def __init__(self, image=None):
if image is not None:
self.bgr = image
self.licenplatevalidator = LicensePlate()
self.confidence = 0.0
self.isFound = False
from detect_angle import detect_angle
import html
from misc.switch import switch
#from os import listdir
from os.path import exists #,isfile, join
app = Flask(__name__) #, static_folder='static', static_url_path='')
UPLOAD_FOLDER = 'uploads'
WEBFILE_FOLDER = 'webfiles'
app.qualityanalyzer = AnalyzeImageQuality()
app.classifier = Classifier()
app.detector = VehicleDetector()
app.licenseplatedetector = LicensePlateDetector()
app.licenseplate = LicensePlate()
app.vehicleidentifier = VIN()
app.results = []
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
app.config['WEBFILE_FOLDER'] = WEBFILE_FOLDER
app.config["CACHE_TYPE"] = "null"
app.config['ALLOWED_EXTENSIONS'] = set(
['txt', 'pdf', 'png', 'jpg', 'jpeg', 'gif'])
app.config['MAX_CONTENT_LENGTH'] = 3 * 1024 * 1024 # 1 Mb limit
app.image_fn = os.path.join(app.config['UPLOAD_FOLDER'], "image.jpg")
app.result_fn = os.path.join(app.config['UPLOAD_FOLDER'], "result.txt")
app.filename = ""
#cache = Cache(config={'CACHE_TYPE': 'redis'})
class LicensePlateDetector:
def __init__(self, image=None):
if image is not None:
self.bgr = image
self.licenplatevalidator = LicensePlate()
self.confidence = 0.0
self.isFound = False
def initialize(self):
self.bgr = None
def preprocess(self, image):
self.bgr = image
gray = cv2.cvtColor(self.bgr, cv2.COLOR_BGR2GRAY)
self.gray = icontrast(255 - gray)
self.laplacian = Laplacian(gray)
#self.sobel = Sobel(gray)
#self.entropy = Entropy(gray)
#self.garbor = Garbor(gray)
self.DoG = DoG(gray)
self.lthr = AdaptiveThreshold(self.laplacian)
self.tophat = tophatmask(gray)
#tophatblackhat(gray)
masks = []
#masks.append(self.tophat)
masks.append(self.DoG)
self.compose = maskize(self.lthr, masks)
self.contour = np.zeros((self.compose.shape[:2]), np.uint8)
def showall(self):
showResult("laplacian", self.laplacian)
showResult("lthr", self.lthr)
#showResult("DoG",self.DoG)
showResult("tophat", self.tophat)
showResult("compose", self.compose)
def process(self, image):
return self.detect(image)
#self.preprocess(image)
#self.showall()
def detect(self, origin, isdebug=False):
start = time.time()
# Default Size
h, w, c = origin.shape
size = 200.0
# Resize
img = cv2.resize(origin, (int(w * size / h), int(size)))
#showResult("img",img)
for case in switch(AnalyzeImageQuality.dayornight(img)):
if case('Day'):
# Extract Good Features
corners = refinedGoodFeatures(origin, img)
mask = checkFeatures(img, corners, isdebug)
closing = close(mask)
refined_gfmask = refine_gfimage(img, closing)
#showResult("refined_gfmask",refined_gfmask)
finalmasks = mkfinalmasks(img,
refined_gfmask,
isday=True,
isdebug=isdebug)
break
if case('Night'):
finalmasks = mkfinalmasks(img, None, isday=isdebug)
break
for colrindex, fmask in enumerate(finalmasks):
if (fmask > 0).sum() == 0:
continue
bboxes = mask2plates(img, fmask)
# Resize
if bboxes is not None:
bboxes = resizeBBoxes(bboxes, h / size)
rois = BBoxes2ROIs(origin, bboxes)
for i, roi in enumerate(rois):
confidence = self.licenplatevalidator.process(
roi, mode=colrs[colrindex], isdebug=isdebug)
print confidence
if confidence > 0.7:
#pts = self.licenplatevalidator.getRefinedROI()
#bbox = refineBBox(bboxes[i],pts)
bbox = resizeBBox(bboxes[i], ratio=0.9)
print("total elapsed time: " +
str(int((time.time() - start) * 1000) / 1000.0) +
"s")
return confidence, [bbox], [roi]
'''
# Check Result
if isdebug and bboxes is not None:
drawBBox(origin,bboxes,debug=True)
for i in range(len(rois)):
showResult("cropped",rois[i])
'''
return 0.0, None, None
#https://docs.opencv.org/2.4/modules/features2d/doc/feature_detection_and_description.html#mser
#https://stackoverflow.com/questions/17647500/exact-meaning-of-the-parameters-given-to-initialize-mser-in-opencv-2-4-x
#https://fossies.org/dox/opencv-3.3.0/mser_8py_source.html
# Text Detection
#http://blog.csdn.net/windtalkersm/article/details/53027685
#http://digital.cs.usu.edu/~vkulyukin/vkweb/teaching/cs7900/Paper1.pdf
# Fill in contour
#https://stackoverflow.com/questions/44185854/extract-text-from-image-using-mser-in-opencv-python
# opencv Python tutorial
#https://github.com/opencv/opencv/tree/master/samples/python
from licenseplate import LicensePlate
from detector import LicensePlateDetector
###
import time
lp = LicensePlate()
lpdetector = LicensePlateDetector()
def main():
start = time.time()
# Load Image
image = cv2.imread(fullpath)
# Load Model
#detector = VehicleDetector()
# Detect Vehicle
bbox_car = detect(opencv2skimage(image)) #mpimg.imread(path)
if bbox_car is not None:
img_car = cropImg_by_BBox(image, bbox_car)
# Detect License Plate
start_detect_lp = time.time()