def start(self, Image, ROIs):
"""Image processing function.
\param image (the enhanced and segmented image)
\return image (the annotated image )
local variable is the list of detected blobs with the following feature columns:
[bb_left,bb_top,bb_width,bb_height, cc_area, sharpness, SNR]
Sharpness is variation of the Laplacian (introduced by Pech-Pacheco
"Diatom autofocusing in brightfield microscopy: a comparative study."
"""
try:
self.startTimer()
self.image = Image
self.ROIs = ROIs
self._Blobs = list()
# Iterate ROis
for ROI in ROIs:
# slice image, assuming ROI:(left,top,width,height)
ROI_image = self.image[ROI[1]:ROI[1] + ROI[3],
ROI[0]:ROI[0] + ROI[2]]
# Binarize and find blobs
BWImage = cv2.adaptiveThreshold(ROI_image, 255,
cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
self.invBin, self.blocksize,
self.offset)
# ConnectedComponentsWithStats output: number of labels, label matrix, stats(left,top,width,height), area
_, _, blobFeatures, _ = cv2.connectedComponentsWithStats(
BWImage, 8, cv2.CV_32S)
# Get blob RoI and area
blobFeatures = blobFeatures[
1:] # skipping background (label 0)
# Filter by blob area
blobFeatures = blobFeatures[np.where(
(blobFeatures[:, cv2.CC_STAT_AREA] > self.minBlobArea)
& (blobFeatures[:, cv2.CC_STAT_AREA] < self.maxBlobArea))]
# Increase array size
blobFeatures = np.concatenate([
blobFeatures,
np.zeros((blobFeatures.shape[0], 3), dtype=int)
],
axis=1)
# Annotate blobs and compute additional features
for blob in blobFeatures:
tl = (blob[0], blob[1])
br = (blob[0] + blob[2], blob[1] + blob[3])
# Compute some metrics of individual blobs
tempImage = self.image[tl[1]:br[1], tl[0]:br[0]]
I_0 = 255.0 - np.min(
tempImage) # peak foreground intensity estimate
I_b = 255.0 - np.max(tempImage) # background intensity
# Shift coordinates wrt ROI
blob[0] += ROI[0]
blob[1] += ROI[1]
#centroid
cX = int((blob[0] + blob[0] + blob[2]) / 2.0) # x1+x2 /2
cY = int((blob[1] - blob[3] + blob[1]) / 2.0) # y1+y2 /2
DetectedBlob = Blob(blob[0], blob[0] + blob[2],
blob[1] - blob[3], blob[1], (cX, cY))
# Local sharpness column
DetectedBlob._local_sharpness = int(
cv2.Laplacian(tempImage, cv2.CV_64F).var())
# Local SNR column
DetectedBlob._local_SNR = int(
(I_0 - I_b) / np.sqrt(I_b)) if I_b > 0 else 0
# Perimeter
tempBWImage = BWImage[tl[1]:br[1], tl[0]:br[0]]
contours, _ = cv2.findContours(tempBWImage, cv2.RETR_LIST,
cv2.CHAIN_APPROX_NONE)
contour = max(
contours,
key=cv2.contourArea) # select largest contour
DetectedBlob._perimeter = len(contour)
#Add blob to list
self._Blobs.append(DetectedBlob)
# Mark in image
# if self.plot:
# cv2.rectangle(ROI_image, tl, br, (0, 0, 0), 1)
#cv2.putText(ROI_image, str(blob[5]), br, cv2.FONT_HERSHEY_SIMPLEX, .5, (0,0,0), 1, cv2.LINE_AA)
# Plot last ROI
if self.plot:
cv2.imshow(self.name, BWImage)
# Finalize
self.stopTimer()
self.signals.finished.emit()
except Exception as err:
exc = traceback.format_exception(type(err),
err,
err.__traceback__,
chain=False)
self.signals.error.emit(exc)
self.signals.message.emit('E: {} exception: {}'.format(
self.name, err))
return self.image
