def calculate_medium_box(boxes):
conf_sum = reduce(lambda t, b: t + b.score, boxes, 0)
aggregation = {}
for name in ['x1', 'y1', 'x2', 'y2']:
aggregation[name] = reduce(lambda t, b: t + b.__dict__[name] * b.score,
boxes, 0) / conf_sum
new_box = al.AnnoRect(**aggregation)
new_box.classID = boxes[0].classID
new_box.score = conf_sum / len(boxes)
return new_box
def annotation_labelbox_from_result(result, columns, url_prefix):
annotation = AnnotationLib.Annotation()
image_url = get_path_from_s3(result[columns['annotation']], url_prefix)
boxes = parse_boxes(result[columns['Answer.results']])
for box in boxes:
annotation.rects.append(AnnotationLib.AnnoRect(*box))
# remove parameters from filename
param_idx = image_url.index('&');
annotation.imageName = image_url[:param_idx];
return annotation
def do(image, anno=None):
"""
Does the rotation for image and rectangles for 90 degrees counterclockwise.
Args:
image (Image): The target image to rotate.
anno (Annotation): The annotations to be rotated with the image.
Returns (tuple):
Rotated image and annotations for it.
"""
w = image.shape[1]
new_image = imrotate(image, 90, reshape=True)
if anno is not None:
anno.rects = [al.AnnoRect(r.y1, w - r.x2, r.y2, w - r.x1) for r in anno.rects]
return new_image, anno
def get_cell_grid(cell_width, cell_height, region_size):
cell_regions = []
for iy in xrange(cell_height):
for ix in xrange(cell_width):
cidx = iy * cell_width + ix
ox = (ix + 0.5) * region_size
oy = (iy + 0.5) * region_size
r = al.AnnoRect(ox - 0.5 * region_size, oy - 0.5 * region_size,
ox + 0.5 * region_size, oy + 0.5 * region_size)
r.track_id = cidx
cell_regions.append(r)
return cell_regions
def annotation_labelact_from_result(result, columns, url_prefix):
annotation = AnnotationLib.Annotation()
image_url = get_path_from_s3(result[columns['annotation']], url_prefix)
# remove parameters from filename
param_idx = image_url.index('&');
image_url_split = image_url[:param_idx].split(',');
assert(len(image_url_split) == 5);
annotation.imageName = image_url_split[0];
rect = AnnotationLib.AnnoRect();
rect.x1 = int(image_url_split[1])
rect.y1 = int(image_url_split[2])
rect.x2 = int(image_url_split[3])
rect.y2 = int(image_url_split[4])
result_split = result[columns['Answer.results']].split(',');
assert(len(result_split) == 1 + 1 + 4 + 4);
# male/female
gender_idx_to_val = {0: ATTR_VAL_GENDER_MALE, 1: ATTR_VAL_GENDER_FEMALE};
ptype_idx_to_val = {0: ATTR_VAL_PTYPE_SALES, 1: ATTR_VAL_PTYPE_CUST};
sales_act_idx_to_val = {0: ATTR_VAL_ACT_SALES_INT, 1: ATTR_VAL_ACT_SALES_CLEAN, 2: ATTR_VAL_ACT_SALES_OTHER};
cust_act_idx_to_val = {0: ATTR_VAL_ACT_CUST_QUEUE, 1: ATTR_VAL_ACT_CUST_INT, 2: ATTR_VAL_ACT_CUST_BROWSE, 3: ATTR_VAL_ACT_CUST_OTHER};
gender_idx = int(result_split[-4]);
rect.at["gender"] = gender_idx_to_val[gender_idx];
# sales/cust
ptype_idx = int(result_split[-3]);
rect.at["ptype"] = ptype_idx_to_val[ptype_idx];
if ptype_idx == 0:
# interact/clean/other
act_idx = int(result_split[-2]);
rect.at["act"] = sales_act_idx_to_val[act_idx];
else:
# queue/interact/browse/other
act_idx = int(result_split[-1]);
rect.at["act"] = cust_act_idx_to_val[act_idx];
annotation.rects.append(rect);
return annotation
def pal2al(_annolist):
annotations = []
for _a in _annolist.annotation:
anno = AnnotationLib.Annotation()
anno.imageName = _a.imageName
anno.rects = []
for _r in _a.rect:
rect = AnnotationLib.AnnoRect()
rect.x1 = _r.x1
rect.x2 = _r.x2
rect.y1 = _r.y1
rect.y2 = _r.y2
if _r.HasField("id"):
rect.id = _r.id
if _r.HasField("track_id"):
rect.track_id = _r.track_id
if _r.HasField("score"):
rect.score = _r.score
if _r.HasField("distance3d"):
rect.distance3d = _r.distance3d
if _r.HasField("width3d"):
rect.width3d = _r.width3d
if _r.HasField("height3d"):
rect.height3d = _r.height3d
if _r.HasField("length3d"):
rect.length3d = _r.length3d
anno.rects.append(rect)
annotations.append(anno)
return annotations
def pal2al(_annolist):
#annotations = [];
annotations = AnnotationLib.AnnoList()
for adesc in _annolist.attribute_desc:
annotations.attribute_desc[adesc.name] = adesc
print "attribute: ", adesc.name, adesc.id
for valdesc in adesc.val_to_str:
annotations.add_attribute_val(adesc.name, valdesc.s, valdesc.id)
attribute_name_from_id = {
adesc.id: aname
for aname, adesc in annotations.attribute_desc.iteritems()
}
attribute_dtype_from_id = {
adesc.id: adesc.dtype
for aname, adesc in annotations.attribute_desc.iteritems()
}
for _a in _annolist.annotation:
anno = AnnotationLib.Annotation()
anno.imageName = _a.imageName
anno.rects = []
for _r in _a.rect:
rect = AnnotationLib.AnnoRect()
rect.x1 = _r.x1
rect.x2 = _r.x2
rect.y1 = _r.y1
rect.y2 = _r.y2
if _r.HasField("id"):
rect.id = _r.id
if _r.HasField("track_id"):
rect.track_id = _r.track_id
if _r.HasField("score"):
rect.score = _r.score
for _at in _r.attribute:
try:
cur_aname = attribute_name_from_id[_at.id]
cur_dtype = attribute_dtype_from_id[_at.id]
except KeyError as e:
print "attribute: ", _at.id
print e
assert (False)
if cur_dtype == AnnotationLib.AnnoList.TYPE_INT32:
rect.at[cur_aname] = _at.val
elif cur_dtype == AnnotationLib.AnnoList.TYPE_FLOAT:
rect.at[cur_aname] = _at.fval
elif cur_dtype == AnnotationLib.AnnoList.TYPE_STRING:
rect.at[cur_aname] = _at.strval
else:
assert (False)
anno.rects.append(rect)
annotations.append(anno)
return annotations
def add_rectangles(H,
orig_image,
confidences,
boxes,
use_stitching=False,
rnn_len=1,
min_conf=0.1,
show_removed=True,
tau=0.25,
show_suppressed=True):
image = np.copy(orig_image[0])
boxes_r = np.reshape(boxes,
(-1, H["grid_height"], H["grid_width"], rnn_len, 4))
confidences_r = np.reshape(
confidences,
(-1, H["grid_height"], H["grid_width"], rnn_len, H['num_classes']))
cell_pix_size = H['region_size']
all_rects = [[[] for _ in range(H["grid_width"])]
for _ in range(H["grid_height"])]
for n in range(rnn_len):
for y in range(H["grid_height"]):
for x in range(H["grid_width"]):
classID = np.argmax(confidences_r[0, y, x, n, 1:]) + 1
bbox = boxes_r[0, y, x, n, :]
abs_cx = int(bbox[0]) + cell_pix_size / 2 + cell_pix_size * x
abs_cy = int(bbox[1]) + cell_pix_size / 2 + cell_pix_size * y
w = bbox[2]
h = bbox[3]
conf = confidences_r[0, y, x, n, classID]
all_rects[y][x].append(
Rect(abs_cx, abs_cy, w, h, conf, classID))
all_rects_r = [r for row in all_rects for cell in row for r in cell]
if use_stitching:
from stitch_wrapper import stitch_rects
acc_rects = stitch_rects(all_rects, tau)
else:
acc_rects = all_rects_r
if show_suppressed:
pairs = [(all_rects_r, (255, 0, 0))]
else:
pairs = []
pairs.append((acc_rects, (0, 255, 0)))
for rect_set, color in pairs:
for rect in rect_set:
if rect.confidence > min_conf:
cv2.rectangle(image, (rect.cx - int(rect.width / 2),
rect.cy - int(rect.height / 2)),
(rect.cx + int(rect.width / 2),
rect.cy + int(rect.height / 2)), color, 2)
rects = []
for rect in acc_rects:
r = al.AnnoRect()
r.x1 = rect.cx - rect.width / 2.
r.x2 = rect.cx + rect.width / 2.
r.y1 = rect.cy - rect.height / 2.
r.y2 = rect.cy + rect.height / 2.
r.score = rect.true_confidence
# r.classID = rect.classID
rects.append(r)
return image, rects
annolist.append(anno)
# load vatic tracks
vatic_dump = pickle.load(open(opts.vatic_filename, "rb"))
num_tracks = len(vatic_dump)
print "number of tracks: ", num_tracks
for tidx in xrange(0, num_tracks):
vatic_boxes = vatic_dump[tidx]["boxes"]
track_len = len(vatic_boxes)
print "track ", tidx, ", track_len: ", track_len
for bidx in xrange(0, track_len):
if vatic_boxes[bidx].lost == 0:
rect = AnnotationLib.AnnoRect()
rect.id = tidx
rect.x1 = vatic_boxes[bidx].xtl
rect.y1 = vatic_boxes[bidx].ytl
rect.x2 = vatic_boxes[bidx].xbr
rect.y2 = vatic_boxes[bidx].ybr
cur_frame = vatic_boxes[bidx].frame
annolist[cur_frame].rects.append(rect)
# save annolist
fname_ext = os.path.basename(opts.vatic_filename)
fname = os.path.splitext(fname_ext)[0]
pal_filename = opts.output_dir + "/" + fname + ".pal"
print "saving ", pal_filename
def inv(r):
rotated_back = al.AnnoRect(width - r.y2, r.x1, width - r.y1, r.x2)
rotated_back.score = r.score
return rotated_back
