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 refine_gfimage(img, mask): #time-consuming-mask
# Draw BBox using gf
bboxes = findBBox(img, mask)
if bboxes is None:
return mask
box_img = np.zeros((img.shape), dtype=np.uint8)
#cv2.drawContours(box_img,[bboxes],-1,(255,255,255),1)
for bbox in bboxes:
bbox_ = resizeBBox(bbox, ratio=1.0)
cv2.drawContours(box_img, [bbox_], 0, (255, 255, 255), 1)
newmask = cv2.cvtColor(box_img, cv2.COLOR_BGR2GRAY)
#
#img_contours,contours,hierarchy = cv2.findContours(newmask, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
contours, hierarchy = cv2.findContours(newmask, cv2.RETR_LIST,
cv2.CHAIN_APPROX_NONE)
img_contours = np.zeros((img.shape), dtype=np.uint8)
if len(contours) == 0:
return mask
for contour in contours:
random.shuffle(contour)
cv2.drawContours(img_contours, [contour], 0, (255, 255, 255), 1)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
newmask = cv2.morphologyEx(img_contours[:, :, 0], cv2.MORPH_CLOSE, kernel)
return newmask
def mask2plates(img, finalmask, isdebug=False):
#showResult("masktest",finalmask)
#ret,binary = cv2.threshold(finalmask,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)\
binary = compositeThreshold(finalmask, mode='otsu')
closing = close(binary)
if isdebug:
showResult("mask2plates:masktest", closing)
# Find Candidate
return findBBox(img, closing, isdebug=isdebug)
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_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