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 findContoursAndDrawBoundingBox(image_rgb):
line_upper = []
line_lower = []
line_experiment = []
grouped_rects = []
gray_image = cv2.cvtColor(image_rgb, cv2.COLOR_BGR2GRAY)
# for k in np.linspace(-1.5, -0.2,10):
for k in np.linspace(0, 50, 16):
# 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 = cv2.adaptiveThreshold(gray_image, 255,
cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY_INV, 17, k)
cv2.imshow("image1", 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] < 1200)
or (bdbox[3] / float(bdbox[2]) > 3
and bdbox[3] * bdbox[2] < 100)):
#cv2.rectangle(image_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), 3)
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), -3)
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)
# image,M = deskew.fastDeskew(image)
return image
def findContoursAndDrawBoundingBox(image_rgb):
line_upper = []
line_lower = []
line_experiment = []
grouped_rects = []
gray_image = cv2.cvtColor(image_rgb, cv2.COLOR_BGR2GRAY) # 转换为灰度图
# for k in np.linspace(-1.5, -0.2,10):
for k in np.linspace(-50, 0, 15): # linspace：均分指令
# 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 = cv2.adaptiveThreshold(
gray_image, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 17,
k) # AdaptiveThreshold 将灰度图像变换到二值图像；
imagex, contours, hierarchy = cv2.findContours(
binary_niblack.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE) # 寻找图像轮廓
for contour in contours:
bdbox = cv2.boundingRect(
contour) # 输入二值图，返回四个值，分别是x，y，w，h；x，y是矩阵左上点的坐标，w，h是矩阵的宽和高
if (bdbox[3] / float(bdbox[2]) > 0.7 and bdbox[3] * bdbox[2] > 100
and bdbox[3] * bdbox[2] < 1200) 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) # copyMakeBorder：扩充image_rgb的边缘，将图像变大，此处输入为彩色图
leftyA, rightyA = fitLine_ransac(np.array(line_lower), 3)
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), -3)
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) # 由四对点计算透射变换
image = cv2.warpPerspective(rgb, mat, (136, 36),
flags=cv2.INTER_CUBIC) # 对图像进行透视变换
image, M = deskew.fastDeskew(image) # 校正角度
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):
# 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
