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Merge pull request #220 from AlanNewImage/v2 

V2
v2
AlanNewImage GitHub 5 years ago
parent
7be4edb899 74e758e403
commit
afa2da27fc
No known key found for this signature in database GPG Key ID: 4AEE18F83AFDEB23
9 changed files with 537 additions and 12 deletions
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  1. +1
    -0
      .gitignore
  2. +2
    -2
      hyperlpr_pip_pkg/demo.py
  3. +2
    -2
      hyperlpr_pip_pkg/hyperlpr/__init__.py
  4. +28
    -8
      hyperlpr_pip_pkg/hyperlpr/hyperlpr.py
  5. +504
    -0
      hyperlpr_pip_pkg/hyperlpr/models/cascade/detector/cascade_double.xml
  6. BIN
      hyperlpr_pip_pkg/test_images/test.jpg
  7. BIN
      hyperlpr_pip_pkg/test_images/test_db.jpg
  8. BIN
      hyperlpr_pip_pkg/test_images/test_db1.jpg
  9. BIN
      hyperlpr_pip_pkg/test_images/test_db2.jpg

+ 1
- 0
.gitignore View File

@@ -7,3 +7,4 @@ Prj-Linux/lpr/TEST_*
Prj-Linux/*build*/
*.pyc
/Prj-PHP/build
*.idea*

+ 2
- 2
hyperlpr_pip_pkg/demo.py View File

@@ -1,4 +1,4 @@
from hyperlpr import *
import cv2
image = cv2.imread("test.png")
print(HyperLPR_plate_recognition(image,16,charSelectionDeskew=False))
image = cv2.imread("./test_images/test_db2.jpg")
print(HyperLPR_plate_recognition(image,16,charSelectionDeskew=False,DB=True))

+ 2
- 2
hyperlpr_pip_pkg/hyperlpr/__init__.py View File

@@ -4,5 +4,5 @@ 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)
def HyperLPR_plate_recognition(Input_BGR,minSize=30,charSelectionDeskew=True , region = "CH",DB=True):
return PR.plate_recognition(Input_BGR,minSize,charSelectionDeskew,DB)

+ 28
- 8
hyperlpr_pip_pkg/hyperlpr/hyperlpr.py View File

@@ -19,12 +19,14 @@ class LPR():

charLocPath= os.path.join(folder,"cascade/char/char_single.xml")
detectorPath = os.path.join(folder,"cascade/detector/detector_ch.xml")
detectorPathDB = os.path.join(folder,"cascade/detector/cascade_double.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.detectorDB = cv2.CascadeClassifier(detectorPathDB)
self.charLoc = cv2.CascadeClassifier(charLocPath)
self.modelRecognition = cv2.dnn.readNetFromCaffe(*modelRecognitionPath)
self.ssd_detection = cv2.dnn.readNetFromCaffe(*mini_ssd_path)
@@ -64,7 +66,7 @@ class LPR():
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):
def detect_traditional(self,image_gray,resize_h = 720,en_scale =1.1,minSize = 30,DB=True):
"""
Detect the approximate location of plate via opencv build-in cascade detection.
:param image_gray: input single channel image (gray) .
@@ -73,7 +75,10 @@ class LPR():
: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))
if DB:
watches = self.detectorDB.detectMultiScale(image_gray, en_scale, 3, minSize=(minSize*4, minSize))
else:
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
@@ -283,7 +288,7 @@ class LPR():
return self.decode_ctc(y_pred)


def plate_recognition(self,image,minSize=30,charSelectionDeskew=True,mode='ssd'):
def plate_recognition(self,image,minSize=30,charSelectionDeskew=True,DB = 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
@@ -301,14 +306,29 @@ class LPR():
image = cv2.imread("tests/image")
print(pr.plateRecognition(image))
"""

images = self.detect_ssd(image)
if DB:
image_gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
images = self.detect_traditional(image_gray)
else:
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)
print(left, top, right, bottom)
if DB:
w, h = right - left, bottom - top
plate = image[top:bottom,left:right,:]
crop_up = plate[int(h * 0.05):int((h) * 0.4), int(w * 0.2):int(w * 0.75)]
crop_down = plate[int((h) * 0.4):int(h), int(w * 0.05):w]
crop_up = cv2.resize(crop_up, (64, 40))
crop_down = cv2.resize(crop_down, (96, 40))
cropped_finetuned = np.concatenate([crop_up, crop_down], 1)
# cv2.imshow("crop",plate)
# cv2.waitKey(0)
else:

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


+ 504
- 0
hyperlpr_pip_pkg/hyperlpr/models/cascade/detector/cascade_double.xml View File

@@ -0,0 +1,504 @@
<?xml version="1.0"?>
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<featureType>LBP</featureType>
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<weightTrimRate>9.4999999999999996e-01</weightTrimRate>
<maxDepth>1</maxDepth>
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</opencv_storage>

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hyperlpr_pip_pkg/test_images/test.jpg View File

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hyperlpr_pip_pkg/test_images/test_db.jpg View File

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Width: 978  |  Height: 640  |  Size: 154 kB

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hyperlpr_pip_pkg/test_images/test_db1.jpg View File

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Width: 2976  |  Height: 3968  |  Size: 3.3 MB

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hyperlpr_pip_pkg/test_images/test_db2.jpg View File

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Width: 2976  |  Height: 3968  |  Size: 2.4 MB

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