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fix sbd #2

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fix sbd #2

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181 changes: 96 additions & 85 deletions scan_service.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,8 @@
import time
import traceback

BORDER_FOR_SORT = 5

def get_result_trac_nghiem(image_trac_nghiem, ANSWER_KEY, debug):
translate = {"A": 0, "B": 1, "C": 2, "D": 3}
revert_translate={0:"A",1:"B",2:"C",3:"D",-1:"N"}
Expand Down Expand Up @@ -142,95 +144,104 @@ def get_result_trac_nghiem(image_trac_nghiem, ANSWER_KEY, debug):


def get_sbd(image_sbd, debug):
image = image_sbd
height, width, channels = image.shape
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.adaptiveThreshold(gray, maxValue=255,
adaptiveMethod=cv2.ADAPTIVE_THRESH_MEAN_C,
thresholdType=cv2.THRESH_BINARY_INV ,
blockSize=15,
C=8)
#
# cv2.imshow("cropped", thresh)
# cv2.waitKey(0)

cnts = cv2.findContours(thresh.copy(), cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)

questionCnts = []
for c in cnts:
(x, y, w, h) = cv2.boundingRect(c)
ar = w / float(h)
if w >=width/13 and h >= height/13 and ar >= 0.7 and ar <= 1.3 and w < width/2 and h<=height/8 :
questionCnts.append(c)

questionCnts = contours.sort_contours(questionCnts,
method="top-to-bottom")[0]
# cv2.drawContours(image, questionCnts, -1, (0, 255, 0), 3)
# cv2.imshow("cropped", image)
# cv2.waitKey(0)

if debug['on']:
cv2.drawContours(image, questionCnts, -1, (0, 255, 0), 3)
cv2.imwrite(debug['folder'] +'contours_sbd1.jpeg', image)

if len(questionCnts)!=100:
thresh = cv2.threshold(gray, 0, 255,
cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
cnts = cv2.findContours(thresh.copy(), cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
questionCnts = []
for c in cnts:
(x, y, w, h) = cv2.boundingRect(c)
ar = w / float(h)
if w >= width / 13 and h >= height / 13 and ar >= 0.7 and ar <= 1.3 and w < width / 2 and h <= height / 8:
questionCnts.append(c)

questionCnts = contours.sort_contours(questionCnts,
method="top-to-bottom")[0]
cv2.drawContours(image, questionCnts, -1, (0, 255, 0), 3)
#cv2.imshow("cropped", image)
#cv2.waitKey(0)

if debug['on']:
cv2.imwrite(debug['folder'] +'contours_sbd.jpeg', image)

sbd = []
thresh = cv2.threshold(gray, 0, 255,
cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
for i in range(0,10):
list_questionCnts=[]
for j1 in range(0,10):
list_questionCnts.append(questionCnts[i+j1*10])
cnts = contours.sort_contours(list_questionCnts,method="top-to-bottom")[0]
bubbled = None
min = 100000000
total=0
for (j, c) in enumerate(cnts):
image = image_sbd.copy()
height, width, channels = image.shape
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.adaptiveThreshold(gray, maxValue=255,
adaptiveMethod=cv2.ADAPTIVE_THRESH_MEAN_C,
thresholdType=cv2.THRESH_BINARY_INV ,
blockSize=15,
C=8)
#
# cv2.imshow("cropped", thresh)
# cv2.waitKey(0)

mask = np.zeros(thresh.shape, dtype="uint8")
cv2.drawContours(mask, [c], -1, 255, -1)
mask = cv2.bitwise_and(thresh, thresh, mask=mask)
total = cv2.countNonZero(mask)
# print(j)
# cv2.imshow("cropped", mask)
# cv2.waitKey(0)
if total <= min:
min = total
if bubbled is None or total > bubbled[0]:
bubbled = (total, j)
cnts = cv2.findContours(thresh.copy(), cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)

if bubbled[0] < min * 1.4:
bubbled = (total, -1)
questionCnts = []
for c in cnts:
(x, y, w, h) = cv2.boundingRect(c)
ar = w / float(h)
if w >=width/13 and h >= height/13 and ar >= 0.7 and ar <= 1.3 and w < width/2 and h<=height/8 :
questionCnts.append(c)

sbd.append(bubbled[1])
questionCnts_sorted = sorted(questionCnts, key=lambda contour: (cv2.boundingRect(contour)[0]//BORDER_FOR_SORT, cv2.boundingRect(contour)[1]//BORDER_FOR_SORT))
# cv2.drawContours(image, questionCnts, -1, (0, 255, 0), 3)
# cv2.imshow("cropped", image)
# cv2.waitKey(0)

if bubbled[1]!=-1:
color = list(np.random.random(size=3) * 256)
cv2.drawContours(image, [cnts[bubbled[1]]], -1, color, 3)
return sbd[::-1],image
if debug['on']:
cv2.drawContours(image, questionCnts, -1, (0, 255, 0), 3)
cv2.imwrite(debug['folder'] +'contours_sbd1.jpeg', image)
logging.info(f"do dai sbd contours {len(questionCnts)}")
if len(questionCnts_sorted)!=100:
thresh = cv2.threshold(gray, 0, 255,
cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
cnts = cv2.findContours(thresh.copy(), cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
questionCnts_sorted = []
for c in cnts:
(x, y, w, h) = cv2.boundingRect(c)
ar = w / float(h)
if w >= width / 13 and h >= height / 13 and ar >= 0.7 and ar <= 1.3 and w < width / 2 and h <= height / 8:
questionCnts.append(c)
questionCnts_sorted = sorted(questionCnts, key=lambda contour: (
cv2.boundingRect(contour)[0] // BORDER_FOR_SORT, cv2.boundingRect(contour)[1] // BORDER_FOR_SORT))

# questionCnts = contours.sort_contours(questionCnts,
# method="top-to-bottom")[0]
cv2.drawContours(image, questionCnts_sorted, -1, (0, 255, 0), 3)
#cv2.imshow("cropped", image)
#cv2.waitKey(0)

if debug['on']:
cv2.imwrite(debug['folder'] +'contours_sbd.jpeg', image)

sbd = []
thresh = cv2.threshold(gray, 0, 255,
cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
if debug['on']:
cv2.imwrite(debug['folder'] + 'sbd-threshed.jpeg', thresh)

for i in range(0,10):
list_questionCnts=[]
for j1 in range(0,10):
list_questionCnts.append(questionCnts_sorted[j1+i*10])
cv2.drawContours(image, questionCnts_sorted, j1+i*10, (255,0,0), -1)

if debug['on']:
cv2.imwrite(debug['folder'] + f'sbd-cot-{str(current_milli_time())}.jpeg', image)
cnts = sorted(list_questionCnts, key=lambda contour: (
cv2.boundingRect(contour)[0] // BORDER_FOR_SORT, cv2.boundingRect(contour)[1] // BORDER_FOR_SORT))
bubbled = None
min = 100000000
total=0
for (j, c) in enumerate(cnts):

mask = np.zeros(thresh.shape, dtype="uint8")
cv2.drawContours(mask, [c], -1, 255, -1)
mask = cv2.bitwise_and(thresh, thresh, mask=mask)
total = cv2.countNonZero(mask)
# print(j)
# cv2.imshow("cropped", mask)
# cv2.waitKey(0)
if total <= min:
min = total
if bubbled is None or total > bubbled[0]:
bubbled = (total, j)

if bubbled[0] < min * 1.4:
bubbled = (total, -1)

sbd.append(bubbled[1])

if bubbled[1]!=-1:
color = list(np.random.random(size=3) * 256)
cv2.drawContours(image, [cnts[bubbled[1]]], -1, color, 3)
return sbd, image



Expand Down