PassportEye does not working with the new version of numpy (2.0)
Rheagal98 opened this issue · 3 comments
As the numpy version does not specified in the setup file, Passporteye automatically install the lastest version of numpy, causing this error
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
new image.py