def _kwimage_to_kwiver_detections(detections):
"""
Convert kwimage detections to kwiver deteted object sets
Args:
detected_objects (kwimage.Detections)
Returns:
kwiver.vital.types.DetectedObjectSet
"""
# convert segmentation masks
if 'segmentations' in detections.data:
print("Warning: segmentations not implemented")
boxes = detections.boxes.to_tlbr()
scores = detections.scores
class_idxs = detections.class_idxs
# convert to kwiver format, apply threshold
detected_objects = DetectedObjectSet()
for tlbr, score, cidx in zip(boxes.data, scores, class_idxs):
class_name = detections.classes[cidx]
bbox_int = np.round(tlbr).astype(np.int32)
bounding_box = BoundingBox(bbox_int[0], bbox_int[1], bbox_int[2],
bbox_int[3])
detected_object_type = DetectedObjectType(class_name, score)
detected_object = DetectedObject(bounding_box, score,
detected_object_type)
detected_objects.add(detected_object)
return detected_objects
def _classification_to_kwiver_detections(classification, w, h):
"""
Convert kwarray classifications to kwiver deteted object sets
Args:
classification (bioharn.clf_predict.Classification)
w (int): width of image
h (int): height of image
Returns:
kwiver.vital.types.DetectedObjectSet
"""
detected_objects = DetectedObjectSet()
if classification.data.get('prob', None) is not None:
# If we have a probability for each class, uses that
class_names = list(classification.classes)
class_prob = classification.prob
detected_object_type = DetectedObjectType(class_names, class_prob)
else:
# Otherwise we only have the score for the predicted calss
class_name = classification.classes[classification.cidx]
class_score = classification.conf
detected_object_type = DetectedObjectType(class_name, class_score)
bounding_box = BoundingBoxD(0, 0, w, h)
detected_object = DetectedObject(bounding_box, classification.conf,
detected_object_type)
detected_objects.add(detected_object)
return detected_objects
def filter_truth(self, init_truth, categories):
filtered_truth = DetectedObjectSet()
use_frame = True
max_length = int(self._max_scale_wrt_chip * float(self._chip_width))
for i, item in enumerate(init_truth):
if item.type is None:
continue
class_lbl = item.type.get_most_likely_class()
if categories is not None and not categories.has_class_name(
class_lbl):
if self._mode == "detection_refiner":
class_lbl = self._negative_category
else:
continue
if categories is not None:
class_lbl = categories.get_class_name(class_lbl)
elif class_lbl not in self._categories:
self._categories.append(class_lbl)
item.type = DetectedObjectType(class_lbl, 1.0)
if self._mode == "detector" and \
( item.bounding_box.width() > max_length or \
item.bounding_box.height() > max_length ):
use_frame = False
break
filtered_truth.add(item)
if self._gt_frames_only and len(init_truth) == 0:
use_frame = False
return filtered_truth, use_frame
def _step(self):
try:
# Grab image container from port using traits
in_img_c = self.grab_input_using_trait('image')
timestamp = self.grab_input_using_trait('timestamp')
dos_ptr = self.grab_input_using_trait('detected_object_set')
print('timestamp = {!r}'.format(timestamp))
# Get current frame and give it to app feature extractor
im = get_pil_image(in_img_c.image())
self._app_feature_extractor.frame = im
bbox_num = 0
# Get detection bbox
dos = dos_ptr.select(self._select_threshold)
bbox_num = dos.size()
det_obj_set = DetectedObjectSet()
if bbox_num == 0:
print(
'!!! No bbox is provided on this frame and skip this frame !!!'
)
else:
# appearance features (format: pytorch tensor)
app_f_begin = timer()
pt_app_features = self._app_feature_extractor(dos, False)
app_f_end = timer()
print('%%%app feature eclapsed time: {}'.format(app_f_end -
app_f_begin))
# get new track state from new frame and detections
for idx, item in enumerate(dos):
bbox = item.bounding_box()
fid = timestamp.get_frame()
ts = timestamp.get_time_usec()
d_obj = item
# store app feature to detectedObject
app_f = new_descriptor(pt_app_features[idx].numpy().size)
app_f[:] = pt_app_features[idx].numpy()
# print( pt_app_features[idx].numpy() )
d_obj.set_descriptor(app_f)
det_obj_set.add(d_obj)
# push track set to output port
self.push_to_port_using_trait('detected_object_set', det_obj_set)
self._base_step()
except BaseException as e:
print(repr(e))
import traceback
print(traceback.format_exc())
sys.stdout.flush()
raise
def _create_detected_object_set():
from kwiver.vital.types import DetectedObject, DetectedObjectSet, BoundingBox
dos = DetectedObjectSet()
bbox = BoundingBox(0, 10, 100, 50)
dos.add(DetectedObject(bbox, 0.2))
dos.add(DetectedObject(bbox, 0.5))
dos.add(DetectedObject(bbox, 0.4))
return dos
def detect(self, in_img_c):
import tensorflow as tf
import humanfriendly
image_height = in_img_c.height()
image_width = in_img_c.width()
if (self.norm_image_type and self.norm_image_type != "none"):
print("Normalizing input image")
in_img = in_img_c.image().asarray().astype("uint16")
bottom, top = self.get_scaling_values(self.norm_image_type, in_img,
image_height)
in_img = self.lin_normalize_image(in_img, bottom, top)
in_img = np.tile(in_img, (1, 1, 3))
else:
in_img = np.array(get_pil_image(in_img_c.image()).convert("RGB"))
start_time = time.time()
boxes, scores, classes = self.generate_detection(
self.detection_graph, in_img)
elapsed = time.time() - start_time
print("Done running detector in {}".format(
humanfriendly.format_timespan(elapsed)))
good_boxes = []
detections = DetectedObjectSet()
for i in range(0, len(scores)):
if (scores[i] >= self.confidence_thresh):
bbox = boxes[i]
good_boxes.append(bbox)
top_rel = bbox[0]
left_rel = bbox[1]
bottom_rel = bbox[2]
right_rel = bbox[3]
xmin = left_rel * image_width
ymin = top_rel * image_height
xmax = right_rel * image_width
ymax = bottom_rel * image_height
dot = DetectedObjectType(self.category_name, scores[i])
obj = DetectedObject(BoundingBoxD(xmin, ymin, xmax, ymax),
scores[i], dot)
detections.add(obj)
print("Detected {}".format(len(good_boxes)))
return detections
def _dowork(self, img_container):
"""
Helper to decouple the algorithm and pipeline logic
CommandLine:
xdoctest viame.processes.camtrawl.processes CamtrawlDetectFishProcess._dowork
Example:
>>> from viame.processes.camtrawl.processes import *
>>> from kwiver.vital.types import ImageContainer
>>> import kwiver.sprokit.pipeline.config
>>> # construct dummy process instance
>>> conf = kwiver.sprokit.pipeline.config.empty_config()
>>> self = CamtrawlDetectFishProcess(conf)
>>> self._configure()
>>> # construct test data
>>> from vital.util import VitalPIL
>>> from PIL import Image as PILImage
>>> pil_img = PILImage.open(ub.grabdata('https://i.imgur.com/Jno2da3.png'))
>>> pil_img = PILImage.fromarray(np.zeros((512, 512, 3), dtype=np.uint8))
>>> img_container = ImageContainer(VitalPIL.from_pil(pil_img))
>>> # Initialize the background detector by sending 10 black frames
>>> for i in range(10):
>>> empty_set = self._dowork(img_container)
>>> # now add a white box that should be detected
>>> np_img = np.zeros((512, 512, 3), dtype=np.uint8)
>>> np_img[300:340, 220:380] = 255
>>> img_container = ImageContainer.fromarray(np_img)
>>> detection_set = self._dowork(img_container)
>>> assert len(detection_set) == 1
>>> obj = detection_set[0]
"""
# This should be read as np.uint8
np_img = img_container.asarray()
detection_set = DetectedObjectSet()
ct_detections = self.detector.detect(np_img)
for detection in ct_detections:
bbox = BoundingBoxD(*detection.bbox.coords)
mask = detection.mask.astype(np.uint8)
vital_mask = ImageContainer.fromarray(mask)
dot = DetectedObjectType("Motion", 1.0)
obj = DetectedObject(bbox, 1.0, dot, mask=vital_mask)
detection_set.add(obj)
return detection_set
def _step(self):
image_container = self.grab_input_using_trait("image")
timestamp = self.grab_input_using_trait("timestamp")
file_name = self.grab_input_using_trait("file_name")
image = image_container.asarray()
h, w, _ = image.shape
bbox_x = w//2
bbox_y = h//2
bbox = BoundingBox( bbox_x - int(self.config_value("bbox_width"))//2,
bbox_y - int(self.config_value("bbox_height"))//2,
bbox_x + int(self.config_value("bbox_width"))//2,
bbox_y + int(self.config_value("bbox_height"))//2 )
dot = DetectedObjectType("Test", 1.0)
do = DetectedObject(bbox, 1.0, dot)
dos = DetectedObjectSet()
dos.add(do)
self.push_to_port_using_trait("detected_object_set", dos)
def merge(self, det_sets):
# Get detection HL info in a list
pred_sets = []
for det_set in det_sets:
pred_set = []
for det in det_set:
# Extract box info for this det
bbox = det.bounding_box
bbox_min_x = int(bbox.min_x())
bbox_max_x = int(bbox.max_x())
bbox_min_y = int(bbox.min_y())
bbox_max_y = int(bbox.max_y())
# Extract type info for this det
if det.type is None:
continue
#class_names = list( det.type.class_names() )
#class_scores = [ det.type.score( n ) for n in class_names ]
class_name = det.type.get_most_likely_class()
class_score = det.type.score(class_name)
pred_set.append([
bbox_min_x, bbox_min_y, bbox_max_x, bbox_max_y, class_name,
class_score
])
pred_sets.append(pred_set)
# Run merging algorithm
#ensemble_preds = ensemble_box( preds_set, self._fusion_weights,
# self._iou_thr, self._skip_box_thr, self._sigma, self._fusion_type )
ensemble_preds = []
# Compile output detections
output = DetectedObjectSet()
for pred in ensemble_preds:
score = pred[5]
bbox = BoundingBoxD(pred[0], pred[1], pred[2], pred[3])
dot = DetectedObjectType(pred[4], score)
det = DetectedObject(bbox, score, dot)
output.add(det)
return output
def _kwimage_to_kwiver_detections(detections):
"""
Convert kwimage detections to kwiver deteted object sets
Args:
detected_objects (kwimage.Detections)
Returns:
kwiver.vital.types.DetectedObjectSet
"""
from kwiver.vital.types.types import ImageContainer, Image
segmentations = None
# convert segmentation masks
if 'segmentations' in detections.data:
segmentations = detections.data['segmentations']
boxes = detections.boxes.to_tlbr()
scores = detections.scores
class_idxs = detections.class_idxs
if not segmentations:
# Placeholders
segmentations = (None, ) * len(boxes)
# convert to kwiver format, apply threshold
detected_objects = DetectedObjectSet()
for tlbr, score, cidx, seg in zip(boxes.data, scores, class_idxs,
segmentations):
class_name = detections.classes[cidx]
bbox_int = np.round(tlbr).astype(np.int32)
bounding_box = BoundingBoxD(bbox_int[0], bbox_int[1], bbox_int[2],
bbox_int[3])
detected_object_type = DetectedObjectType(class_name, score)
detected_object = DetectedObject(bounding_box, score,
detected_object_type)
if seg:
mask = seg.to_relative_mask().numpy().data
detected_object.mask = ImageContainer(Image(mask))
detected_objects.add(detected_object)
return detected_objects
def detect(self, image_data):
input_image = image_data.asarray().astype('uint8')
if self._rgb_to_bgr:
input_image = cv2.cvtColor(input_image, cv2.COLOR_RGB2BGR)
from mmdet.apis import inference_detector
detections = inference_detector(self._model, input_image)
if isinstance(detections, tuple):
bbox_result, segm_result = detections
else:
bbox_result, segm_result = detections, None
if np.size(bbox_result) > 0:
bboxes = np.vstack(bbox_result)
else:
bboxes = []
# convert segmentation masks
masks = []
if segm_result is not None:
segms = mmcv.concat_list(segm_result)
inds = np.where(bboxes[:, -1] > score_thr)[0]
for i in inds:
masks.append(maskUtils.decode(segms[i]).astype(np.bool))
# collect labels
labels = [
np.full(bbox.shape[0], i, dtype=np.int32)
for i, bbox in enumerate(bbox_result)
]
if np.size(labels) > 0:
labels = np.concatenate(labels)
else:
labels = []
# convert to kwiver format, apply threshold
output = DetectedObjectSet()
for bbox, label in zip(bboxes, labels):
class_confidence = float(bbox[-1])
if class_confidence < self._thresh:
continue
bbox_int = bbox.astype(np.int32)
bounding_box = BoundingBoxD(bbox_int[0], bbox_int[1], bbox_int[2],
bbox_int[3])
class_name = self._labels[label]
detected_object_type = DetectedObjectType(class_name,
class_confidence)
detected_object = DetectedObject(bounding_box,
np.max(class_confidence),
detected_object_type)
output.add(detected_object)
if np.size(labels) > 0 and self._display_detections:
mmcv.imshow_det_bboxes(input_image,
bboxes,
labels,
class_names=self._labels,
score_thr=self._thresh,
show=True)
return output
def refine(self, image_data, detections):
if len(detections) == 0:
return detections
img = image_data.asarray().astype('uint8')
predictor = self.predictor
scale = 1.0
img_max_x = np.shape(img)[1]
img_max_y = np.shape(img)[0]
if self._target_type_scales:
scale = self.compute_scale_factor(detections)
if scale != 1.0:
img_max_x = int(img_max_x * scale)
img_max_y = int(img_max_y * scale)
img = cv2.resize(img, (img_max_x, img_max_y))
# Extract patches for ROIs
image_chips = []
detection_ids = []
for i, det in enumerate(detections):
# Extract chip for this detection
bbox = det.bounding_box
bbox_min_x = int(bbox.min_x() * scale)
bbox_max_x = int(bbox.max_x() * scale)
bbox_min_y = int(bbox.min_y() * scale)
bbox_max_y = int(bbox.max_y() * scale)
if self._kwiver_config['chip_method'] == "fixed_width":
chip_width = int(self._kwiver_config['chip_width'])
half_width = int(chip_width / 2)
bbox_min_x = int((bbox_min_x + bbox_max_x) / 2) - half_width
bbox_min_y = int((bbox_min_y + bbox_max_y) / 2) - half_width
bbox_max_x = bbox_min_x + chip_width
bbox_max_y = bbox_min_y + chip_width
if self._border_exclude > 0:
if bbox_min_x <= self._border_exclude:
continue
if bbox_min_y <= self._border_exclude:
continue
if bbox_max_x >= img_max_x - self._border_exclude:
continue
if bbox_max_y >= img_max_y - self._border_exclude:
continue
else:
if bbox_min_x < 0:
bbox_min_x = 0
if bbox_min_y < 0:
bbox_min_y = 0
if bbox_max_x > img_max_x:
bbox_max_x = img_max_x
if bbox_max_y > img_max_y:
bbox_max_y = img_max_y
bbox_area = (bbox_max_x - bbox_min_x) * (bbox_max_y - bbox_min_y)
if self._area_lower_bound > 0 and bbox_area < self._area_lower_bound:
continue
if self._area_upper_bound > 0 and bbox_area > self._area_upper_bound:
continue
crop = img[bbox_min_y:bbox_max_y, bbox_min_x:bbox_max_x]
image_chips.append(crop)
detection_ids.append(i)
# Run classifier on ROIs
classifications = list(predictor.predict(image_chips))
# Put classifications back into detections
output = DetectedObjectSet()
for i, det in enumerate(detections):
if len(detection_ids) == 0 or i != detection_ids[0]:
output.add(det)
continue
new_class = classifications[0]
if new_class.data.get('prob', None) is not None:
# If we have a probability for each class, uses that
class_names = list(new_class.classes)
class_scores = list(new_class.prob)
else:
# Otherwise we only have the score for the predicted class
class_names = [new_class.classes[new_class.cidx]]
class_scores = [new_class.conf]
if self._average_prior and det.type is not None:
priors = det.type
prior_names = priors.class_names()
for name in prior_names:
if name in class_names:
class_scores[class_names.index(name)] += priors.score(
name)
else:
class_names.append(name)
class_scores.append(priors.score(name))
for i in range(len(class_scores)):
class_scores[i] = class_scores[i] * 0.5
detected_object_type = DetectedObjectType(class_names,
class_scores)
det.type = detected_object_type
output.add(det)
detection_ids.pop(0)
classifications.pop(0)
return output
def create_detected_object_set():
dos = DetectedObjectSet()
dos.add(DetectedObject(create_bounding_box()))
return dos
def _step(self):
try:
def timing(desc, f):
"""Return f(), printing a message about how long it took"""
start = timer()
result = f()
end = timer()
print('%%%', desc, ' elapsed time: ', end - start, sep='')
return result
print('step', self._step_id)
# grab image container from port using traits
in_img_c = self.grab_input_using_trait('image')
timestamp = self.grab_input_using_trait('timestamp')
dos_ptr = self.grab_input_using_trait('detected_object_set')
print('timestamp =', repr(timestamp))
# Get current frame
im = get_pil_image(in_img_c.image()).convert('RGB')
# Get detection bbox
if self._gtbbox_flag:
dos = self._m_bbox[self._step_id]
bbox_num = len(dos)
else:
dos = dos_ptr.select(self._select_threshold)
bbox_num = dos.size()
#print('bbox list len is', dos.size())
det_obj_set = DetectedObjectSet()
if bbox_num == 0:
print('!!! No bbox is provided on this frame. Skipping this frame !!!')
else:
# interaction features
grid_feature_list = timing('grid feature', lambda:
self._grid(im.size, dos, self._gtbbox_flag))
# appearance features (format: pytorch tensor)
pt_app_features = timing('app feature', lambda:
self._app_feature_extractor(im, dos, self._gtbbox_flag))
track_state_list = []
next_track_id = int(self._track_set.get_max_track_id()) + 1
# get new track state from new frame and detections
for idx, item in enumerate(dos):
if self._gtbbox_flag:
bbox = item
fid = self._step_id
ts = self._step_id
d_obj = DetectedObject(bbox=item, confidence=1.0)
else:
bbox = item.bounding_box()
fid = timestamp.get_frame()
ts = timestamp.get_time_usec()
d_obj = item
if self._add_features_to_detections:
# store app feature to detected_object
app_f = new_descriptor(g_config.A_F_num)
app_f[:] = pt_app_features[idx].numpy()
d_obj.set_descriptor(app_f)
det_obj_set.add(d_obj)
# build track state for current bbox for matching
cur_ts = track_state(frame_id=self._step_id,
bbox_center=bbox.center(),
interaction_feature=grid_feature_list[idx],
app_feature=pt_app_features[idx],
bbox=[int(bbox.min_x()), int(bbox.min_y()),
int(bbox.width()), int(bbox.height())],
detected_object=d_obj,
sys_frame_id=fid, sys_frame_time=ts)
track_state_list.append(cur_ts)
# if there are no tracks, generate new tracks from the track_state_list
if not self._track_flag:
next_track_id = self._track_set.add_new_track_state_list(next_track_id,
track_state_list, self._track_initialization_threshold)
self._track_flag = True
else:
# check whether we need to terminate a track
for track in list(self._track_set.iter_active()):
# terminating a track based on readin_frame_id or original_frame_id gap
if (self._step_id - track[-1].frame_id > self._terminate_track_threshold
or fid - track[-1].sys_frame_id > self._sys_terminate_track_threshold):
self._track_set.deactivate_track(track)
# call IOU tracker
if self._IOU_flag:
self._track_set, track_state_list = timing('IOU tracking', lambda: (
self._iou_tracker(self._track_set, track_state_list)
))
#print('***track_set len', len(self._track_set))
#print('***track_state_list len', len(track_state_list))
# estimate similarity matrix
similarity_mat, track_idx_list = timing('SRNN association', lambda: (
self._srnn_matching(self._track_set, track_state_list, self._ts_threshold)
))
# reset update_flag
self._track_set.reset_updated_flag()
# Hungarian algorithm
row_idx_list, col_idx_list = timing('Hungarian algorithm', lambda: (
sp.optimize.linear_sum_assignment(similarity_mat)
))
for i in range(len(row_idx_list)):
r = row_idx_list[i]
c = col_idx_list[i]
if -similarity_mat[r, c] < self._similarity_threshold:
# initialize a new track
if (track_state_list[c].detected_object.confidence()
>= self._track_initialization_threshold):
self._track_set.add_new_track_state(next_track_id,
track_state_list[c])
next_track_id += 1
else:
# add to existing track
self._track_set.update_track(track_idx_list[r], track_state_list[c])
# for the remaining unmatched track states, we initialize new tracks
if len(track_state_list) - len(col_idx_list) > 0:
for i in range(len(track_state_list)):
if (i not in col_idx_list
and (track_state_list[i].detected_object.confidence()
>= self._track_initialization_threshold)):
self._track_set.add_new_track_state(next_track_id,
track_state_list[i])
next_track_id += 1
print('total tracks', len(self._track_set))
# push track set to output port
ot_list = ts2ot_list(self._track_set)
ots = ObjectTrackSet(ot_list)
self.push_to_port_using_trait('object_track_set', ots)
self.push_to_port_using_trait('detected_object_set', det_obj_set)
self._step_id += 1
self._base_step()
except BaseException as e:
print( repr( e ) )
import traceback
print( traceback.format_exc() )
sys.stdout.flush()
raise
def extract_chips_for_dets(self, image_files, truth_sets):
import cv2
output_files = []
output_dets = []
for i in range(len(image_files)):
filename = image_files[i]
groundtruth = truth_sets[i]
detections = []
scale = 1.0
if self._target_type_scales:
scale = self.compute_scale_factor(groundtruth)
if len(groundtruth) > 0:
img = cv2.imread(filename)
if len(np.shape(img)) < 2:
continue
img_max_x = np.shape(img)[1]
img_max_y = np.shape(img)[0]
# Optionally scale image
if scale != 1.0:
img_max_x = int(scale * img_max_x)
img_max_y = int(scale * img_max_y)
img = cv2.resize(img, (img_max_x, img_max_y))
# Run optional background detector on data
if self._detector_model:
kw_image = Image(img)
kw_image_container = ImageContainer(kw_image)
detections = self._detector.detect(kw_image_container)
if len(groundtruth) == 0 and len(detections) == 0:
continue
overlaps = np.zeros((len(detections), len(groundtruth)))
det_boxes = []
for det in detections:
bbox = det.bounding_box
det_boxes.append((int(bbox.min_x()), int(bbox.min_y()),
int(bbox.width()), int(bbox.height())))
for i, gt in enumerate(groundtruth):
# Extract chip for this detection
bbox = gt.bounding_box
bbox_min_x = int(bbox.min_x() * scale)
bbox_max_x = int(bbox.max_x() * scale)
bbox_min_y = int(bbox.min_y() * scale)
bbox_max_y = int(bbox.max_y() * scale)
bbox_width = bbox_max_x - bbox_min_x
bbox_height = bbox_max_y - bbox_min_y
max_overlap = 0.0
for j, det in enumerate(det_boxes):
# Compute overlap between detection and truth
(det_min_x, det_min_y, det_width, det_height) = det
# Get the overlap rectangle
overlap_x0 = max(bbox_min_x, det_min_x)
overlap_y0 = max(bbox_min_y, det_min_y)
overlap_x1 = min(bbox_max_x, det_min_x + det_width)
overlap_y1 = min(bbox_max_y, det_min_y + det_height)
# Check if there is an overlap
if overlap_x1 - overlap_x0 <= 0 or overlap_y1 - overlap_y0 <= 0:
continue
# If yes, calculate the ratio of the overlap
det_area = float(det_width * det_height)
gt_area = float(bbox_width * bbox_height)
int_area = float(
(overlap_x1 - overlap_x0) * (overlap_y1 - overlap_y0))
overlap = min(int_area / det_area, int_area / gt_area)
overlaps[j, i] = overlap
if overlap >= self._min_overlap_for_association and overlap > max_overlap:
max_overlap = overlap
bbox_min_x = det_min_x
bbox_min_y = det_min_y
bbox_max_x = det_min_x + det_width
bbox_max_y = det_min_y + det_height
bbox_width = det_width
bbox_height = det_height
if self._chip_method == "fixed_width":
chip_width = int(self._chip_width)
half_width = int(chip_width / 2)
bbox_min_x = int(
(bbox_min_x + bbox_max_x) / 2) - half_width
bbox_min_y = int(
(bbox_min_y + bbox_max_y) / 2) - half_width
bbox_max_x = bbox_min_x + chip_width
bbox_max_y = bbox_min_y + chip_width
bbox_width = chip_width
bbox_height = chip_width
bbox_area = bbox_width * bbox_height
if self._area_lower_bound > 0 and bbox_area < self._area_lower_bound:
continue
if self._area_upper_bound > 0 and bbox_area > self._area_upper_bound:
continue
if self._reduce_category and gt.type() and \
gt.type().get_most_likely_class() == self._reduce_category and \
random.uniform( 0, 1 ) < 0.90:
continue
if self._border_exclude > 0:
if bbox_min_x <= self._border_exclude:
continue
if bbox_min_y <= self._border_exclude:
continue
if bbox_max_x >= img_max_x - self._border_exclude:
continue
if bbox_max_y >= img_max_y - self._border_exclude:
continue
crop = img[bbox_min_y:bbox_max_y, bbox_min_x:bbox_max_x]
self._sample_count = self._sample_count + 1
crop_str = ('%09d' % self._sample_count) + ".png"
new_file = os.path.join(self._chip_directory, crop_str)
cv2.imwrite(new_file, crop)
# Set new box size for this detection
gt.bounding_box = BoundingBoxD(0, 0,
np.shape(crop)[1],
np.shape(crop)[0])
new_set = DetectedObjectSet()
new_set.add(gt)
output_files.append(new_file)
output_dets.append(new_set)
neg_count = 0
for j, det in enumerate(detections):
if max(overlaps[j]) >= self._max_overlap_for_negative:
continue
bbox = det.bounding_box
bbox_min_x = int(bbox.min_x())
bbox_max_x = int(bbox.max_x())
bbox_min_y = int(bbox.min_y())
bbox_max_y = int(bbox.max_y())
bbox_width = bbox_max_x - bbox_min_x
bbox_height = bbox_max_y - bbox_min_y
bbox_area = bbox_width * bbox_height
if self._chip_method == "fixed_width":
chip_width = int(self._chip_width)
half_width = int(chip_width / 2)
bbox_min_x = int(
(bbox_min_x + bbox_max_x) / 2) - half_width
bbox_min_y = int(
(bbox_min_y + bbox_max_y) / 2) - half_width
bbox_max_x = bbox_min_x + chip_width
bbox_max_y = bbox_min_y + chip_width
bbox_width = chip_width
bbox_height = chip_width
if self._area_lower_bound > 0 and bbox_area < self._area_lower_bound:
continue
if self._area_upper_bound > 0 and bbox_area > self._area_upper_bound:
continue
if self._border_exclude > 0:
if bbox_min_x <= self._border_exclude:
continue
if bbox_min_y <= self._border_exclude:
continue
if bbox_max_x >= img_max_x - self._border_exclude:
continue
if bbox_max_y >= img_max_y - self._border_exclude:
continue
# Handle random factor
if self._max_neg_per_frame < 1.0 and random.uniform(
0, 1) > self._max_neg_per_frame:
break
crop = img[bbox_min_y:bbox_max_y, bbox_min_x:bbox_max_x]
self._sample_count = self._sample_count + 1
crop_str = ('%09d' % self._sample_count) + ".png"
new_file = os.path.join(self._chip_directory, crop_str)
cv2.imwrite(new_file, crop)
# Set new box size for this detection
det.bounding_box = BoundingBoxD(0, 0,
np.shape(crop)[1],
np.shape(crop)[0])
det.type = DetectedObjectType(self._negative_category, 1.0)
new_set = DetectedObjectSet()
new_set.add(det)
output_files.append(new_file)
output_dets.append(new_set)
# Check maximum negative count
neg_count = neg_count + 1
if neg_count > self._max_neg_per_frame:
break
return [output_files, output_dets]