def detect_by_gf(origin,
isdebug=False):
if origin is None:
return None
# Default Size
h,w,c = origin.shape
size = 200.0
# Resize
img = cv2.resize(origin,(int(w*size/h),int(size)))
# Extract Good Features
corners = refinedGoodFeatures(origin,img,
model='LP')
mask = checkFeatures(img,corners,True)
# Find Candidate
bboxes = findBBox(img,mask,
model='LP',
debug=True)
# Resize
if bboxes is not None:
bboxes = resizeBBoxes(bboxes,h/size)
# Check Result
if isdebug and bboxes is not None:
drawBBox(origin,bboxes,debug=True)
# Crop Rois
rois = BBoxes2ROIs(origin,bboxes)
if isdebug and rois is not None:
for i in range(len(rois)):
showResult("cropped",rois[i])
return bboxes,rois
def detect_by_seg_gf(origin,
isdebug=False):
if origin is None:
return None
# Default Size
h,w,c = origin.shape
size = 200.0
#origin = opencv2skimage(origin)
# Resize
img = cv2.resize(origin,(int(w*size/h),int(size)))
# Blur
blur = cv2.GaussianBlur(img,(5,5),3)
# Equalization Hist
#origin = equalizehist(origin)
# Extract Good Features
corners = refinedGoodFeatures(origin,img,
model='LP')
corners_,handwrite = refinedCorners(img,corners,False)
checkFeatures(img,corners,True)
# Opencv2Skimage
skimg = cv2.cvtColor(blur, cv2.COLOR_BGR2RGB)
# Segmentation
out,labels = seg(skimg,Debug=False)
# Eval Label
labels = refineLabels(out,labels,corners_,howmany=20)
# Show Result
out = drawLabels(skimg,labels,Debug=isdebug)
if out is None:
return None
changebgcolr(out,labels)
#showResult("labelout",skimage2opencv(out))
# Find Candidate
bboxes = findBBox(img,out,
model='LP',
debug=isdebug)
# Resize
if bboxes is not None:
bboxes = resizeBBoxes(bboxes,h/size)
# Check Candidate
if isdebug and bboxes is not None:
drawBBox(origin,bboxes,debug=isdebug)
# Crop Rois
rois = BBoxes2ROIs(origin,bboxes)
if isdebug and rois is not None:
for i in range(len(rois)):
showResult("cropped",rois[i])
'''
if isdebug:
#labels2boundaries(labels)
contours = labels2contours(labels)
drawBBox(img,contours,debug=True)
'''
return bboxes,rois
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
def detect_by_probability(origin,
isdebug=False):
if origin is None:
return None
# Default Size
h,w,c = origin.shape
size = 200.0
# Resize
img = cv2.resize(origin,(int(w*size/h),int(size)))
#showResult("img",img)
#
if dayornight(img):
# Extract Good Features
corners = refinedGoodFeatures(origin,img)
mask = checkFeatures(img,corners,False)
closing=close(mask)
refined_gfmask = refine_gfimage(img,closing)
#showResult("refined_gfmask",refined_gfmask)
finalmask = mkfinalmask(img,refined_gfmask,isday=True)
else:
finalmask = mkfinalmask(img,None,isday=False)
#
#showResult("masktest",finalmask)
#ret,binary = cv2.threshold(finalmask,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
binary = compositeThreshold(finalmask,mode='otsu')
kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(5,5))
closing=cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel)
if isdebug:
showResult("masktest",closing)
# Find Candidate
bboxes = findBBox(img,closing,isdebug=True)
# Resize
if bboxes is not None:
bboxes = resizeBBoxes(bboxes,h/size)
# Check Result
if isdebug and bboxes is not None:
drawBBox(origin,bboxes,debug=True)
# Crop Rois
rois = BBoxes2ROIs(origin,bboxes)
if isdebug and rois is not None:
for i in range(len(rois)):
showResult("cropped",rois[i])
return bboxes,rois
def detect_by_cascade(origin, resize_h=720, en_scale=1.06, isdebug=False):
if origin is None:
return None
# Default Size
height, width, channels = origin.shape
# Resize
resized = cv2.resize(origin, (int(width * resize_h / height), resize_h))
# Colr2Gray
gray = cv2.cvtColor(resized, cv2.COLOR_RGB2GRAY)
# Detect by Cascade
watches = watch_cascade.detectMultiScale(gray,
en_scale,
1,
minSize=(36, 9))
cropped_images = []
bboxes = []
for (x, y, w, h) in watches:
xmin = x - w * 0.1
ymin = y - h * 0.6
xmin = xmin if xmin >= 0 else 0
xmin = ymin if ymin >= 0 else 0
xmax = xmin + 1.2 * w
ymax = ymin + 1.1 * h
bboxes.append(
np.array([[xmin, ymin], [xmax, ymin], [xmax, ymax], [xmin, ymax]]))
cropped = cropped_from_image(
gray, (int(xmin), int(ymin), int(1.2 * w), int(1.1 * h)))
cropped_images.append(cropped)
# Resize
if bboxes is not None:
bboxes = resizeBBoxes(bboxes, height / float(resize_h))
# Check Result
if isdebug and bboxes is not None:
drawBBox(origin, bboxes, debug=True)
# Crop Rois
rois = BBoxes2ROIs(origin, bboxes)
if isdebug and rois is not None:
for i in range(len(rois)):
showResult("cropped", rois[i])
return bboxes, rois #cropped_images
