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utils.py
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"""
Designed and Developed by-
Udayraj Deshmukh
https://github.com/Udayraj123
"""
from time import localtime, strftime, time
from random import randint
from template import *
from globals import *
# In[62]:
import re
import os
import sys
import cv2
import glob
from imutils import grab_contours
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = (10.0, 8.0)
def setup_dirs(paths):
print('\nChecking Directories...')
for _dir in [paths.saveMarkedDir]:
if(not os.path.exists(_dir)):
print('Created : ' + _dir)
os.makedirs(_dir)
os.mkdir(_dir + '/stack')
os.mkdir(_dir + '/_MULTI_')
os.mkdir(_dir + '/_MULTI_' + '/stack')
# os.mkdir(_dir+sl+'/_BADSCAN_')
# os.mkdir(_dir+sl+'/_BADSCAN_'+'/stack')
else:
print('Present : ' + _dir)
for _dir in [paths.manualDir, paths.resultDir]:
if(not os.path.exists(_dir)):
print('Created : ' + _dir)
os.makedirs(_dir)
else:
print('Present : ' + _dir)
for _dir in [paths.multiMarkedDir, paths.errorsDir, paths.badRollsDir]:
if(not os.path.exists(_dir)):
print('Created : ' + _dir)
os.makedirs(_dir)
else:
print('Present : ' + _dir)
def waitQ():
ESC_KEY = 27
while(cv2.waitKey(1) & 0xFF not in [ord('q'), ESC_KEY]):
pass
cv2.destroyAllWindows()
def normalize_util(img, alpha=0, beta=255):
return cv2.normalize(img, alpha, beta, norm_type=cv2.NORM_MINMAX)
def normalize_hist(img):
hist, bins = np.histogram(img.flatten(), 256, [0, 256])
cdf = hist.cumsum()
cdf_m = np.ma.masked_equal(cdf, 0)
cdf_m = (cdf_m - cdf_m.min()) * 255 / (cdf_m.max() - cdf_m.min())
cdf = np.ma.filled(cdf_m, 0).astype('uint8')
return cdf[img]
def resize_util(img, u_width, u_height=None):
if u_height is None:
h, w = img.shape[:2]
u_height = int(h * u_width / w)
return cv2.resize(img, (int(u_width), int(u_height)))
def resize_util_h(img, u_height, u_width=None):
if u_width is None:
h, w = img.shape[:2]
u_width = int(w * u_height / h)
return cv2.resize(img, (int(u_width), int(u_height)))
def show(name, orig, pause=1, resize=False, resetpos=None):
global windowX, windowY, display_width
if(type(orig) == type(None)):
print(name, " NoneType image to show!")
if(pause):
cv2.destroyAllWindows()
return
origDim = orig.shape[:2]
img = resize_util(orig, display_width, display_height) if resize else orig
cv2.imshow(name, img)
if(resetpos):
windowX = resetpos[0]
windowY = resetpos[1]
cv2.moveWindow(name, windowX, windowY)
h, w = img.shape[:2]
# Set next window position
margin = 25
w += margin
h += margin
if(windowX + w > windowWidth):
windowX = 0
if(windowY + h > windowHeight):
windowY = 0
else:
windowY += h
else:
windowX += w
if(pause):
print(
"Showing '" +
name +
"'\n\tPress Q on image to continue Press Ctrl + C in terminal to exit")
waitQ()
def putLabel(img, label, size):
scale = img.shape[1] / display_width
bgVal = int(np.mean(img))
pos = (int(scale * 80), int(scale * 30))
clr = (255 - bgVal,) * 3
img[(pos[1] - size * 30):(pos[1] + size * 2), :] = bgVal
cv2.putText(img, label, pos, cv2.FONT_HERSHEY_SIMPLEX, size, clr, 3)
def drawTemplateLayout(
img,
template,
shifted=True,
draw_qvals=False,
border=-1):
img = resize_util(img, template.dims[0], template.dims[1])
final_align = img.copy()
boxW, boxH = template.bubbleDims
for QBlock in template.QBlocks:
s, d = QBlock.orig, QBlock.dims
shift = QBlock.shift
if(shifted):
cv2.rectangle(
final_align,
(s[0] + shift,
s[1]),
(s[0] + shift + d[0],
s[1] + d[1]),
CLR_BLACK,
3)
else:
cv2.rectangle(
final_align, (s[0], s[1]), (s[0] + d[0], s[1] + d[1]), CLR_BLACK, 3)
for qStrip, qBoxPts in QBlock.traverse_pts:
for pt in qBoxPts:
x, y = (pt.x + QBlock.shift, pt.y) if shifted else (pt.x, pt.y)
cv2.rectangle(final_align,
(int(x + boxW / 10),
int(y + boxH / 10)),
(int(x + boxW - boxW / 10),
int(y + boxH - boxH / 10)),
CLR_GRAY,
border)
if(draw_qvals):
rect = [y, y + boxH, x, x + boxW]
cv2.putText(final_align, '%d' % (cv2.mean(img[rect[0]:rect[1], rect[2]:rect[3]])[
0]), (rect[2] + 2, rect[0] + (boxH * 2) // 3), cv2.FONT_HERSHEY_SIMPLEX, 0.6, CLR_BLACK, 2)
if(shifted):
cv2.putText(final_align, 's%s' %
(shift), tuple(s -
[template.dims[0] //
20, -
d[1] //
2]), cv2.FONT_HERSHEY_SIMPLEX, TEXT_SIZE, CLR_BLACK, 4)
return final_align
def getPlotImg():
plt.savefig('tmp.png')
# img = cv2.imread('tmp.png',cv2.IMREAD_COLOR)
img = cv2.imread('tmp.png', cv2.IMREAD_GRAYSCALE)
os.remove("tmp.png")
# plt.cla()
# plt.clf()
plt.close()
return img
def order_points(pts):
rect = np.zeros((4, 2), dtype="float32")
# the top-left point will have the smallest sum, whereas
# the bottom-right point will have the largest sum
s = pts.sum(axis=1)
rect[0] = pts[np.argmin(s)]
rect[2] = pts[np.argmax(s)]
diff = np.diff(pts, axis=1)
rect[1] = pts[np.argmin(diff)]
rect[3] = pts[np.argmax(diff)]
# return the ordered coordinates
return rect
def four_point_transform(image, pts):
# obtain a consistent order of the points and unpack them
# individually
rect = order_points(pts)
(tl, tr, br, bl) = rect
# compute the width of the new image, which will be the
widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
maxWidth = max(int(widthA), int(widthB))
# maxWidth = max(int(np.linalg.norm(br-bl)), int(np.linalg.norm(tr-tl)))
# compute the height of the new image, which will be the
heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
maxHeight = max(int(heightA), int(heightB))
# maxHeight = max(int(np.linalg.norm(tr-br)), int(np.linalg.norm(tl-br)))
# now that we have the dimensions of the new image, construct
# the set of destination points to obtain a "birds eye view",
# (i.e. top-down view) of the image, again specifying points
# in the top-left, top-right, bottom-right, and bottom-left
# order
dst = np.array([
[0, 0],
[maxWidth - 1, 0],
[maxWidth - 1, maxHeight - 1],
[0, maxHeight - 1]], dtype="float32")
# compute the perspective transform matrix and then apply it
M = cv2.getPerspectiveTransform(rect, dst)
warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))
# return the warped image
return warped
def dist(p1, p2):
return np.linalg.norm(np.array(p1) - np.array(p2))
def get_reflection(pt, pt1, pt2):
pt, pt1, pt2 = tuple(
map(lambda x: np.array(x, dtype=float), [pt, pt1, pt2]))
return (pt1 + pt2) - pt
def printbuf(x):
sys.stdout.write(str(x))
sys.stdout.write('\r')
def get_fourth_pt(three_pts):
m = []
for i in range(3):
m.append(dist(three_pts[i], three_pts[(i + 1) % 3]))
v = max(m)
for i in range(3):
if(m[i] != v and m[(i + 1) % 3] != v):
refl = (i + 1) % 3
break
fourth_pt = get_reflection(
three_pts[refl], three_pts[(refl + 1) % 3], three_pts[(refl + 2) % 3])
return fourth_pt
def angle(p1, p2, p0):
dx1 = float(p1[0] - p0[0])
dy1 = float(p1[1] - p0[1])
dx2 = float(p2[0] - p0[0])
dy2 = float(p2[1] - p0[1])
return (dx1 * dx2 + dy1 * dy2) / \
np.sqrt((dx1 * dx1 + dy1 * dy1) * (dx2 * dx2 + dy2 * dy2) + 1e-10)
def checkMaxCosine(approx):
# assumes 4 pts present
maxCosine = 0
minCosine = 1.5
for i in range(2, 5):
cosine = abs(angle(approx[i % 4], approx[i - 2], approx[i - 1]))
maxCosine = max(cosine, maxCosine)
minCosine = min(cosine, minCosine)
# TODO add to plot dict
# print(maxCosine)
if(maxCosine >= 0.35):
print('Quadrilateral is not a rectangle.')
return False
return True
def validateRect(approx):
# TODO: add logic from app?!
return len(approx) == 4 and checkMaxCosine(approx.reshape(4, 2))
def auto_canny(image, sigma=0.93):
# compute the median of the single channel pixel intensities
v = np.median(image)
# apply automatic Canny edge detection using the computed median
lower = int(max(0, (1.0 - sigma) * v))
upper = int(min(255, (1.0 + sigma) * v))
edged = cv2.Canny(image, lower, upper)
# return the edged image
return edged
def resetSaveImg(key):
global saveImgList
saveImgList[key] = []
def appendSaveImg(key, img):
if(saveimglvl >= int(key)):
global saveImgList
if(key not in saveImgList):
saveImgList[key] = []
saveImgList[key].append(img.copy())
def findPage(image_norm):
# Done: find ORIGIN for the quadrants
# Done, Auto tune! : Get canny parameters tuned
# (https://www.pyimagesearch.com/2015/04/06/zero-parameter-automatic-canny-edge-detection-with-python-and-opencv/)
image_norm = normalize_util(image_norm)
ret, image_norm = cv2.threshold(image_norm, 200, 255, cv2.THRESH_TRUNC)
image_norm = normalize_util(image_norm)
appendSaveImg(1, image_norm)
# kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 10))
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (10, 10))
"""
# Closing is reverse of Opening, Dilation followed by Erosion.
A pixel in the original image (either 1 or 0) will be considered 1 only if all the pixels
under the kernel is 1, otherwise it is eroded (made to zero).
"""
# Close the small holes, i.e. Complete the edges on canny image
closed = cv2.morphologyEx(image_norm, cv2.MORPH_CLOSE, kernel)
appendSaveImg(1, closed)
edge = cv2.Canny(closed, 185, 55)
# findContours returns outer boundaries in CW and inner boundaries in ACW
# order.
cnts = grab_contours(
cv2.findContours(
edge,
cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE))
# hullify to resolve disordered curves due to noise
cnts = [cv2.convexHull(c) for c in cnts]
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:5]
sheet = []
for c in cnts:
if cv2.contourArea(c) < MIN_PAGE_AREA:
continue
peri = cv2.arcLength(c, True)
# ez algo -
# https://en.wikipedia.org/wiki/Ramer–Douglas–Peucker_algorithm
approx = cv2.approxPolyDP(c, epsilon=0.025 * peri, closed=True)
# print("Area",cv2.contourArea(c), "Peri", peri)
# check its rectangle-ness:
if(validateRect(approx)):
sheet = np.reshape(approx, (4, -1))
cv2.drawContours(image_norm, [approx], -1, (0, 255, 0), 2)
cv2.drawContours(edge, [approx], -1, (255, 255, 255), 10)
break
# box = perspective.order_points(box)
# sobel = cv2.addWeighted(cv2.Sobel(edge, cv2.CV_64F, 1, 0, ksize=3),0.5,cv2.Sobel(edge, cv2.CV_64F, 0, 1, ksize=3),0.5,0,edge)
# ExcessDo : make it work on killer images
# edge2 = auto_canny(image_norm)
# show('Morphed Edges',np.hstack((closed,edge)),1,1)
appendSaveImg(1, edge)
return sheet
# Resizing the marker within scaleRange at rate of descent_per_step to
# find the best match.
def getBestMatch(image_eroded_sub, marker):
descent_per_step = (
marker_rescale_range[1] - marker_rescale_range[0]) // marker_rescale_steps
h, w = marker.shape[:2]
res, best_scale = None, None
allMaxT = 0
for r0 in np.arange(
marker_rescale_range[1], marker_rescale_range[0], -1 * descent_per_step): # reverse order
s = float(r0 * 1 / 100)
if(s == 0.0):
continue
rescaled_marker = resize_util_h(
marker if ERODE_SUB_OFF else marker,
u_height=int(
h * s))
# res is the black image with white dots
res = cv2.matchTemplate(
image_eroded_sub,
rescaled_marker,
cv2.TM_CCOEFF_NORMED)
maxT = res.max()
if(allMaxT < maxT):
# print('Scale: '+str(s)+', Circle Match: '+str(round(maxT*100,2))+'%')
best_scale, allMaxT = s, maxT
if(allMaxT < thresholdCircle):
print("\tWarning: Template matching too low! Should you pass --noCropping flag?")
if(showimglvl >= 1):
show("res", res, 1, 0)
if(best_scale is None):
print("No matchings for given scaleRange:", marker_rescale_range)
return best_scale, allMaxT
def adjust_gamma(image, gamma=1.0):
# build a lookup table mapping the pixel values [0, 255] to
# their adjusted gamma values
invGamma = 1.0 / gamma
table = np.array([((i / 255.0) ** invGamma) * 255
for i in np.arange(0, 256)]).astype("uint8")
# apply gamma correction using the lookup table
return cv2.LUT(image, table)
clahe = cv2.createCLAHE(clipLimit=5.0, tileGridSize=(8, 8))
# TODO Fill these for stats
thresholdCircles = []
badThresholds = []
veryBadPoints = []
def getROI(image, filename, noCropping=False):
global clahe
for i in range(saveimglvl):
resetSaveImg(i + 1)
appendSaveImg(1, image)
"""
TODO later Autorotate:
- Rotate 90 : check page width:height, CW/ACW? - do CW, then pass to 180 check.
- Rotate 180 :
Nope, OMR specific, paper warping may be imperfect. - check markers centroid
Nope - OCR check
Match logo - can work, but 'lon' too big and may unnecessarily rotate? - but you know the scale
Check roll field morphed
"""
# TODO: (remove noCropping bool) Automate the case of close up scan(incorrect page)-
# ^Note: App rejects croppeds along with others
# image = resize_util(image, uniform_width, uniform_height)
# Preprocessing the image
img = image.copy()
# TODO: need to detect if image is too blurry already! (M1: check
# noCropping dimensions b4 resizing coz it won't be blurry otherwise _/)
img = cv2.GaussianBlur(img, (3, 3), 0)
image_norm = normalize_util(img)
if(noCropping == False):
# Need this resize for arbitrary high res images: before passing to
# findPage
if(image_norm.shape[1] > uniform_width * 2):
image_norm = resize_util(image_norm, uniform_width * 2)
sheet = findPage(image_norm)
if sheet == []:
print("\tError: Paper boundary not found! Should you pass --noCropping flag?")
return None
else:
print("Found page corners: \t", sheet.tolist())
# Warp layer 1
image_norm = four_point_transform(image_norm, sheet)
# Resize only after cropping the page for clarity as well as uniformity
# for non noCropping images
image_norm = resize_util(image_norm, uniform_width, uniform_height)
image = resize_util(image, uniform_width, uniform_height)
appendSaveImg(1, image_norm)
# Return preprocessed image
return image_norm
def handle_markers(image_norm, marker, curr_filename):
if ERODE_SUB_OFF:
image_eroded_sub = normalize_util(image_norm)
else:
image_eroded_sub = normalize_util(image_norm
- cv2.erode(image_norm,
kernel=np.ones((5, 5)),
iterations=5))
# Quads on warped image
quads = {}
h1, w1 = image_eroded_sub.shape[:2]
midh, midw = h1 // 3, w1 // 2
origins = [[0, 0], [midw, 0], [0, midh], [midw, midh]]
quads[0] = image_eroded_sub[0:midh, 0:midw]
quads[1] = image_eroded_sub[0:midh, midw:w1]
quads[2] = image_eroded_sub[midh:h1, 0:midw]
quads[3] = image_eroded_sub[midh:h1, midw:w1]
# Draw Quadlines
image_eroded_sub[:, midw:midw + 2] = 255
image_eroded_sub[midh:midh + 2, :] = 255
best_scale, allMaxT = getBestMatch(image_eroded_sub, marker)
if(best_scale is None):
# TODO: Plot and see performance of marker_rescale_range
if(showimglvl >= 1):
show('Quads', image_eroded_sub)
return None
optimal_marker = resize_util_h(
marker if ERODE_SUB_OFF else marker, u_height=int(
marker.shape[0] * best_scale))
h, w = optimal_marker.shape[:2]
centres = []
sumT, maxT = 0, 0
print("Matching Marker:\t", end=" ")
for k in range(0, 4):
res = cv2.matchTemplate(quads[k], optimal_marker, cv2.TM_CCOEFF_NORMED)
maxT = res.max()
print("Q" + str(k + 1) + ": maxT", round(maxT, 3), end="\t")
if(maxT < thresholdCircle or abs(allMaxT - maxT) >= thresholdVar):
# Warning - code will stop in the middle. Keep Threshold low to
# avoid.
print(
curr_filename,
"\nError: No circle found in Quad",
k + 1,
"\n\tthresholdVar",
thresholdVar,
"maxT",
maxT,
"allMaxT",
allMaxT,
"Should you pass --noCropping flag?")
if(showimglvl >= 1):
show("no_pts_" + curr_filename, image_eroded_sub, 0)
show("res_Q" + str(k + 1), res, 1)
return None
pt = np.argwhere(res == maxT)[0]
pt = [pt[1], pt[0]]
pt[0] += origins[k][0]
pt[1] += origins[k][1]
# print(">>",pt)
image_norm = cv2.rectangle(image_norm, tuple(
pt), (pt[0] + w, pt[1] + h), (150, 150, 150), 2)
# display:
image_eroded_sub = cv2.rectangle(
image_eroded_sub,
tuple(pt),
(pt[0] + w,
pt[1] + h),
(50,
50,
50) if ERODE_SUB_OFF else (
155,
155,
155),
4)
centres.append([pt[0] + w / 2, pt[1] + h / 2])
sumT += maxT
print("Optimal Scale:", best_scale)
# analysis data
thresholdCircles.append(sumT / 4)
image_norm = four_point_transform(image_norm, np.array(centres))
# appendSaveImg(1,image_eroded_sub)
# appendSaveImg(1,image_norm)
appendSaveImg(2, image_eroded_sub)
# Debugging image -
# res = cv2.matchTemplate(image_eroded_sub,optimal_marker,cv2.TM_CCOEFF_NORMED)
# res[ : , midw:midw+2] = 255
# res[ midh:midh+2, : ] = 255
# show("Markers Matching",res)
if(showimglvl >= 2 and showimglvl < 4):
image_eroded_sub = resize_util_h(image_eroded_sub, image_norm.shape[0])
image_eroded_sub[:, -5:] = 0
h_stack = np.hstack((image_eroded_sub, image_norm))
show("Warped: " + curr_filename, resize_util(h_stack,
int(display_width * 1.6)), 0, 0, [0, 0])
# iterations : Tuned to 2.
# image_eroded_sub = image_norm - cv2.erode(image_norm, kernel=np.ones((5,5)),iterations=2)
return image_norm
def getGlobalThreshold(
QVals_orig,
plotTitle=None,
plotShow=True,
sortInPlot=True,
looseness=1):
"""
Note: Cannot assume qStrip has only-gray or only-white bg (in which case there is only one jump).
So there will be either 1 or 2 jumps.
1 Jump :
......
||||||
|||||| <-- risky THR
|||||| <-- safe THR
....||||||
||||||||||
2 Jumps :
......
|||||| <-- wrong THR
....||||||
|||||||||| <-- safe THR
..||||||||||
||||||||||||
The abstract "First LARGE GAP" is perfect for this.
Current code is considering ONLY TOP 2 jumps(>= MIN_GAP) to be big, gives the smaller one
"""
# Sort the Q vals
QVals = sorted(QVals_orig)
# Find the FIRST LARGE GAP and set it as threshold:
ls = (looseness + 1) // 2
l = len(QVals) - ls
max1, thr1 = MIN_JUMP, 255
for i in range(ls, l):
jump = QVals[i + ls] - QVals[i - ls]
if(jump > max1):
max1 = jump
thr1 = QVals[i - ls] + jump / 2
# NOTE: thr2 is deprecated, thus is JUMP_DELTA
# Make use of the fact that the JUMP_DELTA(Vertical gap ofc) between
# values at detected jumps would be atleast 20
max2, thr2 = MIN_JUMP, 255
# Requires atleast 1 gray box to be present (Roll field will ensure this)
for i in range(ls, l):
jump = QVals[i + ls] - QVals[i - ls]
newThr = QVals[i - ls] + jump / 2
if(jump > max2 and abs(thr1 - newThr) > JUMP_DELTA):
max2 = jump
thr2 = newThr
# globalTHR = min(thr1,thr2)
globalTHR, j_low, j_high = thr1, thr1 - max1 // 2, thr1 + max1 // 2
# # For normal images
# thresholdRead = 116
# if(thr1 > thr2 and thr2 > thresholdRead):
# print("Note: taking safer thr line.")
# globalTHR, j_low, j_high = thr2, thr2 - max2//2, thr2 + max2//2
if(plotTitle is not None):
f, ax = plt.subplots()
ax.bar(range(len(QVals_orig)), QVals if sortInPlot else QVals_orig)
ax.set_title(plotTitle)
thrline = ax.axhline(globalTHR, color='green', ls='--', linewidth=5)
thrline.set_label("Global Threshold")
thrline = ax.axhline(thr2, color='red', ls=':', linewidth=3)
thrline.set_label("THR2 Line")
# thrline=ax.axhline(j_low,color='red',ls='-.', linewidth=3)
# thrline=ax.axhline(j_high,color='red',ls='-.', linewidth=3)
# thrline.set_label("Boundary Line")
# ax.set_ylabel("Mean Intensity")
ax.set_ylabel("Values")
ax.set_xlabel("Position")
ax.legend()
if(plotShow):
plt.title(plotTitle)
plt.show()
return globalTHR, j_low, j_high
def getLocalThreshold(
qNo,
QVals,
globalTHR,
noOutliers,
plotTitle=None,
plotShow=True):
"""
TODO: Update this documentation too-
//No more - Assumption : Colwise background color is uniformly gray or white, but not alternating. In this case there is atmost one jump.
0 Jump :
<-- safe THR?
.......
...|||||||
|||||||||| <-- safe THR?
// How to decide given range is above or below gray?
-> global QVals shall absolutely help here. Just run same function on total QVals instead of colwise _//
How to decide it is this case of 0 jumps
1 Jump :
......
||||||
|||||| <-- risky THR
|||||| <-- safe THR
....||||||
||||||||||
"""
# Sort the Q vals
QVals = sorted(QVals)
# Small no of pts cases:
# base case: 1 or 2 pts
if(len(QVals) < 3):
thr1 = globalTHR if np.max(
QVals) - np.min(QVals) < MIN_GAP else np.mean(QVals)
else:
# qmin, qmax, qmean, qstd = round(np.min(QVals),2), round(np.max(QVals),2), round(np.mean(QVals),2), round(np.std(QVals),2)
# GVals = [round(abs(q-qmean),2) for q in QVals]
# gmean, gstd = round(np.mean(GVals),2), round(np.std(GVals),2)
# # DISCRETION: Pretty critical factor in reading response
# # Doesn't work well for small number of values.
# DISCRETION = 2.7 # 2.59 was closest hit, 3.0 is too far
# L2MaxGap = round(max([abs(g-gmean) for g in GVals]),2)
# if(L2MaxGap > DISCRETION*gstd):
# noOutliers = False
# # ^Stackoverflow method
# print(qNo, noOutliers,"qstd",round(np.std(QVals),2), "gstd", gstd,"Gaps in gvals",sorted([round(abs(g-gmean),2) for g in GVals],reverse=True), '\t',round(DISCRETION*gstd,2), L2MaxGap)
# else:
# Find the LARGEST GAP and set it as threshold: //(FIRST LARGE GAP)
l = len(QVals) - 1
max1, thr1 = MIN_JUMP, 255
for i in range(1, l):
jump = QVals[i + 1] - QVals[i - 1]
if(jump > max1):
max1 = jump
thr1 = QVals[i - 1] + jump / 2
# print(qNo,QVals,max1)
# If not confident, then only take help of globalTHR
if(max1 < CONFIDENT_JUMP):
if(noOutliers):
# All Black or All White case
thr1 = globalTHR
else:
# TODO: Low confidence parameters here
pass
# if(thr1 == 255):
# print("Warning: threshold is unexpectedly 255! (Outlier Delta issue?)",plotTitle)
if(plotShow and plotTitle is not None):
f, ax = plt.subplots()
ax.bar(range(len(QVals)), QVals)
thrline = ax.axhline(thr1, color='green', ls=('-.'), linewidth=3)
thrline.set_label("Local Threshold")
thrline = ax.axhline(globalTHR, color='red', ls=':', linewidth=5)
thrline.set_label("Global Threshold")
ax.set_title(plotTitle)
ax.set_ylabel("Bubble Mean Intensity")
ax.set_xlabel("Bubble Number(sorted)")
ax.legend()
# TODO append QStrip to this plot-
# appendSaveImg(6,getPlotImg())
if(plotShow):
plt.show()
return thr1
# from matplotlib.ticker import MaxNLocator
# def plotArray(QVals, plotTitle, sort = False, plot=True ):
# f, ax = plt.subplots()
# if(sort):
# QVals = sorted(QVals)
# ax.bar(range(len(QVals)),QVals)
# ax.set_title(plotTitle)
# ax.set_ylabel("Values")
# ax.set_xlabel("Position")
# ax.xaxis.set_major_locator(MaxNLocator(integer=True))
# if(plot):
# plt.show()
# # else: they will call this
# # appendSaveImg(appendImgLvl,getPlotImg())
def saveImg(path, final_marked):
print('Saving Image to ' + path)
cv2.imwrite(path, final_marked)
def readResponse(template, image, name, savedir=None, autoAlign=False):
global clahe
try:
img = image.copy()
origDim = img.shape[:2]
# print("noCropping dim", origDim)
img = resize_util(img, template.dims[0], template.dims[1])
# print("Resized dim", img.shape[:2])
if(img.max() > img.min()):
img = normalize_util(img)
# Processing copies
transp_layer = img.copy()
final_marked = img.copy()
# putLabel(final_marked,"Crop Size: " + str(origDim[0])+"x"+str(origDim[1]) + " "+name, size=1)
morph = img.copy()
appendSaveImg(3, morph)
# TODO: evaluate if CLAHE is really req
if(autoAlign):
# Note: clahe is good for morphology, bad for thresholding
morph = clahe.apply(morph)
appendSaveImg(3, morph)
# Remove shadows further, make columns/boxes darker (less gamma)
morph = adjust_gamma(morph, GAMMA_LOW)
ret, morph = cv2.threshold(morph, 220, 220, cv2.THRESH_TRUNC)
morph = normalize_util(morph)
appendSaveImg(3, morph)
if(showimglvl >= 4):
show("morph1", morph, 0, 1)
# Overlay Transparencies
alpha = 0.65
alpha1 = 0.55
boxW, boxH = template.bubbleDims
lang = ['E', 'H']
OMRresponse = {}
multimarked, multiroll = 0, 0
# TODO Make this part useful for visualizing status checks
# blackVals=[0]
# whiteVals=[255]
if(showimglvl >= 5):
# "QTYPE_ROLL":[]}#,"QTYPE_MED":[]}
allCBoxvals = {"Int": [], "Mcq": []}
# ,"QTYPE_ROLL":[]}#,"QTYPE_MED":[]}
qNums = {"Int": [], "Mcq": []}
# Find Shifts for the QBlocks --> Before calculating threshold!
if(autoAlign):
# print("Begin Alignment")
# Open : erode then dilate
# Vertical kernel
v_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 10))
morph_v = cv2.morphologyEx(
morph, cv2.MORPH_OPEN, v_kernel, iterations=3)
ret, morph_v = cv2.threshold(morph_v, 200, 200, cv2.THRESH_TRUNC)
morph_v = 255 - normalize_util(morph_v)
if(showimglvl >= 3):
show("morphed_vertical", morph_v, 0, 1)
# show("morph1",morph,0,1)
# show("morphed_vertical",morph_v,0,1)
appendSaveImg(3, morph_v)
morphTHR = 60 # for Mobile images
# morphTHR = 40 # for scan Images
# best tuned to 5x5 now
_, morph_v = cv2.threshold(
morph_v, morphTHR, 255, cv2.THRESH_BINARY)
morph_v = cv2.erode(
morph_v, np.ones(
(5, 5), np.uint8), iterations=2)
appendSaveImg(3, morph_v)
# h_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (10, 2))
# morph_h = cv2.morphologyEx(morph, cv2.MORPH_OPEN, h_kernel, iterations=3)
# ret, morph_h = cv2.threshold(morph_h,200,200,cv2.THRESH_TRUNC)
# morph_h = 255 - normalize_util(morph_h)
# show("morph_h",morph_h,0,1)
# _, morph_h = cv2.threshold(morph_h,morphTHR,255,cv2.THRESH_BINARY)
# morph_h = cv2.erode(morph_h, np.ones((5,5),np.uint8), iterations = 2)
if(showimglvl >= 3):
show("morph_thr_eroded", morph_v, 0, 1)
appendSaveImg(6, morph_v)
# template alignment code
# OUTPUT : each QBlock.shift is updated
for QBlock in template.QBlocks:
s, d = QBlock.orig, QBlock.dims
# internal constants - wont need change much
# TODO - ALIGN_STRIDE would depend on template's Dimensions
ALIGN_STRIDE, MATCH_COL, ALIGN_STEPS = 1, 5, int(boxW * 2 / 3)
shift, steps = 0, 0
THK = 3
while steps < ALIGN_STEPS:
L = np.mean(morph_v[s[1]:s[1]+d[1],s[0]+shift-THK:-THK+s[0]+shift+MATCH_COL])
R = np.mean(morph_v[s[1]:s[1]+d[1],s[0]+shift-MATCH_COL+d[0]+THK:THK+s[0]+shift+d[0]])
# For demonstration purposes-
if(QBlock.key == "Int1"):
ret = morph_v.copy()
cv2.rectangle(ret,(s[0]+shift-THK,s[1]),(s[0]+shift+THK+d[0],s[1]+d[1]),CLR_WHITE,3)
appendSaveImg(6, ret)
# print(shift, L, R)
LW, RW = L > 100, R > 100
if(LW):
if(RW):
break
else:
shift -= ALIGN_STRIDE
else:
if(RW):
shift += ALIGN_STRIDE
else:
break
steps += 1
QBlock.shift = shift
# print("Aligned QBlock: ",QBlock.key,"Corrected Shift:", QBlock.shift,", Dimensions:", QBlock.dims, "orig:", QBlock.orig,'\n')
# print("End Alignment")
final_align = None
if(showimglvl >= 2):
initial_align = drawTemplateLayout(img, template, shifted=False)
final_align = drawTemplateLayout(
img, template, shifted=True, draw_qvals=True)
# appendSaveImg(4,mean_vals)
appendSaveImg(2, initial_align)
appendSaveImg(2, final_align)
appendSaveImg(5, img)
if(autoAlign):
final_align = np.hstack((initial_align, final_align))
# Get mean vals n other stats
allQVals, allQStripArrs, allQStdVals = [], [], []
totalQStripNo = 0
for QBlock in template.QBlocks:
QStdVals = []
for qStrip, qBoxPts in QBlock.traverse_pts:
QStripvals = []
for pt in qBoxPts:
# shifted
x, y = (pt.x + QBlock.shift, pt.y)
rect = [y, y + boxH, x, x + boxW]
QStripvals.append(
cv2.mean(img[rect[0]:rect[1], rect[2]:rect[3]])[0])
QStdVals.append(round(np.std(QStripvals), 2))
allQStripArrs.append(QStripvals)
# _, _, _ = getGlobalThreshold(QStripvals, "QStrip Plot", plotShow=False, sortInPlot=True)
# hist = getPlotImg()
# show("QStrip "+qBoxPts[0].qNo, hist, 0, 1)
allQVals.extend(QStripvals)
# print(totalQStripNo, qBoxPts[0].qNo, QStdVals[len(QStdVals)-1])
totalQStripNo += 1
allQStdVals.extend(QStdVals)
# print("Begin getGlobalThresholdStd")
globalStdTHR, jstd_low, jstd_high = getGlobalThreshold(allQStdVals)# , "Q-wise Std-dev Plot", plotShow=True, sortInPlot=True)
# print("End getGlobalThresholdStd")
# print("Begin getGlobalThreshold")
# plt.show()
# hist = getPlotImg()
# show("StdHist", hist, 0, 1)
# Note: Plotting takes Significant times here --> Change Plotting args
# to support showimglvl
# , "Mean Intensity Histogram",plotShow=True, sortInPlot=True)
globalTHR, j_low, j_high = getGlobalThreshold(allQVals, looseness=4)
# TODO colorama
print(
"Thresholding:\t\t globalTHR: ", round(
globalTHR, 2), "\tglobalStdTHR: ", round(
globalStdTHR, 2), "\t(Looks like a Xeroxed OMR)" if(
globalTHR == 255) else "")
# plt.show()
# hist = getPlotImg()
# show("StdHist", hist, 0, 1)
# print("End getGlobalThreshold")
# if(showimglvl>=1):
# hist = getPlotImg()
# show("Hist", hist, 0, 1)