def get_rois_for_image(self, img):
self.ensure_model_is_loaded()
# get rois
if self.__use_selective_search_rois:
rects, scaled_img, scale = getSelectiveSearchRois(
img, ss_scale, ss_sigma, ss_minSize,
roi_maxImgDim) # interpolation=cv2.INTER_AREA
else:
rects = []
scaled_img, scale = imresizeMaxDim(img,
roi_maxImgDim,
boUpscale=True,
interpolation=cv2.INTER_AREA)
imgWidth, imgHeight = imArrayWidthHeight(scaled_img)
if not self.__use_selective_search_rois:
if (imgWidth, imgHeight) in self.__rois_only_grid_cache:
return self.__rois_only_grid_cache[(imgWidth, imgHeight)]
# add grid rois
if self.__use_grid_rois:
if (imgWidth, imgHeight) in self.__grid_rois_cache:
rectsGrid = self.__grid_rois_cache[(imgWidth, imgHeight)]
else:
rectsGrid = getGridRois(imgWidth, imgHeight, grid_nrScales,
grid_aspectRatios)
self.__grid_rois_cache[(imgWidth, imgHeight)] = rectsGrid
rects += rectsGrid
# run filter
rois = filterRois(rects, imgWidth, imgHeight, roi_minNrPixels,
roi_maxNrPixels, roi_minDim, roi_maxDim,
roi_maxAspectRatio)
if len(rois) == 0: # make sure at least one roi returned per image
rois = [[5, 5, imgWidth - 5, imgHeight - 5]]
# scale up to original size and save to disk
# note: each rectangle is in original image format with [x,y,x2,y2]
original_rois = np.int32(np.array(rois) / scale)
img_width = len(img[0])
img_height = len(img)
# all rois need to be scaled + padded to cntk input image size
targetw, targeth, w_offset, h_offset, scale = roiTransformPadScaleParams(
img_width, img_height, self.__resize_width, self.__resize_height)
rois = []
for original_roi in original_rois:
x, y, x2, y2 = roiTransformPadScale(original_roi, w_offset,
h_offset, scale)
xrel = float(x) / (1.0 * targetw)
yrel = float(y) / (1.0 * targeth)
wrel = float(x2 - x) / (1.0 * targetw)
hrel = float(y2 - y) / (1.0 * targeth)
rois.append([xrel, yrel, wrel, hrel])
# pad rois if needed:
if len(rois) < self.__nr_rois:
rois += [[0, 0, 0, 0]] * (self.__nr_rois - len(rois))
elif len(rois) > self.__nr_rois:
rois = rois[:self.__nr_rois]
if not self.__use_selective_search_rois:
self.__rois_only_grid_cache[(imgWidth,
imgHeight)] = (np.array(rois),
original_rois)
return np.array(rois), original_rois
def generate_input_rois(testing=False):
p = PARAMETERS.get_parameters_for_dataset()
if not p.datasetName.startswith("pascalVoc"):
# init
makeDirectory(p.roiDir)
roi_minDim = p.roi_minDimRel * p.roi_maxImgDim
roi_maxDim = p.roi_maxDimRel * p.roi_maxImgDim
roi_minNrPixels = p.roi_minNrPixelsRel * p.roi_maxImgDim*p.roi_maxImgDim
roi_maxNrPixels = p.roi_maxNrPixelsRel * p.roi_maxImgDim*p.roi_maxImgDim
for subdir in subDirs:
makeDirectory(os.path.join(p.roiDir, subdir))
imgFilenames = getFilesInDirectory(os.path.join(p.imgDir, subdir), ".jpg")
# loop over all images
for imgIndex,imgFilename in enumerate(imgFilenames):
roiPath = "{}/{}/{}.roi.txt".format(p.roiDir, subdir, imgFilename[:-4])
# load image
print (imgIndex, len(imgFilenames), subdir, imgFilename)
tstart = datetime.datetime.now()
imgPath = os.path.join(p.imgDir, subdir, imgFilename)
imgOrig = imread(imgPath)
if imWidth(imgPath) > imHeight(imgPath):
print (imWidth(imgPath) , imHeight(imgPath))
# get rois
if boAddSelectiveSearchROIs:
print ("Calling selective search..")
rects, img, scale = getSelectiveSearchRois(imgOrig, p.ss_scale, p.ss_sigma, p.ss_minSize, p.roi_maxImgDim) #interpolation=cv2.INTER_AREA
print (" Number of rois detected using selective search: " + str(len(rects)))
else:
rects = []
img, scale = imresizeMaxDim(imgOrig, p.roi_maxImgDim, boUpscale=True, interpolation=cv2.INTER_AREA)
imgWidth, imgHeight = imArrayWidthHeight(img)
# add grid rois
if boAddRoisOnGrid:
rectsGrid = getGridRois(imgWidth, imgHeight, p.grid_nrScales, p.grid_aspectRatios)
print (" Number of rois on grid added: " + str(len(rectsGrid)))
rects += rectsGrid
# run filter
print (" Number of rectangles before filtering = " + str(len(rects)))
rois = filterRois(rects, imgWidth, imgHeight, roi_minNrPixels, roi_maxNrPixels, roi_minDim, roi_maxDim, p.roi_maxAspectRatio)
if len(rois) == 0: #make sure at least one roi returned per image
rois = [[5, 5, imgWidth-5, imgHeight-5]]
print (" Number of rectangles after filtering = " + str(len(rois)))
# scale up to original size and save to disk
# note: each rectangle is in original image format with [x,y,x2,y2]
rois = np.int32(np.array(rois) / scale)
assert (np.min(rois) >= 0)
assert (np.max(rois[:, [0,2]]) < imArrayWidth(imgOrig))
assert (np.max(rois[:, [1,3]]) < imArrayHeight(imgOrig))
np.savetxt(roiPath, rois, fmt='%d')
print (" Time [ms]: " + str((datetime.datetime.now() - tstart).total_seconds() * 1000))
# clear imdb cache and other files
if os.path.exists(p.cntkFilesDir):
assert(p.cntkFilesDir.endswith("cntkFiles"))
if not testing:
userInput = input('--> INPUT: Press "y" to delete directory ' + p.cntkFilesDir + ": ")
if userInput.lower() not in ['y', 'yes']:
print ("User input is %s: exiting now." % userInput)
exit(-1)
shutil.rmtree(p.cntkFilesDir)
time.sleep(0.1) # avoid access problems
# create cntk representation for each image
for image_set in image_sets:
imdb = p.imdbs[image_set]
print ("Number of images in set {} = {}".format(image_set, imdb.num_images))
makeDirectory(p.cntkFilesDir)
# open files for writing
cntkImgsPath, cntkRoiCoordsPath, cntkRoiLabelsPath, nrRoisPath = getCntkInputPaths(p.cntkFilesDir, image_set)
with open(nrRoisPath, 'w') as nrRoisFile, \
open(cntkImgsPath, 'w') as cntkImgsFile, \
open(cntkRoiCoordsPath, 'w') as cntkRoiCoordsFile, \
open(cntkRoiLabelsPath, 'w') as cntkRoiLabelsFile:
# for each image, transform rois etc to cntk format
for imgIndex in range(0, imdb.num_images):
if imgIndex % 50 == 0:
print ("Processing image set '{}', image {} of {}".format(image_set, imgIndex, imdb.num_images))
currBoxes = imdb.roidb[imgIndex]['boxes']
currGtOverlaps = imdb.roidb[imgIndex]['gt_overlaps']
imgPath = imdb.image_path_at(imgIndex)
imgWidth, imgHeight = imWidthHeight(imgPath)
# all rois need to be scaled + padded to cntk input image size
targetw, targeth, w_offset, h_offset, scale = roiTransformPadScaleParams(imgWidth, imgHeight,
p.cntk_padWidth, p.cntk_padHeight)
boxesStr = ""
labelsStr = ""
nrBoxes = len(currBoxes)
for boxIndex, box in enumerate(currBoxes):
rect = roiTransformPadScale(box, w_offset, h_offset, scale)
boxesStr += getCntkRoiCoordsLine(rect, p.cntk_padWidth, p.cntk_padHeight)
labelsStr += getCntkRoiLabelsLine(currGtOverlaps[boxIndex, :].toarray()[0],
p.train_posOverlapThres,
p.nrClasses)
# if less than e.g. 2000 rois per image, then fill in the rest using 'zero-padding'.
boxesStr, labelsStr = cntkPadInputs(nrBoxes, p.cntk_nrRois, p.nrClasses, boxesStr, labelsStr)
# update cntk data
nrRoisFile.write("{}\n".format(nrBoxes))
cntkImgsFile.write("{}\t{}\t0\n".format(imgIndex, imgPath))
cntkRoiCoordsFile.write("{} |rois{}\n".format(imgIndex, boxesStr))
cntkRoiLabelsFile.write("{} |roiLabels{}\n".format(imgIndex, labelsStr))
print ("DONE.")
return True
def generate_input_rois(testing=False):
p = PARAMETERS.get_parameters_for_dataset()
if not p.datasetName.startswith("pascalVoc"):
# init
makeDirectory(p.roiDir)
roi_minDim = p.roi_minDimRel * p.roi_maxImgDim
roi_maxDim = p.roi_maxDimRel * p.roi_maxImgDim
roi_minNrPixels = p.roi_minNrPixelsRel * p.roi_maxImgDim*p.roi_maxImgDim
roi_maxNrPixels = p.roi_maxNrPixelsRel * p.roi_maxImgDim*p.roi_maxImgDim
for subdir in subDirs:
makeDirectory(os.path.join(p.roiDir, subdir))
imgFilenames = getFilesInDirectory(os.path.join(p.imgDir, subdir), ".jpg")
# loop over all images
for imgIndex,imgFilename in enumerate(imgFilenames):
roiPath = "{}/{}/{}.roi.txt".format(p.roiDir, subdir, imgFilename[:-4])
# load image
print (imgIndex, len(imgFilenames), subdir, imgFilename)
tstart = datetime.datetime.now()
imgPath = os.path.join(p.imgDir, subdir, imgFilename)
imgOrig = imread(imgPath)
if imWidth(imgPath) > imHeight(imgPath):
print (imWidth(imgPath) , imHeight(imgPath))
# get rois
if boAddSelectiveSearchROIs:
print ("Calling selective search..")
rects, img, scale = getSelectiveSearchRois(imgOrig, p.ss_scale, p.ss_sigma, p.ss_minSize, p.roi_maxImgDim) #interpolation=cv2.INTER_AREA
print (" Number of rois detected using selective search: " + str(len(rects)))
else:
rects = []
img, scale = imresizeMaxDim(imgOrig, p.roi_maxImgDim, boUpscale=True, interpolation=cv2.INTER_AREA)
imgWidth, imgHeight = imArrayWidthHeight(img)
# add grid rois
if boAddRoisOnGrid:
rectsGrid = getGridRois(imgWidth, imgHeight, p.grid_nrScales, p.grid_aspectRatios)
print (" Number of rois on grid added: " + str(len(rectsGrid)))
rects += rectsGrid
# run filter
print (" Number of rectangles before filtering = " + str(len(rects)))
rois = filterRois(rects, imgWidth, imgHeight, roi_minNrPixels, roi_maxNrPixels, roi_minDim, roi_maxDim, p.roi_maxAspectRatio)
if len(rois) == 0: #make sure at least one roi returned per image
rois = [[5, 5, imgWidth-5, imgHeight-5]]
print (" Number of rectangles after filtering = " + str(len(rois)))
# scale up to original size and save to disk
# note: each rectangle is in original image format with [x,y,x2,y2]
rois = np.int32(np.array(rois) / scale)
assert (np.min(rois) >= 0)
assert (np.max(rois[:, [0,2]]) < imArrayWidth(imgOrig))
assert (np.max(rois[:, [1,3]]) < imArrayHeight(imgOrig))
np.savetxt(roiPath, rois, fmt='%d')
print (" Time [ms]: " + str((datetime.datetime.now() - tstart).total_seconds() * 1000))
# clear imdb cache and other files
if os.path.exists(p.cntkFilesDir):
assert(p.cntkFilesDir.endswith("cntkFiles"))
if not testing:
userInput = input('--> INPUT: Press "y" to delete directory ' + p.cntkFilesDir + ": ")
if userInput.lower() not in ['y', 'yes']:
print ("User input is %s: exiting now." % userInput)
exit(-1)
shutil.rmtree(p.cntkFilesDir)
time.sleep(0.1) # avoid access problems
# create cntk representation for each image
for image_set in image_sets:
imdb = p.imdbs[image_set]
print ("Number of images in set {} = {}".format(image_set, imdb.num_images))
makeDirectory(p.cntkFilesDir)
# open files for writing
cntkImgsPath, cntkRoiCoordsPath, cntkRoiLabelsPath, nrRoisPath = getCntkInputPaths(p.cntkFilesDir, image_set)
with open(nrRoisPath, 'w') as nrRoisFile, \
open(cntkImgsPath, 'w') as cntkImgsFile, \
open(cntkRoiCoordsPath, 'w') as cntkRoiCoordsFile, \
open(cntkRoiLabelsPath, 'w') as cntkRoiLabelsFile:
# for each image, transform rois etc to cntk format
for imgIndex in range(0, imdb.num_images):
if imgIndex % 50 == 0:
print ("Processing image set '{}', image {} of {}".format(image_set, imgIndex, imdb.num_images))
currBoxes = imdb.roidb[imgIndex]['boxes']
currGtOverlaps = imdb.roidb[imgIndex]['gt_overlaps']
imgPath = imdb.image_path_at(imgIndex)
imgWidth, imgHeight = imWidthHeight(imgPath)
# all rois need to be scaled + padded to cntk input image size
targetw, targeth, w_offset, h_offset, scale = roiTransformPadScaleParams(imgWidth, imgHeight,
p.cntk_padWidth, p.cntk_padHeight)
boxesStr = ""
labelsStr = ""
nrBoxes = len(currBoxes)
for boxIndex, box in enumerate(currBoxes):
rect = roiTransformPadScale(box, w_offset, h_offset, scale)
boxesStr += getCntkRoiCoordsLine(rect, p.cntk_padWidth, p.cntk_padHeight)
labelsStr += getCntkRoiLabelsLine(currGtOverlaps[boxIndex, :].toarray()[0],
p.train_posOverlapThres,
p.nrClasses)
# if less than e.g. 2000 rois per image, then fill in the rest using 'zero-padding'.
boxesStr, labelsStr = cntkPadInputs(nrBoxes, p.cntk_nrRois, p.nrClasses, boxesStr, labelsStr)
# update cntk data
nrRoisFile.write("{}\n".format(nrBoxes))
cntkImgsFile.write("{}\t{}\t0\n".format(imgIndex, imgPath))
cntkRoiCoordsFile.write("{} |rois{}\n".format(imgIndex, boxesStr))
cntkRoiLabelsFile.write("{} |roiLabels{}\n".format(imgIndex, labelsStr))
print ("DONE.")
return True