def detectVisibleSide(self, image):
imageCopy = cv.CloneImage(image)
colorSegmenter = ColorSegmenter()
greenSegmentation = colorSegmenter.segmentImageByColor(imageCopy, colorSegmenter.green, 3, 4)
contours = self.__findContoursInPicture__(greenSegmentation)
middleHeight = self.__getMiddleHeightOfImage__(imageCopy)
contours = self.__removeContoursOnBottom__(contours, middleHeight)
if len(contours) > 0:
return self.WEST_SIDE
else:
return self.EAST_SIDE
def search(self, queryKps, queryDescs):
results = {}
for samplePath in self.samplePaths:
obj = cv2.imread(samplePath)
# segmenter = DinoSegmenter2()
segImage = ColorSegmenter.getMagentaBlob(obj)
# gray = cv2.cvtColor(segImage, cv2.COLOR_BGR2GRAY)
(kps, descs) = self.descriptor.describe(segImage)
score = self.match(queryKps, queryDescs, kps, descs)
results[samplePath] = score
if len(results) > 0:
# sort the result for having the most possible match at the first
results = sorted([(a,b) for (b,a) in results.items() if a > 0], reverse = True)
return results
avg2 = np.float32(f)
background = BackgroundRemoval.preprocessbackground(c, f, avg2)
_, f = c.read()
gray = cv2.cvtColor(f, cv2.COLOR_BGR2GRAY)
while True:
_, f = c.read()
gray = cv2.cvtColor(f, cv2.COLOR_BGR2GRAY)
image_nobackground = BackgroundRemoval.removebackground(gray, background)
b, g, r = cv2.split(f)
nb = np.minimum(image_nobackground, b)
ng = np.minimum(image_nobackground, g)
nr = np.minimum(image_nobackground, r)
bn, gn, rn = normalized(ng, nr, ng)
backgroundRemovedImage = cv2.merge((nb, ng, nr))
res = ColorSegmenter.getMagentaBlob(backgroundRemovedImage)
objectdetection = cv2.cvtColor(res, cv2.COLOR_BGR2GRAY)
edged = cv2.Canny(objectdetection, 100, 250)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (7, 7))
closed = cv2.morphologyEx(edged, cv2.MORPH_CLOSE, kernel)
contours, hierarchy = cv2.findContours(closed, 2, 1)
areaContours = []
res2 = copy.deepcopy(res)
for cnt in contours:
# Only if there are 2 contours or something
area = cv2.contourArea(cnt)
hull = cv2.convexHull(cnt, returnPoints=False)
if len(hull) > 3 and len(cnt) > 3:
defects = cv2.convexityDefects(cnt, hull)
if defects != None:
if useSIFT:
minMatches = 50
descriptor = DinoDescriptor(useSIFT = useSIFT)
dinoMatcher = DinoMatcher(descriptor, glob.glob(args["samples"] + "/*.png"), ratio = ratio, minMatches = minMatches, useHamming = useHamming)
dinoResultsHandler = DinoResultsHandler(db)
# capture from web cam
cap = cv2.VideoCapture(0)
while True:
ret, queryImage = cap.read()
if ret == True:
queryImage = utils2.resize(queryImage, width = 1000)
# Segment the pink area out
segImage = ColorSegmenter.getMagentaBlob(queryImage)
# Describe the query image
(queryKps, queryDescs, queryKpdRaw) = descriptor.describeQuery(segImage)
# It is really important to handle the camera idling time.
if len(queryKps) == 0:
print("Place The Object In The Camera!")
cv2.imshow("Query", queryImage)
# else let's start matching our samples to the query
else:
# To show the key points on query image
kpImage = cv2.drawKeypoints(queryImage, queryKpdRaw, None)
# let's also add the cool green box
greenBoxImg = dinoResultsHandler.drawGreenBox(queryImage, segImage, kpImage)
# showing the box must have a timer sleep, otherwise it will be flushed
cv2.imshow("Query", greenBoxImg)
time.sleep(0.025)
