for c in cnts:
peri=cv2.arcLength(c,True)
approx=cv2.approxPolyDP(c,0.02*peri,True)
if len(approx)==4:
screenCnt=approx
break
print("Step 2: Find contours of paper")
cv2.drawContours(image, [screenCnt], -1,(0,255,0),2)
# cv2.imshow("Outline",image)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
warped=four_point_transform(orig, screenCnt.reshape(4,2)*ratio)
warped=cv2.cvtColor(warped,cv2.COLOR_BGR2GRAY)
T=threshold_local(warped,11,offset=10,method="gaussian")
warped=(warped > T).astype("uint8")*255
print("Step 3: Apply perspective transform")
imS=cv2.resize(warped,(650,650))
# cv2.imshow("output",imS)
# cv2.imwrite('Output Image.png',imS)
# cv2.waitKey(0)
from PIL import Image
import PIL.Image
from pytesseract import image_to_string
def extract_rois(image_path, scoreboard_id):
color_image = cv2.imread(image_path, cv2.IMREAD_COLOR)
apply_tl = cv2.matchTemplate(color_image,
scoreboard_templates[scoreboard_id]["tl"],
cv2.TM_CCOEFF_NORMED)
apply_tr = cv2.matchTemplate(color_image,
scoreboard_templates[scoreboard_id]["tr"],
cv2.TM_CCOEFF_NORMED)
apply_bl = cv2.matchTemplate(color_image,
scoreboard_templates[scoreboard_id]["bl"],
cv2.TM_CCOEFF_NORMED)
apply_br = cv2.matchTemplate(color_image,
scoreboard_templates[scoreboard_id]["br"],
cv2.TM_CCOEFF_NORMED)
minval_tl, maxval_tl, minloc_tl, maxloc_tl = cv2.minMaxLoc(apply_tl)
minval_tr, maxval_tr, minloc_tr, maxloc_tr = cv2.minMaxLoc(apply_tr)
minval_bl, maxval_bl, minloc_bl, maxloc_bl = cv2.minMaxLoc(apply_bl)
minval_br, maxval_br, minloc_br, maxloc_br = cv2.minMaxLoc(apply_br)
if maxval_tl < .92 or maxval_tr < .92 or maxval_bl < .92 or maxval_br < .92:
return {"error": "Couldn't identify scoreboard for %s" % image_path}
sbpos_tl = addTuples(maxloc_tl,
scoreboard_template_offsets[scoreboard_id]["tl"])
sbpos_tr = addTuples(maxloc_tr,
scoreboard_template_offsets[scoreboard_id]["tr"])
sbpos_bl = addTuples(maxloc_bl,
scoreboard_template_offsets[scoreboard_id]["bl"])
sbpos_br = addTuples(maxloc_br,
scoreboard_template_offsets[scoreboard_id]["br"])
cv2.line(color_image, sbpos_tl, sbpos_tr, (255, 0, 0), 1)
cv2.line(color_image, sbpos_tr, sbpos_br, (255, 0, 0), 1)
cv2.line(color_image, sbpos_br, sbpos_bl, (255, 0, 0), 1)
cv2.line(color_image, sbpos_bl, sbpos_tl, (255, 0, 0), 1)
cv2.imshow("scoreboard_vis", color_image)
cv2.waitKey(0)
cv2.destroyWindow("scoreboard_vis")
corrected = fpt.four_point_transform(
color_image,
np.array([sbpos_tl, sbpos_tr, sbpos_bl, sbpos_br], dtype="float32"))
red_divider_y = round(corrected.shape[0] / 3)
yellow_divider_y = red_divider_y * 2
offset_left = 30
cell_width = 15.85
# Extract ROIs
red_cells = []
yellow_cells = []
for i in range(12):
left_with_extra_room = offset_left + round(cell_width * i)
right_with_extra_room = left_with_extra_room + round(cell_width) + 4
red_roi = corrected[0:red_divider_y,
left_with_extra_room:right_with_extra_room]
yellow_roi = corrected[yellow_divider_y:,
left_with_extra_room:right_with_extra_room]
red_cells.append(red_roi)
yellow_cells.append(yellow_roi)
return {"red": red_cells, "yellow": yellow_cells, "full": corrected}
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 0)
edged = cv2.Canny(gray, 75, 200)
#Step2: Find contour representing the paper being scanned
img, contours, hierarchies = cv2.findContours(edged.copy(), cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
cnts = sorted(contours, key=cv2.contourArea, reverse=True)[:5]
for c in cnts:
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.02 * peri, True)
if (len(approx) == 4):
page = approx
break
#Step 3: Apply a Perspective Transform & Threshold
warped = four_point_transform(orig, page.reshape(4, 2) * ratio)
warped = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)
th1 = cv2.adaptiveThreshold(warped, 255, cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY, 11, 8)
th2 = cv2.adaptiveThreshold(warped, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY, 15, 8)
warped = cv2.addWeighted(src1=th1, alpha=0.7, src2=th2, beta=0.3, gamma=0)
cv2.imshow("Original", imutils.resize(orig, height=650))
cv2.imshow("Scanned", imutils.resize(warped, height=650))
cv2.waitKey(0)
edged=cv2.Canny(blurred,th1,th2)
img,cnts,hierarchies=cv2.findContours(edged.copy(),cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
cnts=sorted(cnts,key=cv2.contourArea,reverse=True)
for c in cnts:
peri=cv2.arcLength(c,True)
approx=cv2.approxPolyDP(c,0.02*peri,True)
if len(approx)==4:
exam_page=approx
break
paper=four_point_transform(image,exam_page.reshape(4,2))
warped=four_point_transform(gray,exam_page.reshape(4,2))
ret,thresh=cv2.threshold(warped,0,255,cv2.THRESH_BINARY_INV|cv2.THRESH_OTSU)
cnts2=cv2.findContours(thresh.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
cnts2=imutils.grab_contours(cnts2)
qstn_cnts=[]
for c in cnts2:
(x,y,w,h)=cv2.boundingRect(c)
ar=w/float(h)
if w >= 20 and h >= 20 and ar >= 0.9 and ar <= 1.1:
qstn_cnts.append(c)