303 lines
7.5 KiB
Python
303 lines
7.5 KiB
Python
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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from json import load,dumps
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DEBUG = True
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#clockwise from top left
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order = [ 0, 2, 3, 5, 4, 1 ]
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matchMethods = ['cv2.TM_CCOEFF', 'cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCORR', 'cv2.TM_CCORR_NORMED', 'cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED']
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def read_text (textPath) :
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holePunches = {}
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with open(textPath) as json:
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holePunches = load(json)
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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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normalImage = sys.argv[-1]
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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 + '.json'
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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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print(f'Calibrating to scan {basename(normalImage)}')
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registrationMark = cv2.imread('./registrationMark.png', 0)
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w, h = registrationMark.shape[:2]
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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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marks = []
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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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#print(holePunches)
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def get_distance(ref, point):
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# print('ref: {} , point: {}'.format(ref, point))
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x1, y1 = ref[0], ref[1]
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x2, y2 = point[0], point[1]
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return math.hypot(x2 - x1, y2 - y1)
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def group_points(points):
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groups = {}
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groupnum = 0
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while len(points) > 1:
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groupnum += 1
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key = str(groupnum)
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groups[key] = []
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ref = points.pop(0)
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for i, point in enumerate(points):
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d = get_distance(ref, point)
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if d < 30:
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groups[key].append(points[i])
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points[i] = None
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points = list(filter(lambda x: x is not None, points))
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return list([[int(np.mean(list([x[0] for x in groups[arr]]))), int(np.mean(list([x[1] for x in groups[arr]])))] for arr in groups])
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def find_closest (pt, pts) :
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return pts[min(range(len(pts)), key = lambda i: get_distance(pts[i], pt))]
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def find_in_half (half) :
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halfGray = cv2.cvtColor(half, cv2.COLOR_BGR2GRAY)
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res = cv2.matchTemplate(halfGray, registrationMark, cv2.TM_CCOEFF_NORMED)
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threshold = 0.7
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loc = np.where( res >= threshold)
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for pt in zip(*loc[::-1]):
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cv2.rectangle(half, pt, (pt[0] + w, pt[1] + h), (0,0,255), 2)
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return list(zip(*loc[::-1]))
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ttly = holePunches['0']['y']-round(height*0.05)
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ttlx = holePunches['0']['x']
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topHalf = img[ttly:holePunches['1']['y']+round(height*0.1), ttlx:holePunches['2']['x']]
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topHalfPts = find_in_half(topHalf)
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thpts = group_points(topHalfPts)
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for pt in thpts :
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#print(f'{ttlx + pt[0]},{ttly + pt[1]}')
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marks.append((ttlx + pt[0] + round(w / 2), ttly + pt[1] + round(h / 2),))
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print(f'Found {len(thpts)} points')
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#display(topHalf)
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btly = holePunches['4']['y']-round(height*0.1)
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btlx = holePunches['3']['x']
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bottomHalf = img[btly:holePunches['5']['y']+round(height*0.05), btlx:holePunches['4']['x']]
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bottomHalfPts = find_in_half(bottomHalf)
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bhpts = group_points(bottomHalfPts)
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for pt in bhpts :
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#print(f'{btlx + pt[0]},{btly + pt[1]}')
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marks.append((btlx + pt[0] + round(w / 2), btly + pt[1] + round(h / 2), ))
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clean = original.copy()
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for pt in marks :
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#print(pt)
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cv2.circle(clean, pt, 50, (0,0,255), -1)
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print(f'Found {len(bhpts)} points')
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if len(marks) != 16 :
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print(f'{len(marks)} != 16 marks')
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exit(1)
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##
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# TOP LEFT
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##
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topLeftTR = find_closest((0,0,), marks)
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#print(topLeftTR)
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topLeftSorted = sorted(marks, key=lambda e: get_distance(e, topLeftTR))
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topLeftSorted.remove(topLeftTR)
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if abs(topLeftSorted[0][1] - topLeftTR[1]) < abs(topLeftSorted[1][1] - topLeftTR[1]) :
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topLeftBR = topLeftSorted[0]
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else :
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topLeftBR = topLeftSorted[1]
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topLeftTL = topLeftSorted[2]
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topLeftBL = topLeftSorted[4]
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if DEBUG :
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cv2.line(clean, topLeftTR, topLeftBR, (0,0,255,), 3)
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cv2.line(clean, topLeftTR, topLeftTL, (0,0,255,), 3)
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cv2.line(clean, topLeftBR, topLeftBL, (0,0,255,), 3)
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cv2.line(clean, topLeftTL, topLeftBL, (0,0,255,), 3)
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##
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# TOP RIGHT
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##
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topRightTL = find_closest((width,0,), marks)
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#print(topRightTL)
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topRightSorted = sorted(topLeftSorted, key=lambda e: get_distance(e, topRightTL))
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topRightSorted.remove(topRightTL)
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topRightSorted.remove(topLeftBR)
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topRightSorted.remove(topLeftTL)
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topRightSorted.remove(topLeftBL)
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topRightBL = topRightSorted[0]
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topRightTR = topRightSorted[1]
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if abs(topRightTL[0] - topRightSorted[2][0]) > abs(topRightTL[0] - topRightSorted[3][0]) :
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topRightBR = topRightSorted[2]
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else :
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topRightBR = topRightSorted[3]
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if DEBUG :
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cv2.line(clean, topRightTR, topRightBR, (0,0,255,), 3)
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cv2.line(clean, topRightTR, topRightTL, (0,0,255,), 3)
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cv2.line(clean, topRightBR, topRightBL, (0,0,255,), 3)
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cv2.line(clean, topRightTL, topRightBL, (0,0,255,), 3)
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##
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# BOTTOM LEFT
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##
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bottomLeftTL = find_closest((0,height,), marks)
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bottomLeftSorted = sorted(topRightSorted, key=lambda e: get_distance(e, bottomLeftTL))
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bottomLeftSorted.remove(bottomLeftTL)
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bottomLeftSorted.remove(topRightBR)
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bottomLeftSorted.remove(topRightTR)
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bottomLeftSorted.remove(topRightBL)
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bottomLeftBL = bottomLeftSorted[0]
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bottomLeftTR = bottomLeftSorted[2]
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bottomLeftBR = bottomLeftSorted[3]
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if DEBUG :
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cv2.line(clean, bottomLeftTR, bottomLeftBR, (0,0,255,), 3)
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cv2.line(clean, bottomLeftTR, bottomLeftTL, (0,0,255,), 3)
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cv2.line(clean, bottomLeftBR, bottomLeftBL, (0,0,255,), 3)
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cv2.line(clean, bottomLeftTL, bottomLeftBL, (0,0,255,), 3)
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##
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# BOTTOM RIGHT
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##
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bottomRightTR = find_closest((width,height,), marks)
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bottomRightSorted = sorted(bottomLeftSorted, key=lambda e: get_distance(e, bottomRightTR))
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bottomRightSorted.remove(bottomRightTR)
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bottomRightSorted.remove(bottomLeftBR)
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bottomRightSorted.remove(bottomLeftTR)
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bottomRightSorted.remove(bottomLeftBL)
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bottomRightBR = bottomRightSorted[0]
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bottomRightTL = bottomRightSorted[1]
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bottomRightBL = bottomRightSorted[2]
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if DEBUG :
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cv2.line(clean, bottomRightTR, bottomRightBR, (0,0,255,), 3)
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cv2.line(clean, bottomRightTR, bottomRightTL, (0,0,255,), 3)
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cv2.line(clean, bottomRightBR, bottomRightBL, (0,0,255,), 3)
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cv2.line(clean, bottomRightTL, bottomRightBL, (0,0,255,), 3)
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jsonOut = {
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'width' : width,
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'height' : height,
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'holePunches' : holePunches,
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'0' : {
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'0' : {
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'x' : bottomLeftTL[0],
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'y' : bottomLeftTL[1]
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},
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'1' : {
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'x' : bottomLeftTR[0],
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'y' : bottomLeftTR[1]
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},
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'2' : {
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'x' : bottomLeftBR[0],
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'y' : bottomLeftBR[1]
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},
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'3' : {
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'x' : bottomLeftBL[0],
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'y' : bottomLeftBL[1]
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}
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},
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'1' : {
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'0' : {
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'x' : topLeftTL[0],
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'y' : topLeftTL[1]
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},
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'1' : {
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'x' : topLeftTR[0],
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'y' : topLeftTR[1]
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},
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'2' : {
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'x' : topLeftBR[0],
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'y' : topLeftBR[1]
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},
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'3' : {
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'x' : topLeftBL[0],
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'y' : topLeftBL[1]
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}
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},
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'2' : {
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'0' : {
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'x' : topRightTL[0],
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'y' : topRightTL[1]
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},
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'1' : {
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'x' : topRightTR[0],
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'y' : topRightTR[1]
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},
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'2' : {
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'x' : topRightBR[0],
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'y' : topRightBR[1]
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},
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'3' : {
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'x' : topRightBL[0],
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'y' : topRightBL[1]
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}
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},
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'3' : {
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'0' : {
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'x' : bottomRightTL[0],
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'y' : bottomRightTL[1]
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},
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'1' : {
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'x' : bottomRightTR[0],
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'y' : bottomRightTR[1]
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},
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'2' : {
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'x' : bottomRightBR[0],
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'y' : bottomRightBR[1]
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},
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'3' : {
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'x' : bottomRightBL[0],
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'y' : bottomRightBL[1]
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}
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}
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}
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calibrationFile = f'{normalImage}.calibration.json'
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with open(calibrationFile, 'w') as output:
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output.write(dumps(jsonOut, sort_keys = True, indent = 4))
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print(f'Wrote calibration file to {calibrationFile}')
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display(clean)
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