def getRawCroppedImageBlob(roidb, records, scale_inds, getCropped):
"""Builds an input blob from the images in the roidb at the specified
scales.
"""
num_images = len(roidb)
processed_ims = []
im_scales = []
for idx, roi in enumerate(roidb):
im = cv2.imread(roi['image'])
if roi['flipped']:
im = im[:, ::-1, :]
target_size = cfg.TRAIN.SCALES[0]
cimg = im
if getCropped:
cimg = cropImageToAnnoRegion(
im, roi['boxes']
[0]) # always the first box since we are *flattened*
target_size = cfg.TRAIN.SCALES[0]
cimg, cimg_scale = prep_im_for_blob(cimg, cfg.PIXEL_MEANS, target_size,
cfg.TRAIN.MAX_SIZE)
cimg_scale = 1.0 # why always "1.0"??
processed_ims.append(cimg)
im_scales.append(cimg_scale)
# Create a blob to hold the input images
blob = im_list_to_blob(processed_ims)
return blob, im_scales
def HOGfromRoidbSample(sample,
orient=9,
pix_per_cell=8,
cell_per_block=2,
hog_channel=0):
features = []
img = cv2.imread(sample['image'])
for box in sample['boxes']:
clean_box(box, sample)
cimg = cropImageToAnnoRegion(img, box)
feature_image = np.copy(cimg)
try:
features.append(HOGFromImage(feature_image))
except:
print(sample)
return None
return features
def loadImage(imdb, image_path, image_index, bbox, al_net, idx):
isImBlob = False
if imdb.is_image_index_flattened and al_net is None:
raw_img = cv2.imread(image_path)
img = cropImageToAnnoRegion(raw_img, bbox)
elif imdb.is_image_index_flattened and al_net is not None:
sampleRoidb = [imdb.roidb[idx]]
sampleRoidb[0]['image'] = image_path
sampleRecords = []
numClasses = len(cfg.DATASETS.CLASSES)
input_data = alcls_get_minibatch(sampleRoidb, sampleRecords, al_net,
numClasses)
img = {}
img['data'] = input_data['data']
isImBlob = True
else:
img = cv2.imread(image_path)
return img, isImBlob
def bboxHOGfromRoidbSample(sample,orient=9, pix_per_cell=8,
cell_per_block=2, hog_channel=0):
features = []
if 'image' not in sample.keys():
# print(sample)
print(sample.keys())
print("WARINING [bboxHOGfromRoidbSample]: the\
image field is not available for the above sample")
return None
img = cv2.imread(sample['image'])
for box in sample['boxes']:
box = clean_box(box,sample['width'],sample['height'])
cimg = cropImageToAnnoRegion(img,box)
feature_image = np.copy(cimg)
try:
features.append(HOGFromImage(feature_image))
except Exception as e:
print(e)
print('hog failed @ path {}'.format(sample['image']))
return None
return features
d[sline[0]] = [{
"path": sline[1],
"box": map(int, sline[2].split("_")),
"score": float(sline[3])
}]
return d
if __name__ == '__main__':
args = parse_args()
print('Called with args:')
print(args)
setID = args.setID
repeat = args.repeat
size = args.size
fn = constructFilenameToLoad(setID, repeat, size)
ntdResults = loadNtdFile(fn)
pprint.pprint(ntdResults)
for dataset, iconicSamples in ntdResults.items():
for iconicSample in iconicSamples:
img = cv2.imread(iconicSample['path'])
box = iconicSample['box']
score = iconicSample['score']
cimg = cropImageToAnnoRegion(img, box)
saveFn = constructFilenameToSaveImage(setID, repeat, size, dataset,
score)
cv2.imwrite(saveFn, cimg)