def getAlignedImage(img):
# for i in range(50, 100):
# for j in range(200, 255):
# binary_img = getCanny(img, j, i, 3, 0)
# print(i, j)
# show(binary_img, 10)
binary_img = utils.getCanny(img, 20, 50, 3, 0)
# show(binary_img, 0)
max_contour, max_area = utils.findMaxContour(binary_img)
h, w = img.shape[:2]
bg = np.zeros((h, w, 3), np.uint8)
bg.fill(0)
cv2.drawContours(bg, max_contour, -1, (0, 0, 255), 1)
bg = utils.getCanny(bg, 30, 30, 7, 0)
lines = cv2.HoughLinesP(bg, 1, np.pi / 180, 100, maxLineGap=50)
# 画出边框线
# for i in range(len(lines)):
# for x1, y1, x2, y2 in lines[i]:
# cv2.line(img, (x1, y1), (x2, y2), (112, 255, 0), 2)
# show(img,0)
# 得到四个角坐标
point = utils.getCornerPoint(lines)
print(point)
# boxes = adaPoint(point, ratio)
boxes = np.trunc(point)
boxes = utils.orderPoints(boxes)
warped = utils.warpImage(img, boxes)
utils.show(warped, 0)
return warped
def justify_front(path):
print(path)
img = cv2.imdecode(np.fromfile(path, dtype=np.uint8), 1)
# for i in range(100, 200):
# for j in range(50, 200):
# binary_img = utils.getCanny(img, i, j, 15, 0)
# print(i, j)
# utils.show(binary_img, 1)
binary_img = utils.getCanny(img, 100, 150, 15, 0)
# utils.show(binary_img, 0)
contours, hierarchy = cv2.findContours(binary_img, cv2.RETR_CCOMP,
cv2.CHAIN_APPROX_NONE)
flag = False
for i in range(len(contours)):
x, y, w, h = cv2.boundingRect(contours[i])
area = w * h
if (area > 20000) & (abs(w - h) < 20):
if (x > 400) & (y > 270):
print(path)
flag = True
# print(x, y, w, h)
# img = cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 2)
# utils.show(img, 0)
if flag:
img = utils.rotate_image(img, 180)
utils.show(img, 1)
cv2.imencode('.jpg', img)[1].tofile(path)
def getExpDateCrop(img):
# img = getAlignedImage(img)
back = False
image_grey = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
front_face_detected = frontFaceClassifier.detectMultiScale(image_grey, scaleFactor=1.2,
minNeighbors=5, flags=cv2.CASCADE_SCALE_IMAGE)
if len(front_face_detected) != 0:
back = True
binary_img = utils.getCanny(img, 100, 200, 20, 0)
# for i in range(180,200):
# for j in range(350,400):
# binary_img = utils.getCanny(img, j, i, 15, 0)
# print(i, j)
# utils.show(binary_img, 1)
# utils.show(binary_img, 0)
contours, hierarchy = cv2.findContours(binary_img, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)
recList = []
for i in range(len(contours)):
x, y, w, h = cv2.boundingRect(contours[i])
area = w * h
if (area > 1000) & (h < 80) & (y > 300) & (x > 280) & (x < 400):
recList.append(x)
recList.append(y)
recList.append(w)
recList.append(h)
# cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 3) # 绿色
# utils.show(img, 0)
# 对轮廓坐标进行排序
if len(recList) != 0:
recArray = np.array(recList).reshape(-1, 4)
idex = np.lexsort([recArray[:, 0], recArray[:, -1]])
sorted_data = recArray[idex, :]
# print(sorted_data)
# utils.show(img, 0)
# 找到期限的坐标
# for i in range(len(sorted_data)):
x, y, w, h = sorted_data[0]
print(x, y, w, h)
# cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 3) # 红色
# utils.show(img, 0)
return img[y:y + h, x:x + w], back
else:
print("?????")
return "", back
def crop_main(img_path):
# base_path=img_path.split("/")
img = cv2.imdecode(np.fromfile(img_path, dtype=np.uint8), 1)
img = utils.img_resize(img, 900)
binary_img = utils.getCanny(img, 20, 50, 3, 0)
# max_contour, max_area = utils.findMaxContour(binary_img)
contours, _ = cv2.findContours(binary_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
for i in range(len(contours)):
area = cv2.contourArea(contours[i])
rotate = False
if area > 5000:
x, y, w, h = cv2.boundingRect(contours[i])
if w > h:
if (w / h < 1) | (w / h > 2):
break
else:
print(w, h)
else:
if (h / w < 1) | (h / w > 2):
break
else:
print(w, h)
rotate = True
image = img[y - 10:y + h + 10, x - 10:x + w + 10]
print(image.shape)
if rotate:
image = utils.rotate_image(image, 90)
image = cv2.resize(image, (856, 540), interpolation=cv2.INTER_CUBIC)
print(image.shape)
if len(image.shape) == 3:
image_grey = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
else:
image_grey = image
front_face_detected = frontFaceClassifier.detectMultiScale(image_grey, scaleFactor=1.2,
minNeighbors=5, flags=cv2.CASCADE_SCALE_IMAGE)
if len(front_face_detected) != 0:
for x, y, w, h in front_face_detected:
if x < 856 / 2:
image = utils.rotate_image(image, 180)
# cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 255), 2)
utils.show(image, 1)
name = img_path.split("\\")[1]
# 把框出来的身份证图片另存为
cv2.imencode('.jpg', image)[1].tofile(
"E:/KingT/staff/test/all身份证/" + name + "_" + str(i) + img_path.split("\\")[2])
def detect_two(path1, path2):
global null_list
print("=====detect two=====")
print(path1)
print(path2)
null_flag = False
img1 = cv2.imdecode(np.fromfile(path1, dtype=np.uint8), 1)
img2 = cv2.imdecode(np.fromfile(path2, dtype=np.uint8), 1)
img_list = []
for img in [img1, img2]:
# img = utils.img_resize(img)
# for i in range(0, 100):
# for j in range(0, 100):
# binary_img = utils.getCanny(img, i, j, 6, 0)
# print(i, j)
# utils.show(binary_img, 10)
binary_img = utils.getCanny(img, 20, 60, 20, 0)
# utils.show(utils.img_resize(binary_img), 0)
contours, _ = cv2.findContours(binary_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
for i in range(len(contours)):
rotate = False
x, y, w, h = cv2.boundingRect(contours[i])
# print(x, y, w, h)
# m = cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 2)
# utils.show(utils.img_resize(m), 0)
if (w * h > 25000) & ((w > 275) | (h > 275)) & (x > 0) & (y > 0):
if w > h:
if (w / h < 1) | (w / h > 2):
break
else:
print(w, h)
else:
if (h / w < 1) | (h / w > 2):
break
else:
print(w, h)
rotate = True
if (y - 10 > 0) & (x - 10 > 0):
image = img[y - 10:y + h + 10, x - 10:x + w + 10]
else:
image = img[y:y + h, x:x + w]
if rotate:
image = utils.rotate_image(image, 90)
image = cv2.resize(image, (856, 540), interpolation=cv2.INTER_CUBIC)
# utils.show(image, 0)
img_list.append(image)
while len(img_list) < 2:
print(str(len(img_list)), path1, path2)
print("null")
null_flag = True
null_list.append(path1)
img_list.append(np.zeros((540, 856, 3), dtype=np.uint8))
fname = path1.split("\\")[1]
out_folder_path = "E:/KingT/staff/result/身份证null/" + fname
if null_flag:
pic_name1 = path1.split("\\")[2]
pic_name2 = path2.split("\\")[2]
if not os.path.exists(out_folder_path):
os.mkdir(out_folder_path)
# shutil.copy(path1, out_folder_path + "/" + pic_name1)
# shutil.copy(path2, out_folder_path + "/" + pic_name2)
else:
if os.path.exists(out_folder_path):
# ...
shutil.rmtree(out_folder_path)
# 检测人脸
for i in range(2):
image_grey = cv2.cvtColor(img_list[0], cv2.COLOR_BGR2GRAY)
front_face_detected = frontFaceClassifier.detectMultiScale(image_grey, scaleFactor=1.25,
minNeighbors=5,
flags=cv2.CASCADE_SCALE_IMAGE)
if len(front_face_detected) != 0:
for x, y, w, h in front_face_detected:
# print(x, y, w, h)
if (w > 120) & (x > 300):
# cv2.rectangle(img_list[0], (x, y), (x + w, y + h), (0, 255, 255), 2)
# utils.show(img_list[1], 200)
# utils.show(img_list[0], 200)
return img_list[1], img_list[0]
else:
if i == 1:
return img_list[0], img_list[1]
else:
img_list[0] = utils.rotate_image(img_list[0], 180)
else:
return img_list[0], img_list[1]
def recognize_concat(front_path, back_path, concat):
if concat:
front = cv2.imdecode(np.fromfile(front_path, dtype=np.uint8), 1)
back = cv2.imdecode(np.fromfile(back_path, dtype=np.uint8), 1)
front = cv2.resize(front, (856, 540), interpolation=cv2.INTER_CUBIC)
back = cv2.resize(back, (856, 540), interpolation=cv2.INTER_CUBIC)
bg = np.ones((1110, 876, 3), dtype=np.uint8) * 255
bg[10:550, 10:866] = front
bg[560:1100, 10:866] = back
output_path = "E:/KingT/staff/result/身份证new/" + front_path.split(
"_")[0].split("\\")[1] + ".jpg"
# cv2.imencode('.jpg', bg)[1].tofile(output_path)
global number_list
global exp_date_list
global non_detect_list
for path in [front_path, back_path]:
if path == "":
break
print(path)
name = path.split("-")[1]
img = cv2.imdecode(np.fromfile(path, dtype=np.uint8), 1)
img = cv2.resize(img, (856, 540), interpolation=cv2.INTER_CUBIC)
# for i in range(180,240):
# for j in range(100, 500):
# binary_img = utils.getCanny(img, i, j, 15, 0)
# print(i, j)
# utils.show(binary_img, 1)
binary_img = utils.getCanny(img, 180, 170, 15, 0)
contours, hierarchy = cv2.findContours(binary_img, cv2.RETR_CCOMP,
cv2.CHAIN_APPROX_NONE)
global nonList
recList = []
flag1 = False
for i in range(len(contours)):
x, y, w, h = cv2.boundingRect(contours[i])
if (x > 100) & (x < 500) & (w > 300) & (y > 300) & (h > 30) & (
y < 500):
recList.append(x)
recList.append(y)
recList.append(w)
recList.append(h)
recArray = np.array(recList).reshape(-1, 4)
idex = np.lexsort([recArray[:, 2], recArray[:, 1]])
# 从大到小排
idex = np.flip(idex)
sorted_data = recArray[idex, :]
for data in sorted_data:
x, y, w, h = data[0:4]
if (h < 70) & (w > 200) & (w < 600) & (y + h < 520) & (y + h >
420):
print(path)
print("find1: %d %d %d %d" % (x, y, w, h))
crop = img[y:y + h, x:x + w]
imagegray = cv2.cvtColor(crop, cv2.COLOR_RGB2GRAY)
# for i in range(300):
# retval, imagebin = cv2.threshold(imagegray, i, 255, cv2.THRESH_BINARY+cv2.THRESH_TRUNC)
# utils.show(imagebin, 1)
# text = pytesseract.image_to_string(imagebin, lang='eng')
# print(i, text)
retval, imagebin = cv2.threshold(
imagegray, 20, 255, cv2.THRESH_BINARY + cv2.THRESH_TRUNC)
text = pytesseract.image_to_string(imagebin, lang='eng')
if len(text) == 0:
text = pytesseract.image_to_string(crop, lang='eng')
if len(text) != 0:
if path.find("front") != -1:
text = text.replace("=", "-").replace("~", "-").replace(" ", "") \
.replace(",", ".").replace(":", ".").replace("::", ".").replace("+", ".").replace(";", ".")
exp_date_list.append([name, text])
else:
text = text.replace("$", "3").replace(" ", "")
number_list.append([name, text])
print(text)
else:
non_detect_list.append(path)
print()
# 红色 第一次找到
img = cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255),
2)
flag1 = True
utils.show(img, 1)
break
if not flag1:
utils.show(binary_img, 1)
# for i in range(100, 500):
# for j in range(0, 900):
# binary_img = utils.getCanny(img, i, j, 15, 0)
# print(i, j)
# utils.show(binary_img, 1)
binary_img = utils.getCanny(img, 200, 300, 18, 0)
contours, hierarchy = cv2.findContours(binary_img, cv2.RETR_CCOMP,
cv2.CHAIN_APPROX_NONE)
recList = []
flag2 = False
for i in range(len(contours)):
x, y, w, h = cv2.boundingRect(contours[i])
if (x > 100) & (x < 500) & (w > 300) & (y > 300) & (h > 30) & (
y < 500):
recList.append(x)
recList.append(y)
recList.append(w)
recList.append(h)
recArray = np.array(recList).reshape(-1, 4)
idex = np.lexsort([recArray[:, 2], recArray[:, 1]])
# 从大到小排
idex = np.flip(idex)
sorted_data = recArray[idex, :]
for data in sorted_data:
x, y, w, h = data[0:4]
if (h < 70) & (w > 200) & (w < 600) & (y + h < 520) & (y + h >
420):
print(path)
print("find2: %d %d %d %d" % (x, y, w, h))
crop = img[y:y + h, x:x + w]
imagegray = cv2.cvtColor(crop, cv2.COLOR_RGB2GRAY)
retval, imagebin = cv2.threshold(
imagegray, 20, 255,
cv2.THRESH_BINARY + cv2.THRESH_TRUNC)
text = pytesseract.image_to_string(imagebin, lang='eng')
if len(text) == 0:
text = pytesseract.image_to_string(crop, lang='eng')
if len(text) != 0:
if path.find("front") != -1:
text = text.replace("=", "-").replace("~", "-").replace(" ", "") \
.replace(",", ".").replace(":", ".").replace("::", ".").replace("+", ".").replace(";",
".")
exp_date_list.append([name, text])
else:
text = text.replace("$", "3").replace(" ", "")
number_list.append([name, text])
print(text)
else:
non_detect_list.append(path)
# 绿色 第二次找到
img = cv2.rectangle(img, (x, y), (x + w, y + h),
(0, 255, 0), 2)
flag2 = True
utils.show(img, 1)
break
else:
print(x, y, w, h)
# 蓝色 找到 但是不对
img = cv2.rectangle(img, (x, y), (x + w, y + h),
(255, 0, 0), 2)
utils.show(img, 1)
if not flag2:
nonList.append(path)
print(nonList)
utils.show(binary_img, 1)