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v2.0

v2
jackyu1127 5 years ago
parent
1766dbf82c
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27764bfed7
12 changed files with 12595 additions and 0 deletions
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  1. +11
    -0
      hyperlpr_pip_pkg/.idea/hyperlpr_python_pkg.iml
  2. +7
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  3. +8
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  5. +4
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      hyperlpr_pip_pkg/demo.py
  6. +19
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      hyperlpr_pip_pkg/hyperlpr/LICENSE
  7. +8
    -0
      hyperlpr_pip_pkg/hyperlpr/__init__.py
  8. +317
    -0
      hyperlpr_pip_pkg/hyperlpr/hyperlpr.py
  9. +10408
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      hyperlpr_pip_pkg/hyperlpr/models/cascade/char/char_single.xml
  10. +1242
    -0
      hyperlpr_pip_pkg/hyperlpr/models/cascade/detector/detector_ch.xml
  11. +5
    -0
      hyperlpr_pip_pkg/hyperlpr/table_chs.py
  12. +33
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      hyperlpr_pip_pkg/setup.py

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hyperlpr_pip_pkg/.idea/hyperlpr_python_pkg.iml View File

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hyperlpr_pip_pkg/demo.py View File

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from hyperlpr import *
import cv2
image = cv2.imread("test.png")
print(HyperLPR_plate_recognition(image,16,charSelectionDeskew=False))

+ 19
- 0
hyperlpr_pip_pkg/hyperlpr/LICENSE View File

@@ -0,0 +1,19 @@
Copyright (c) 2018 The Python Packaging Authority

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

+ 8
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hyperlpr_pip_pkg/hyperlpr/__init__.py View File

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name = "hyperlpr_python_pkg"
import sys
from .hyperlpr import LPR
import os

PR = LPR(os.path.join(os.path.split(os.path.realpath(__file__))[0],"models"))
def HyperLPR_plate_recognition(Input_BGR,minSize=30,charSelectionDeskew=True , region = "CH"):
return PR.plate_recognition(Input_BGR,minSize,charSelectionDeskew)

+ 317
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hyperlpr_pip_pkg/hyperlpr/hyperlpr.py View File

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#coding=utf-8
import cv2
import numpy as np
import os
from .table_chs import chars




class LPR():
def __init__(self,folder):
"""
Init the recognition instance.

:param model_detection: opencv cascade model which detecting license plate.
:param model_finemapping: finemapping model which deskew the license plate
:param model_rec: CNN based sequence recognition model trained with CTC loss.
"""

charLocPath= os.path.join(folder,"cascade/char/char_single.xml")
detectorPath = os.path.join(folder,"cascade/detector/detector_ch.xml")
modelRecognitionPath = [os.path.join(folder,"dnn/SegmenationFree-Inception.prototxt"),os.path.join(folder,"dnn/SegmenationFree-Inception.caffemodel")]
modelFineMappingPath= [os.path.join(folder,"dnn/HorizonalFinemapping.prototxt"),os.path.join(folder,"dnn/HorizonalFinemapping.caffemodel")]
mini_ssd_path= [os.path.join(folder,"dnn/mininet_ssd_v1.prototxt"),os.path.join(folder,"dnn/mininet_ssd_v1.caffemodel")]
refine_net_path = [os.path.join(folder,"dnn/refinenet.prototxt"),os.path.join(folder,"dnn/refinenet.caffemodel")]

self.detector = cv2.CascadeClassifier(detectorPath)
self.charLoc = cv2.CascadeClassifier(charLocPath)
self.modelRecognition = cv2.dnn.readNetFromCaffe(*modelRecognitionPath)
self.ssd_detection = cv2.dnn.readNetFromCaffe(*mini_ssd_path)
self.refine_net = cv2.dnn.readNetFromCaffe(*refine_net_path)

def detect_ssd(self,im):
"""
Detect the approximate location of plate via single shot detector based on modified mobilenet.
:param im: input image (BGR) .
:return: [[cropped,x1,y2,x2,y2] ,... ]
"""
_im = im.copy()
pixel_means = [0.406, 0.456, 0.485]
pixel_stds = [0.225, 0.224, 0.229]
pixel_scale = 255.0
rows, cols, c = im.shape
im_tensor = np.zeros((1, 3, im.shape[0], im.shape[1]))
im = im.astype(np.float32)
for i in range(3):
im_tensor[0, i, :, :] = (im[:, :, 2 - i] / pixel_scale - pixel_means[2 - i]) / pixel_stds[2 - i]
self.ssd_detection.setInput(im_tensor)
print(im_tensor.shape)
cropped_images = []
cvOut = self.ssd_detection.forward()
for detection in cvOut[0, 0, :, :]:
score = float(detection[2])
if score > 0.5:
x1 = int(detection[3] * cols)
y1 = int(detection[4] * rows)
x2 = int(detection[5] * cols)
y2 = int(detection[6] * rows)
x1 = max(x1,0)
y1 = max(y1,0)
x2 = min(x2,im.shape[1]-1)
y2 = min(y2,im.shape[0]-1)
cropped = _im[y1:y2,x1:x2]
cropped_images.append([cropped ,[x1,y1,x2,y2]])
return cropped_images

def detect_traditional(self,image_gray,resize_h = 720,en_scale =1.1,minSize = 30):
"""
Detect the approximate location of plate via opencv build-in cascade detection.
:param image_gray: input single channel image (gray) .
:param resize_h: adjust input image size to a fixed size.
:param en_scale: the ratio of image between every scale of images in cascade detection.
:param minSize: minSize of plate increase this parameter can increase the speed of detection.
:return: the results.
"""
watches = self.detector.detectMultiScale(image_gray, en_scale, 3, minSize=(minSize*4, minSize))
cropped_images = []
for (x, y, w, h) in watches:
x -= w * 0.14
w += w * 0.28
y -= h * 0.15
h += h * 0.35
x1 = int(x)
y1 = int(y)
x2 = int(x+w)
y2 = int(y+h)
x1 = max(x1,0)
y1 = max(y1,0)
x2 = min(x2,image_gray.shape[1]-1)
y2 = min(y2,image_gray.shape[0]-1)
cropped = image_gray[y1:y2,x1:x2]
cropped_images.append([cropped ,[x1,y1,x2,y2]])
return cropped_images


def loose_crop(self,image, box, aspect_ratio, padding_ratio=1.7):
"""
Crop the image with an extend rectangle.
:param image: input image (BGR).
:param box: origin bounding box.
:param aspect_ratio: the aspect ratio that need to keep.
:param padding_ratio: padding ratio of origin rectangle.
:return: the cropped image
"""
x1, y1, x2, y2 = box
cx, cy = ((x2 + x1) // 2, (y2 + y1) // 2)
if (x2 - x1) / (y2 - y1) > aspect_ratio:
_w = int((x2 - x1) * padding_ratio)
_h = int(_w / aspect_ratio)
else:
_h = int((y2 - y1) * padding_ratio)
_w = int(_h * aspect_ratio)
x1, y1, x2, y2 = cx - _w // 2, cy - _h // 2, cx + _w // 2, cy + _h // 2
x1 = int(max(x1, 0))
y1 = int(max(y1, 0))
cropped = image[y1:y2, x1:x2]
return cropped


def decode_ctc(self,y_pred):
"""
Decode the results from the last layer of recognition model.
:param y_pred: the feature map output last feature map.
:return: decode results.
"""
results = ""
confidence = 0.0
y_pred = y_pred.T
table_pred = y_pred
res = table_pred.argmax(axis=1)
for i,one in enumerate(res):
if one<len(chars) and (i==0 or (one!=res[i-1])):
results+= chars[one]
confidence+=table_pred[i][one]
confidence/= len(results)
return results,confidence

def fit_ransac(self,pts, zero_add=0):
"""
fit a line and use RANSAC algorithm to reject outlier.
:param pts: input pts
:return: The line border around the image on the location of the point
"""
if len(pts) >= 2:
[vx, vy, x, y] = cv2.fitLine(pts, cv2.DIST_HUBER, 0, 0.01, 0.01)
lefty = int((-x * vy / vx) + y)
righty = int(((136 - x) * vy / vx) + y)
return lefty + 30 + zero_add, righty + 30 + zero_add
return 0, 0

def fine_mapping(self,image_rgb):
"""
fit plate upper and lower with multi-threshold method to segment single character.
:param image_rgb:
:return: fined image.
"""

line_upper = [];
line_lower = [];
line_experiment = []
if image_rgb.ndim == 3:
gray_image = cv2.cvtColor(image_rgb, cv2.COLOR_BGR2GRAY)
else:
gray_image = image_rgb

for k in np.linspace(-50, 0, 16):
binary_niblack = cv2.adaptiveThreshold(gray_image, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 17,k)
if cv2.__version__[0] == "4":
contours, hierarchy = cv2.findContours(binary_niblack.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
else:
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)):
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 = self.fit_ransac(np.array(line_lower), 3)
leftyB, rightyB = self.fit_ransac(np.array(line_upper), -3)
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)
return image


def fine_mapping_by_selecting(self,image_rgb,line_upper,line_lower ):
"""
fit plate upper and lower with detecting character boundingbox
:param image_rgb: input image
:param line_upper: padding of upper
:param line_lower: padding of lower
:return: fined image.
"""


rgb = cv2.copyMakeBorder(image_rgb, 30, 30, 0, 0, cv2.BORDER_REPLICATE)
leftyA, rightyA = self.fit_ransac(np.array(line_lower), 3)
leftyB, rightyB = self.fit_ransac(np.array(line_upper), -3)
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)
return image

def to_refine(self,image, pts, scale=3.0):
"""
refine the image by input points.
:param image_rgb: input image
:param pts: points
"""
x1, y1, x2, y2, x3, y3, x4, y4 = pts.ravel()
cx, cy = int(128 // 2), int(48 // 2)
cw = 64
ch = 24
tx1 = cx - cw // 2
ty1 = cy - ch // 2
tx2 = cx + cw // 2
ty2 = cy - ch // 2
tx3 = cx + cw // 2
ty3 = cy + ch // 2
tx4 = cx - cw // 2
ty4 = cy + ch // 2
target_pts = np.array([[tx1, ty1], [tx2, ty2], [tx3, ty3], [tx4, ty4]]).astype(np.float32) * scale
org_pts = np.array([[x1, y1], [x2, y2], [x3, y3], [x4, y4]]).astype(np.float32)
mat_ = cv2.estimateRigidTransform(org_pts, target_pts, True)
dsize = (int(120 * scale), int(48 * scale))
warped = cv2.warpAffine(image, mat_, dsize)
return warped

def affine_crop(self, image, pts):
"""
crop a image by affine transform.
:param image_rgb: input image
:param pts: points
"""
x1, y1, x2, y2, x3, y3, x4, y4 = pts.ravel()
target_pts = np.array([[0, 0], [136, 0], [136, 36], [0, 36]]).astype(np.float32)
org_pts = np.array([[x1, y1], [x2, y2], [x3, y3], [x4, y4]]).astype(np.float32)
mat = cv2.getPerspectiveTransform(org_pts, target_pts)
dsize = (136, 36)
warped = cv2.warpPerspective(image, mat, dsize)
return warped

def finetune(self,image_, stage=2):
"""
cascade fine tune a image by regress four corner of plate.
:param image_rgb: input image
:param stages: cascade stage
"""

tof = image_.copy()
image = cv2.resize(tof, (120, 48))
blob = cv2.dnn.blobFromImage(image, size=(120, 48), swapRB=False, mean=(127.5, 127.5, 127.5), scalefactor=0.0078125, crop=False)
self.refine_net.setInput(blob)
h, w, c = image_.shape
pts = (self.refine_net.forward("conv6-3").reshape(4, 2) * np.array([w, h])).astype(np.int)
g = self.to_refine(image_, pts)
blob = cv2.dnn.blobFromImage(g, size=(120, 48), swapRB=False, mean=(127.5, 127.5, 127.5), scalefactor=0.0078125, crop=False)
self.refine_net.setInput(blob)
h, w, c = g.shape
pts = (self.refine_net.forward("conv6-3").reshape(4, 2) * np.array([w, h])).astype(np.int)
cropped = self.affine_crop(g, pts)
return cropped


def segmentation_free_recognition(self,src):
"""
return: ctc decode results
"""
temp = cv2.resize(src,( 160,40))
temp = temp.transpose(1, 0, 2)
blob = cv2.dnn.blobFromImage(temp, 1/255.0, (40, 160), (0,0,0), False, False)
self.modelRecognition.setInput(blob)
y_pred = self.modelRecognition.forward()[0]
y_pred = y_pred[:,2:,:]
y_pred = np.squeeze(y_pred)
return self.decode_ctc(y_pred)


def plate_recognition(self,image,minSize=30,charSelectionDeskew=True,mode='ssd'):
"""
the simple pipline consists of detection . deskew , fine mapping alignment, recognition.
:param image: the input BGR image from imread used by opencv
:param minSize: the minSize of plate
:param charSelectionDeskew: use character detection when fine mapping stage which will reduce the False Accept Rate as far as possible.
:return: will return [ [plate1 string ,confidence1, location1 ],
[plate2 string ,confidence2, location2 ] ....
]

usage:
import cv2
import numpy as np
from hyperlpr import LPR
pr = LPR("models")
image = cv2.imread("tests/image")
print(pr.plateRecognition(image))
"""

images = self.detect_ssd(image)
res_set = []
for j,plate in enumerate(images):
plate,[left,top,right,bottom] = plate
print(left,top,right,bottom)
cropped = self.loose_crop(image, [left, top, right, bottom], 120 / 48)
cropped_finetuned = self.finetune(cropped)
res, confidence = self.segmentation_free_recognition(cropped_finetuned)
res_set.append([res,confidence,[left,top,right,bottom ]])
return res_set


#

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hyperlpr_pip_pkg/hyperlpr/models/cascade/char/char_single.xml
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hyperlpr_pip_pkg/hyperlpr/models/cascade/detector/detector_ch.xml
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hyperlpr_pip_pkg/hyperlpr/table_chs.py View File

@@ -0,0 +1,5 @@
chars = [u"京", u"沪", u"津", u"渝", u"冀", u"晋", u"蒙", u"辽", u"吉", u"黑", u"苏", u"浙", u"皖", u"闽", u"赣", u"鲁", u"豫", u"鄂", u"湘", u"粤", u"桂",
u"琼", u"川", u"贵", u"云", u"藏", u"陕", u"甘", u"青", u"宁", u"新", u"0", u"1", u"2", u"3", u"4", u"5", u"6", u"7", u"8", u"9", u"A",
u"B", u"C", u"D", u"E", u"F", u"G", u"H", u"J", u"K", u"L", u"M", u"N", u"P", u"Q", u"R", u"S", u"T", u"U", u"V", u"W", u"X",
u"Y", u"Z",u"港",u"学",u"使",u"警",u"澳",u"挂",u"军",u"北",u"南",u"广",u"沈",u"兰",u"成",u"济",u"海",u"民",u"航",u"空"
]

+ 33
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hyperlpr_pip_pkg/setup.py View File

@@ -0,0 +1,33 @@
import setuptools

# with open("README.md", "r") as fh:
# long_description = fh.read()
long_description = "LPR framework"
setuptools.setup(
name="hyperlpr",
version="0.0.2",
author="Jack Yu",
author_email="jack-yu-business@foxmail.com",
description="A general small license plate recognition framework",
long_description=long_description,
long_description_content_type="text/markdown",
url="https://github.com/zeusees/HyperLPR",
packages=setuptools.find_packages(),
classifiers=(
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 2",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
),
install_requires=[
"opencv-python"
],
package_data={
"hyperlpr": [
"models/cascade/char/*.*",
"models/cascade/detector/*.*",
"models/dnn/*.*"
]
},

)

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