.lineskip_tol * (
        b1.height + b2.height) and (b1.width > 0) and (b2.width > 0) and (0.5 < b1.width / b2.width < 2.0)

def _merge_any_two_boxes(self, box_list):
    n = len(box_list)
    for i in range(n):
        for j in range(i + 1, n):
            if self._are_nearby_parallel_boxes(box_list[i], box_list[j]):
                a, b = box_list[i], box_list[j]
                merged_points = np.vstack([a.points, b.points])
                merged_box = RotatedBox.from_points(merged_points, self.box_type)
                if merged_box.width / merged_box.height >= self.min_box_aspect:
                    box_list.remove(a)
                    box_list.remove(b)
                    box_list.append(merged_box)
                    return True
    return False

def _merge_boxes(self, box_list):
    while self._merge_any_two_boxes(box_list):
        pass
    return box_list

def _fixup_boxes(self, box_list):
    for box in box_list:
        if abs(abs(box.angle) - np.pi / 2) <= 0.01:
            box.angle = np.pi / 2
        if abs(box.angle) <= 0.01:
            box.angle = 0.0
    return box_list

class ExtractAllBoxes(object): """Extract all the images from the boxes, for external OCR processing"""

__provides__ = ['rois']
__depends__ = ['boxes', 'img', 'scale_factor']

def __call__(self, boxes, img, scale_factor):
    return [box.extract_from_image(img, 1.0 / scale_factor) for box in boxes]

class FindFirstValidMRZ(object): """Iterates over boxes found by MRZBoxLocator, passes them to BoxToMRZ, finds the first valid MRZ or the best-scoring MRZ"""

__provides__ = ['box_idx', 'roi', 'text', 'mrz']
__depends__ = ['boxes', 'img', 'img_small', 'scale_factor', '__data__']

def __init__(self, use_original_image=True, extra_cmdline_params=''):
    self.box_to_mrz = BoxToMRZ(use_original_image, extra_cmdline_params=extra_cmdline_params)

def __call__(self, boxes, img, img_small, scale_factor, data):
    mrzs = []
    data['__debug__mrz'] = []
    for i, b in enumerate(boxes):
        roi, text, mrz = self.box_to_mrz(b, img, img_small, scale_factor)
        data['__debug__mrz'].append((roi, text, mrz))
        if mrz.valid:
            return i, roi, text, mrz
        elif mrz.valid_score > 0:
            mrzs.append((i, roi, text, mrz))
    if not mrzs:
        return None, None, None, None
    else:
        mrzs.sort(key=lambda x: x[3].valid_score)
        return mrzs[-1]

class BoxToMRZ(object): """Extracts ROI from the image, corresponding to a box found by MRZBoxLocator, does OCR and MRZ parsing on this region."""

__provides__ = ['roi', 'text', 'mrz']
__depends__ = ['box', 'img', 'img_small', 'scale_factor']

def __init__(self, use_original_image=True, extra_cmdline_params=''):
    self.use_original_image = use_original_image
    self.extra_cmdline_params = extra_cmdline_params

def __call__(self, box, img, img_small, scale_factor):
    img = img if self.use_original_image else img_small
    scale = 1.0 / scale_factor if self.use_original_image else 1.0
    roi = box.extract_from_image(img, scale)
    text = ocr(roi, extra_cmdline_params=self.extra_cmdline_params)

    if '>>' in text or ('>' in text and '<' not in text):
        roi = roi[::-1, ::-1]
        text = ocr(roi, extra_cmdline_params=self.extra_cmdline_params)

    if '<' not in text:
        return roi, text, MRZ.from_ocr(text)

    mrz = MRZ.from_ocr(text)
    mrz.aux['method'] = 'direct'

    if not mrz.valid:
        text, mrz = self._try_larger_image(roi, text, mrz)

    if not mrz.valid:
        text, mrz = self._try_larger_image(roi, text, mrz, 1)

    if not mrz.valid:
        text, mrz = self._try_black_tophat(roi, text, mrz)

    return roi, text, mrz

def _try_larger_image(self, roi, cur_text, cur_mrz, filter_order=3):
    if roi.shape[1] <= 700:
        scale_by = int(1050.0 / roi.shape[1] + 0.5)
        roi_lg = transform.rescale(roi, scale_by, order=filter_order, mode='constant', channel_axis=None, anti_aliasing=True)
        new_text = ocr(roi_lg, extra_cmdline_params=self.extra_cmdline_params)
        new_mrz = MRZ.from_ocr(new_text)
        new_mrz.aux['method'] = 'rescaled(%d)' % filter_order
        if new_mrz.valid_score > cur_mrz.valid_score:
            cur_mrz = new_mrz
            cur_text = new_text
    return cur_text, cur_mrz

def _try_black_tophat(self, roi, cur_text, cur_mrz):
    roi_b = morphology.black_tophat(roi, morphology.disk(5))
    new_text = ocr(roi_b, extra_cmdline_params=self.extra_cmdline_params)
    new_mrz = MRZ.from_ocr(new_text)
    if new_mrz.valid_score > cur_mrz.valid_score:
        new_mrz.aux['method'] = 'black_tophat'
        cur_text, cur_mrz = new_text, new_mrz

    new_text, new_mrz = self._try_larger_image(roi_b, cur_text, cur_mrz)
    if new_mrz.valid_score > cur_mrz.valid_score:
        new_mrz.aux['method'] = 'black_tophat(rescaled(3))'
        cur_text, cur_mrz = new_text, new_mrz

    return cur_text, cur_mrz

class TryOtherMaxWidth(object): """ If mrz was not found so far in the current pipeline, changes the max_width parameter of the scaler to 1000 and reruns the pipeline again. """

__provides__ = ['mrz_final']
__depends__ = ['mrz', '__pipeline__']

def __init__(self, other_max_width=1000):
    self.other_max_width = other_max_width

def __call__(self, mrz, __pipeline__):
    if mrz is None and (__pipeline__['img_binary'].mean() < 0.01 or __pipeline__['img'].mean() > 0.95):
        __pipeline__.replace_component('scaler', Scaler(self.other_max_width))
        new_mrz = __pipeline__['mrz']
        if new_mrz is not None:
            new_mrz.aux['method'] = new_mrz.aux['method'] + '|max_width(%d)' % self.other_max_width
        mrz = new_mrz
    return mrz

class MRZPipeline(Pipeline): """This is the "currently best-performing" pipeline for parsing MRZ from a given image file."""

def __init__(self, file, extra_cmdline_params=''):
    super(MRZPipeline, self).__init__()
    self.version = '1.0'
    self.file = file
    self.add_component('loader', Loader(file))
    self.add_component('scaler', Scaler())
    self.add_component('boone', BooneTransform())
    self.add_component('box_locator', MRZBoxLocator())
    self.add_component('mrz', FindFirstValidMRZ(extra_cmdline_params=extra_cmdline_params))
    self.add_component('other_max_width', TryOtherMaxWidth())

    self.add_component('extractor', ExtractAllBoxes())

@property
def result(self):
    return self['mrz_final']

def read_mrz(file, save_roi=False, extra_cmdline_params=''): """The main interface function to this module, encapsulating the recognition pipeline. Given an image filename, runs MRZPipeline on it, returning the parsed MRZ object.

:param file: A filename or a stream to read the file data from.
:param save_roi: when this is True, the .aux['roi'] field will contain the Region of Interest where the MRZ was parsed from.
:param extra_cmdline_params:extra parameters to the ocr.py
"""
p = MRZPipeline(file, extra_cmdline_params)
mrz = p.result
if mrz is not None and save_roi:
    mrz.aux['roi'] = p['roi']
return mrz

konstantint / PassportEye

PassportEye does not working with the new version of numpy (2.0) #75