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No commits in common. "81d79a1602cfc8627eb5969fc168324c439e865f" and "b93ef905aede17477a870f59b3f76b9e81f513f2" have entirely different histories.
81d79a1602
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b93ef905ae
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@ -1,2 +1 @@
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env
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__pycache__
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@ -8,27 +8,9 @@ Two scripts:
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## Analysis Steps
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1. Locate positions of 6 hole punches - normalize.py
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2. Orient to square position - normalize.py
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1. Locate positions of 6 hole punches
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2. Orient to square position
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3. Find all 4 fiducials
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4. Calculate their position relative to the hole punches
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5. Create a template
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6. Use the template-filling script to recreate calibration page as a proof to be checked on a lightbox
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## Page Hole Punch Order
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```
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______________
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| 1 3 |
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| 3 4 |
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| 5 6 |
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---------------
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```
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6. Use the template-filling script to recreate calibration page as a proof to be checked on a lightbox
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@ -1,62 +0,0 @@
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import sys
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import cv2
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import numpy as np
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import math
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from os.path import exists, basename
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from common import image_resize, display, normalize_angle
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#clockwise from top left
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order = [ 1, 3, 4, 6, 5, 2 ]
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def read_text (textPath) :
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holePunches = {}
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with open(textPath) as t:
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for line in t:
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i = int(line[0])
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parts = line.split(' : ')
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vals = parts[1].split(',')
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holePunches[i] = {
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'x' : int(vals[0]),
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'y' : int(vals[1])
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}
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return holePunches
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#
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# CALIBRATE
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#
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if len(sys.argv) < 2:
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print('Please provide path of normalized scan to calibrate to')
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exit(1)
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if len(sys.argv) < 3:
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print('Please provide path to output svg template')
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exit(2)
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normalImage = sys.argv[-2]
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if not exists(normalImage) :
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print('Normalized scan does not exist, please provide one that does')
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exit(2)
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normalText = normalImage + '.txt'
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if not exists(normalText) :
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print('Corresponding normalized scan text does not exist, please generate one')
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exit(3)
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outputTmpl = sys.argv[-1]
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print(f'Calibrating to scan {basename(normalImage)}')
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holePunches = read_text(normalText)
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original = cv2.imread(normalImage)
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img = original.copy()
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height, width = img.shape[:2]
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orientation = height > width
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if not orientation :
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print(f'Scan is not in portrait mode, exiting...')
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exit(3)
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display(img)
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@ -1,122 +0,0 @@
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import cv2
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import math
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def image_resize(image, width = None, height = None, inter = cv2.INTER_AREA):
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dim = None
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(h, w) = image.shape[:2]
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if width is None and height is None:
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return image
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if width is None:
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r = height / float(h)
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dim = (int(w * r), height)
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else:
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r = width / float(w)
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dim = (width, int(h * r))
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resized = cv2.resize(image, dim, interpolation = inter)
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return resized
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def display (image) :
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resized = image_resize(image, 800, 800)
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cv2.imshow('img', resized)
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while cv2.getWindowProperty('img', cv2.WND_PROP_VISIBLE) > 0:
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key = cv2.waitKey(0)
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if key == 27:
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cv2.destroyAllWindows()
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break
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exit(0)
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# taken from
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# https://gist.github.com/phn/1111712/35e8883de01916f64f7f97da9434622000ac0390
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def normalize_angle (num, lower=0.0, upper=360.0, b=False):
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"""Normalize number to range [lower, upper) or [lower, upper].
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Parameters
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----------
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num : float
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The number to be normalized.
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lower : float
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Lower limit of range. Default is 0.0.
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upper : float
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Upper limit of range. Default is 360.0.
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b : bool
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Type of normalization. See notes.
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Returns
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-------
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n : float
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A number in the range [lower, upper) or [lower, upper].
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Raises
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------
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ValueError
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If lower >= upper.
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Notes
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-----
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If the keyword `b == False`, the default, then the normalization
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is done in the following way. Consider the numbers to be arranged
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in a circle, with the lower and upper marks sitting on top of each
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other. Moving past one limit, takes the number into the beginning
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of the other end. For example, if range is [0 - 360), then 361
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becomes 1. Negative numbers move from higher to lower
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numbers. So, -1 normalized to [0 - 360) becomes 359.
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If the keyword `b == True` then the given number is considered to
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"bounce" between the two limits. So, -91 normalized to [-90, 90],
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becomes -89, instead of 89. In this case the range is [lower,
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upper]. This code is based on the function `fmt_delta` of `TPM`.
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Range must be symmetric about 0 or lower == 0.
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Examples
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--------
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>>> normalize(-270,-180,180)
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90
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>>> import math
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>>> math.degrees(normalize(-2*math.pi,-math.pi,math.pi))
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0.0
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>>> normalize(181,-180,180)
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-179
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>>> normalize(-180,0,360)
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180
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>>> normalize(36,0,24)
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12
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>>> normalize(368.5,-180,180)
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8.5
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>>> normalize(-100, -90, 90, b=True)
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-80.0
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>>> normalize(100, -90, 90, b=True)
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80.0
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>>> normalize(181, -90, 90, b=True)
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-1.0
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>>> normalize(270, -90, 90, b=True)
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-90.0
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"""
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# abs(num + upper) and abs(num - lower) are needed, instead of
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# abs(num), since the lower and upper limits need not be 0. We need
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# to add half size of the range, so that the final result is lower +
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# <value> or upper - <value>, respectively.
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res = num
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if not b:
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if lower >= upper:
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raise ValueError("Invalid lower and upper limits: (%s, %s)" %
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(lower, upper))
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res = num
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if num > upper or num == lower:
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num = lower + abs(num + upper) % (abs(lower) + abs(upper))
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if num < lower or num == upper:
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num = upper - abs(num - lower) % (abs(lower) + abs(upper))
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res = lower if res == upper else num
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else:
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total_length = abs(lower) + abs(upper)
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if num < -total_length:
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num += math.ceil(num / (-2 * total_length)) * 2 * total_length
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if num > total_length:
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num -= math.floor(num / (2 * total_length)) * 2 * total_length
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if num > upper:
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num = total_length - num
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if num < lower:
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num = -total_length - num
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res = num * 1.0 # Make all numbers float, to be consistent
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return res
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@ -2,12 +2,34 @@ import sys
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import cv2
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import numpy as np
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import math
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from json import dumps
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from os.path import exists
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from common import image_resize, display, normalize_angle
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#clockwise from top left
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order = [ 1, 3, 4, 6, 5, 2 ]
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def image_resize(image, width = None, height = None, inter = cv2.INTER_AREA):
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dim = None
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(h, w) = image.shape[:2]
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if width is None and height is None:
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return image
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if width is None:
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r = height / float(h)
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dim = (int(w * r), height)
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else:
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r = width / float(w)
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dim = (width, int(h * r))
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resized = cv2.resize(image, dim, interpolation = inter)
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return resized
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def display (image) :
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resized = image_resize(image, 800, 800)
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cv2.imshow('img', resized)
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while cv2.getWindowProperty('img', cv2.WND_PROP_VISIBLE) > 0:
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key = cv2.waitKey(0)
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if key == 27:
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cv2.destroyAllWindows()
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break
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exit(0)
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def get_center (contour) :
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M = cv2.moments(contour)
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@ -39,6 +61,98 @@ def is_close (point, points) :
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return True
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return False
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# taken from
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# https://gist.github.com/phn/1111712/35e8883de01916f64f7f97da9434622000ac0390
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def normalize_angle (num, lower=0.0, upper=360.0, b=False):
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"""Normalize number to range [lower, upper) or [lower, upper].
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Parameters
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----------
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num : float
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The number to be normalized.
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lower : float
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Lower limit of range. Default is 0.0.
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upper : float
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Upper limit of range. Default is 360.0.
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b : bool
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Type of normalization. See notes.
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Returns
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-------
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n : float
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A number in the range [lower, upper) or [lower, upper].
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Raises
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------
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ValueError
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If lower >= upper.
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Notes
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-----
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If the keyword `b == False`, the default, then the normalization
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is done in the following way. Consider the numbers to be arranged
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in a circle, with the lower and upper marks sitting on top of each
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other. Moving past one limit, takes the number into the beginning
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of the other end. For example, if range is [0 - 360), then 361
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becomes 1. Negative numbers move from higher to lower
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numbers. So, -1 normalized to [0 - 360) becomes 359.
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If the keyword `b == True` then the given number is considered to
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"bounce" between the two limits. So, -91 normalized to [-90, 90],
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becomes -89, instead of 89. In this case the range is [lower,
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upper]. This code is based on the function `fmt_delta` of `TPM`.
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Range must be symmetric about 0 or lower == 0.
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Examples
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--------
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>>> normalize(-270,-180,180)
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90
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>>> import math
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>>> math.degrees(normalize(-2*math.pi,-math.pi,math.pi))
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0.0
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>>> normalize(181,-180,180)
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-179
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>>> normalize(-180,0,360)
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180
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>>> normalize(36,0,24)
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12
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>>> normalize(368.5,-180,180)
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8.5
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>>> normalize(-100, -90, 90, b=True)
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-80.0
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>>> normalize(100, -90, 90, b=True)
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80.0
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>>> normalize(181, -90, 90, b=True)
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-1.0
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>>> normalize(270, -90, 90, b=True)
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-90.0
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"""
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# abs(num + upper) and abs(num - lower) are needed, instead of
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# abs(num), since the lower and upper limits need not be 0. We need
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# to add half size of the range, so that the final result is lower +
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# <value> or upper - <value>, respectively.
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res = num
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if not b:
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if lower >= upper:
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raise ValueError("Invalid lower and upper limits: (%s, %s)" %
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(lower, upper))
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res = num
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if num > upper or num == lower:
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num = lower + abs(num + upper) % (abs(lower) + abs(upper))
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if num < lower or num == upper:
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num = upper - abs(num - lower) % (abs(lower) + abs(upper))
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res = lower if res == upper else num
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else:
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total_length = abs(lower) + abs(upper)
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if num < -total_length:
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num += math.ceil(num / (-2 * total_length)) * 2 * total_length
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if num > total_length:
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num -= math.floor(num / (2 * total_length)) * 2 * total_length
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if num > upper:
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num = total_length - num
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if num < lower:
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num = -total_length - num
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res = num * 1.0 # Make all numbers float, to be consistent
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return res
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def mean (lst):
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return sum(lst) / len(lst)
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@ -135,15 +249,6 @@ def find_hole_punches (img) :
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return holePunches
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def simplify_hole_punches (holePunches) :
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simple = {}
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for hp in holePunches :
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simple[hp['order']] = {
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'x' : hp['x'],
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'y' : hp['y']
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}
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return simple
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def correct_rotation (img, original, holePunches) :
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horizLines = [
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(3, 1),
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@ -290,10 +395,6 @@ def normalize_image(blank, rotated, offset, tl) :
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return blank
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#
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# NORMALIZE
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#
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if len(sys.argv) < 2:
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print('Please provide path of scan to normalize')
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exit(1)
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@ -303,11 +404,6 @@ if len(sys.argv) < 3:
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exit(2)
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scanImage = sys.argv[-2]
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if not exists(scanImage) :
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print('Scan provided does not exist')
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exit(5)
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normalImage = sys.argv[-1]
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pageDim = (11, 8.5)
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pageRatio = pageDim[1] / pageDim[0]
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@ -359,8 +455,9 @@ print(f'Writing normalized image to {normalImage}')
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cv2.imwrite(normalImage, normal)
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evaluation = find_hole_punches(normal)
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jsonOut = simplify_hole_punches(evaluation)
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with open(f'{normalImage}.json', 'w') as output:
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output.write(dumps(jsonOut, sort_keys = True, indent = 4))
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print(f'Wrote hole punch definition file to {normalImage}.json')
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with open(f'{normalImage}.txt', 'w') as evalFile :
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for hp in evaluation:
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evalFile.write(f'{hp["order"] + 1} : {hp["x"]},{hp["y"]}\n')
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#display(normal)
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Loading…
Reference in New Issue