def findContoursAndDrawBoundingBox2(image_rgb):
line_upper = []
line_lower = []
line_experiment = []
grouped_rects = []
gray_image = cv2.cvtColor(image_rgb, cv2.COLOR_BGR2GRAY)
for k in np.linspace(-1.6, -0.2, 10):
# for k in np.linspace(-15, 0, 15):
# #
# thresh_niblack = threshold_niblack(gray_image, window_size=21, k=k)
# binary_niblack = gray_image > thresh_niblack
# binary_niblack = binary_niblack.astype(np.uint8) * 255
binary_niblack = nt.niBlackThreshold(gray_image, 19, k)
# cv2.imshow("binary_niblack_opencv",binary_niblack_)
# cv2.imshow("binary_niblack_skimage", binary_niblack)
# cv2.waitKey(0)
imagex, contours, hierarchy = cv2.findContours(binary_niblack.copy(),
cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
for contour in contours:
bdbox = cv2.boundingRect(contour)
if (bdbox[3] / float(bdbox[2]) > 0.7 and bdbox[3] * bdbox[2] > 100
and bdbox[3] * bdbox[2] < 1000) or (
bdbox[3] / float(bdbox[2]) > 3
and bdbox[3] * bdbox[2] < 100):
# cv2.rectangle(rgb,(bdbox[0],bdbox[1]),(bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]),(255,0,0),1)
line_upper.append([bdbox[0], bdbox[1]])
line_lower.append([bdbox[0] + bdbox[2], bdbox[1] + bdbox[3]])
line_experiment.append([bdbox[0], bdbox[1]])
line_experiment.append(
[bdbox[0] + bdbox[2], bdbox[1] + bdbox[3]])
# grouped_rects.append(bdbox)
rgb = cv2.copyMakeBorder(image_rgb, 30, 30, 0, 0, cv2.BORDER_REPLICATE)
leftyA, rightyA = fitLine_ransac(np.array(line_lower), 2)
rows, cols = rgb.shape[:2]
# rgb = cv2.line(rgb, (cols - 1, rightyA), (0, leftyA), (0, 0, 255), 1,cv2.LINE_AA)
leftyB, rightyB = fitLine_ransac(np.array(line_upper), -4)
rows, cols = rgb.shape[:2]
# rgb = cv2.line(rgb, (cols - 1, rightyB), (0, leftyB), (0,255, 0), 1,cv2.LINE_AA)
pts_map1 = np.float32([[cols - 1, rightyA], [0, leftyA],
[cols - 1, rightyB], [0, leftyB]])
pts_map2 = np.float32([[136, 36], [0, 36], [136, 0], [0, 0]])
mat = cv2.getPerspectiveTransform(pts_map1, pts_map2)
image = cv2.warpPerspective(rgb, mat, (136, 36), flags=cv2.INTER_CUBIC)
image, M = pd.fastDeskew(image)
return image
def findContoursAndDrawBoundingBox2(image_rgb):
line_upper = []
line_lower = []
line_experiment = []
grouped_rects = []
gray_image = cv2.cvtColor(image_rgb, cv2.COLOR_BGR2GRAY)
for k in np.linspace(-1.6, -0.2, 10):
binary_niblack = nt.niBlackThreshold(gray_image, 19, k)
imagex, contours, hierarchy = cv2.findContours(
binary_niblack.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for contour in contours:
bdbox = cv2.boundingRect(contour)
if (bdbox[3] / float(bdbox[2]) > 0.7 and bdbox[3] * bdbox[2] > 100 and bdbox[3] * bdbox[2] < 1000) or (bdbox[3] / float(bdbox[2]) > 3 and bdbox[3] * bdbox[2] < 100):
line_upper.append([bdbox[0], bdbox[1]])
line_lower.append([bdbox[0] + bdbox[2], bdbox[1] + bdbox[3]])
line_experiment.append([bdbox[0], bdbox[1]])
line_experiment.append(
[bdbox[0] + bdbox[2], bdbox[1] + bdbox[3]])
rgb = cv2.copyMakeBorder(image_rgb, 30, 30, 0, 0, cv2.BORDER_REPLICATE)
leftyA, rightyA = fitLine_ransac(np.array(line_lower), 2)
rows, cols = rgb.shape[:2]
leftyB, rightyB = fitLine_ransac(np.array(line_upper), -4)
rows, cols = rgb.shape[:2]
pts_map1 = np.float32(
[[cols - 1, rightyA], [0, leftyA], [cols - 1, rightyB], [0, leftyB]])
pts_map2 = np.float32([[136, 36], [0, 36], [136, 0], [0, 0]])
mat = cv2.getPerspectiveTransform(pts_map1, pts_map2)
image = cv2.warpPerspective(rgb, mat, (136, 36), flags=cv2.INTER_CUBIC)
image, M = deskew.fastDeskew(image)
return image
def refineCrop(sections, width=16):
new_sections = []
for section in sections:
# cv2.imshow("section¡",section)
# cv2.blur(section,(3,3),3)
sec_center = np.array([section.shape[1] / 2, section.shape[0] / 2])
binary_niblack = nt.niBlackThreshold(section, 17, -0.255)
imagex, contours, hierarchy = cv2.findContours(binary_niblack,
cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
boxs = []
for contour in contours:
x, y, w, h = cv2.boundingRect(contour)
ratio = w / float(h)
if ratio<1 and h>36*0.4 and y<16\
:
box = [x, y, w, h]
boxs.append([box, np.array([x + w / 2, y + h / 2])])
# cv2.rectangle(section,(x,y),(x+w,y+h),255,1)
# print boxs
dis_ = np.array([((one[1] - sec_center)**2).sum() for one in boxs])
if len(dis_) == 0:
kernal = [0, 0, section.shape[1], section.shape[0]]
else:
kernal = boxs[dis_.argmin()][0]
center_c = (kernal[0] + kernal[2] / 2, kernal[1] + kernal[3] / 2)
w_2 = int(width / 2)
h_2 = kernal[3] / 2
if center_c[0] - w_2 < 0:
w_2 = center_c[0]
new_box = [center_c[0] - w_2, kernal[1], width, kernal[3]]
# print new_box[2]/float(new_box[3])
if new_box[2] / float(new_box[3]) > 0.5:
# print "异常"
h = int((new_box[2] / 0.35) / 2)
if h > 35:
h = 35
new_box[1] = center_c[1] - h
if new_box[1] < 0:
new_box[1] = 1
new_box[3] = h * 2
section = section[new_box[1]:new_box[1] + new_box[3],
new_box[0]:new_box[0] + new_box[2]]
# cv2.imshow("section",section)
# cv2.waitKey(0)
new_sections.append(section)
# print new_box
return new_sections
def findContoursAndDrawBoundingBox2(image_rgb):
line_upper = [];
line_lower = [];
line_experiment = []
grouped_rects = []
gray_image = cv2.cvtColor(image_rgb,cv2.COLOR_BGR2GRAY)
for k in np.linspace(-1.6, -0.2,10):
# for k in np.linspace(-15, 0, 15):
# #
# thresh_niblack = threshold_niblack(gray_image, window_size=21, k=k)
# binary_niblack = gray_image > thresh_niblack
# binary_niblack = binary_niblack.astype(np.uint8) * 255
binary_niblack = nt.niBlackThreshold(gray_image,19,k)
# cv2.imshow("binary_niblack_opencv",binary_niblack_)
# cv2.imshow("binary_niblack_skimage", binary_niblack)
# cv2.waitKey(0)
imagex, contours, hierarchy = cv2.findContours(binary_niblack.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
for contour in contours:
bdbox = cv2.boundingRect(contour)
if (bdbox[3]/float(bdbox[2])>0.7 and bdbox[3]*bdbox[2]>100 and bdbox[3]*bdbox[2]<1000) or (bdbox[3]/float(bdbox[2])>3 and bdbox[3]*bdbox[2]<100):
# cv2.rectangle(rgb,(bdbox[0],bdbox[1]),(bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]),(255,0,0),1)
line_upper.append([bdbox[0],bdbox[1]])
line_lower.append([bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]])
line_experiment.append([bdbox[0],bdbox[1]])
line_experiment.append([bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]])
# grouped_rects.append(bdbox)
rgb = cv2.copyMakeBorder(image_rgb,30,30,0,0,cv2.BORDER_REPLICATE)
leftyA, rightyA = fitLine_ransac(np.array(line_lower),2)
rows,cols = rgb.shape[:2]
# rgb = cv2.line(rgb, (cols - 1, rightyA), (0, leftyA), (0, 0, 255), 1,cv2.LINE_AA)
leftyB, rightyB = fitLine_ransac(np.array(line_upper),-4)
rows,cols = rgb.shape[:2]
# rgb = cv2.line(rgb, (cols - 1, rightyB), (0, leftyB), (0,255, 0), 1,cv2.LINE_AA)
pts_map1 = np.float32([[cols - 1, rightyA], [0, leftyA],[cols - 1, rightyB], [0, leftyB]])
pts_map2 = np.float32([[136,36],[0,36],[136,0],[0,0]])
mat = cv2.getPerspectiveTransform(pts_map1,pts_map2)
image = cv2.warpPerspective(rgb,mat,(136,36),flags=cv2.INTER_CUBIC)
image,M= deskew.fastDeskew(image)
return image
def refineCrop(sections,width=16):
new_sections = []
for section in sections:
# cv2.imshow("section¡",section)
# cv2.blur(section,(3,3),3)
sec_center = np.array([section.shape[1]/2,section.shape[0]/2])
binary_niblack = nt.niBlackThreshold(section,17,-0.255)
imagex, contours, hierarchy = cv2.findContours(binary_niblack,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
boxs = []
for contour in contours:
x,y,w,h = cv2.boundingRect(contour)
ratio = w/float(h)
if ratio<1 and h>36*0.4 and y<16\
:
box = [x,y,w,h]
boxs.append([box,np.array([x+w/2,y+h/2])])
# cv2.rectangle(section,(x,y),(x+w,y+h),255,1)
# print boxs
dis_ = np.array([ ((one[1]-sec_center)**2).sum() for one in boxs])
if len(dis_)==0:
kernal = [0, 0, section.shape[1], section.shape[0]]
else:
kernal = boxs[dis_.argmin()][0]
center_c = (kernal[0]+kernal[2]/2,kernal[1]+kernal[3]/2)
w_2 = int(width/2)
h_2 = kernal[3]/2
if center_c[0] - w_2< 0:
w_2 = center_c[0]
new_box = [center_c[0] - w_2,kernal[1],width,kernal[3]]
# print new_box[2]/float(new_box[3])
if new_box[2]/float(new_box[3])>0.5:
# print "异常"
h = int((new_box[2]/0.35 )/2)
if h>35:
h = 35
new_box[1] = center_c[1]- h
if new_box[1]<0:
new_box[1] = 1
new_box[3] = h*2
section = section[new_box[1]:new_box[1]+new_box[3],new_box[0]:new_box[0]+new_box[2]]
# cv2.imshow("section",section)
# cv2.waitKey(0)
new_sections.append(section)
# print new_box
return new_sections