def get_cfm_result(self):
# detection threshold for each class
# (this is adaptively set based on the max_per_set constraint)
thresh = -np.inf * np.ones(self.num_classes)
# top_scores will hold one min heap of scores per class (used to enforce
# the max_per_set constraint)
top_scores = [[] for _ in xrange(self.num_classes)]
# all detections and segmentation are collected into a list:
# Since the number of dets/segs are of variable size
all_boxes = [[[] for _ in xrange(self.num_images)]
for _ in xrange(self.num_classes)]
all_masks = [[[] for _ in xrange(self.num_images)]
for _ in xrange(self.num_classes)]
_t = {'im_detect': Timer(), 'misc': Timer()}
for i in xrange(self.num_images):
_t['im_detect'].tic()
masks, boxes, seg_scores = self.cfm_network_forward(i)
for j in xrange(1, self.num_classes):
inds = np.where(seg_scores[:, j] > thresh[j])[0]
cls_scores = seg_scores[inds, j]
cls_boxes = boxes[inds, :]
cls_masks = masks[inds, :]
top_inds = np.argsort(-cls_scores)[:self.max_per_image]
cls_scores = cls_scores[top_inds]
cls_boxes = cls_boxes[top_inds, :]
cls_masks = cls_masks[top_inds, :]
# push new scores onto the min heap
for val in cls_scores:
heapq.heappush(top_scores[j], val)
# if we've collected more than the max number of detection,
# then pop items off the min heap and update the class threshold
if len(top_scores[j]) > self.max_per_set:
while len(top_scores[j]) > self.max_per_set:
heapq.heappop(top_scores[j])
thresh[j] = top_scores[j][0]
box_before_nms = np.hstack((cls_boxes, cls_scores[:, np.newaxis]))\
.astype(np.float32, copy=False)
mask_before_nms = cls_masks.astype(np.float32, copy=False)
all_boxes[j][i], all_masks[j][i] = apply_nms_mask_single(
box_before_nms, mask_before_nms, cfg.TEST.NMS)
_t['im_detect'].toc()
print 'process image %d/%d, forward average time %f' % (
i, self.num_images, _t['im_detect'].average_time)
for j in xrange(1, self.num_classes):
for i in xrange(self.num_images):
inds = np.where(all_boxes[j][i][:, -1] > thresh[j])[0]
all_boxes[j][i] = all_boxes[j][i][inds, :]
all_masks[j][i] = all_masks[j][i][inds]
return all_boxes, all_masks
def get_cfm_result(self):
# detection threshold for each class
# (this is adaptively set based on the max_per_set constraint)
thresh = -np.inf * np.ones(self.num_classes)
# top_scores will hold one min heap of scores per class (used to enforce
# the max_per_set constraint)
top_scores = [[] for _ in xrange(self.num_classes)]
# all detections and segmentation are collected into a list:
# Since the number of dets/segs are of variable size
all_boxes = [[[] for _ in xrange(self.num_images)]
for _ in xrange(self.num_classes)]
all_masks = [[[] for _ in xrange(self.num_images)]
for _ in xrange(self.num_classes)]
_t = {'im_detect': Timer(), 'misc': Timer()}
for i in xrange(self.num_images):
_t['im_detect'].tic()
masks, boxes, seg_scores = self.cfm_network_forward(i)
for j in xrange(1, self.num_classes):
inds = np.where(seg_scores[:, j] > thresh[j])[0]
cls_scores = seg_scores[inds, j]
cls_boxes = boxes[inds, :]
cls_masks = masks[inds, :]
top_inds = np.argsort(-cls_scores)[:self.max_per_image]
cls_scores = cls_scores[top_inds]
cls_boxes = cls_boxes[top_inds, :]
cls_masks = cls_masks[top_inds, :]
# push new scores onto the min heap
for val in cls_scores:
heapq.heappush(top_scores[j], val)
# if we've collected more than the max number of detection,
# then pop items off the min heap and update the class threshold
if len(top_scores[j]) > self.max_per_set:
while len(top_scores[j]) > self.max_per_set:
heapq.heappop(top_scores[j])
thresh[j] = top_scores[j][0]
box_before_nms = np.hstack((cls_boxes, cls_scores[:, np.newaxis]))\
.astype(np.float32, copy=False)
mask_before_nms = cls_masks.astype(np.float32, copy=False)
all_boxes[j][i], all_masks[j][i] = apply_nms_mask_single(box_before_nms, mask_before_nms, cfg.TEST.NMS)
_t['im_detect'].toc()
print 'process image %d/%d, forward average time %f' % (i, self.num_images,
_t['im_detect'].average_time)
for j in xrange(1, self.num_classes):
for i in xrange(self.num_images):
inds = np.where(all_boxes[j][i][:, -1] > thresh[j])[0]
all_boxes[j][i] = all_boxes[j][i][inds, :]
all_masks[j][i] = all_masks[j][i][inds]
return all_boxes, all_masks
def get_segmentation_result(self):
# detection threshold for each class
# (this is adaptively set based on the max_per_set constraint)
thresh = -np.inf * np.ones(self.num_classes)
# top_scores will hold one min heap of scores per class (used to enforce
# the max_per_set constraint)
top_scores = [[] for _ in xrange(self.num_classes)]
# all detections and segmentation are collected into a list:
# Since the number of dets/segs are of variable size
all_boxes = [[[] for _ in xrange(self.num_images)]
for _ in xrange(self.num_classes)]
all_masks = [[[] for _ in xrange(self.num_images)]
for _ in xrange(self.num_classes)]
_t = {'im_detect': Timer(), 'misc': Timer()}
for i in xrange(self.num_images):
im = cv2.imread(self.imdb.image_path_at(i))
_t['im_detect'].tic()
masks, boxes, seg_scores = self._segmentation_forward(im)
_t['im_detect'].toc()
if not cfg.TEST.USE_MASK_MERGE:
for j in xrange(1, self.num_classes):
inds = np.where(seg_scores[:, j] > thresh[j])[0]
cls_scores = seg_scores[inds, j]
cls_boxes = boxes[inds, :]
cls_masks = masks[inds, :]
top_inds = np.argsort(-cls_scores)[:self.max_per_image]
cls_scores = cls_scores[top_inds]
cls_boxes = cls_boxes[top_inds, :]
cls_masks = cls_masks[top_inds, :]
# push new scores onto the min heap
for val in cls_scores:
heapq.heappush(top_scores[j], val)
# if we've collected more than the max number of detection,
# then pop items off the min heap and update the class threshold
if len(top_scores[j]) > self.max_per_set:
while len(top_scores[j]) > self.max_per_set:
heapq.heappop(top_scores[j])
thresh[j] = top_scores[j][0]
# Add new boxes into record
box_before_nms = np.hstack((cls_boxes, cls_scores[:, np.newaxis]))\
.astype(np.float32, copy=False)
mask_before_nms = cls_masks.astype(np.float32, copy=False)
all_boxes[j][i], all_masks[j][i] = apply_nms_mask_single(
box_before_nms, mask_before_nms, cfg.TEST.NMS)
else:
if cfg.TEST.USE_GPU_MASK_MERGE:
result_mask, result_box = gpu_mask_voting(
masks, boxes, seg_scores, self.num_classes,
self.max_per_image, im.shape[1], im.shape[0])
else:
result_box, result_mask = cpu_mask_voting(
masks, boxes, seg_scores, self.num_classes,
self.max_per_image, im.shape[1], im.shape[0])
# no need to create a min heap since the output will not exceed max number of detection
for j in xrange(1, self.num_classes):
all_boxes[j][i] = result_box[j - 1]
all_masks[j][i] = result_mask[j - 1]
print 'process image %d/%d, forward average time %f' % (
i, self.num_images, _t['im_detect'].average_time)
for j in xrange(1, self.num_classes):
for i in xrange(self.num_images):
inds = np.where(all_boxes[j][i][:, -1] > thresh[j])[0]
all_boxes[j][i] = all_boxes[j][i][inds, :]
all_masks[j][i] = all_masks[j][i][inds]
return all_boxes, all_masks
def get_segmentation_result(self):
# detection threshold for each class
# (this is adaptively set based on the max_per_set constraint)
thresh = -np.inf * np.ones(self.num_classes)
# top_scores will hold one min heap of scores per class (used to enforce
# the max_per_set constraint)
top_scores = [[] for _ in xrange(self.num_classes)]
# all detections and segmentation are collected into a list:
# Since the number of dets/segs are of variable size
all_boxes = [[[] for _ in xrange(self.num_images)]
for _ in xrange(self.num_classes)]
all_masks = [[[] for _ in xrange(self.num_images)]
for _ in xrange(self.num_classes)]
_t = {'im_detect': Timer(), 'misc': Timer()}
for i in xrange(self.num_images):
im = cv2.imread(self.imdb.image_path_at(i))
_t['im_detect'].tic()
masks, boxes, seg_scores = self._segmentation_forward(im)
_t['im_detect'].toc()
if not cfg.TEST.USE_MASK_MERGE:
for j in xrange(1, self.num_classes):
inds = np.where(seg_scores[:, j] > thresh[j])[0]
cls_scores = seg_scores[inds, j]
cls_boxes = boxes[inds, :]
cls_masks = masks[inds, :]
top_inds = np.argsort(-cls_scores)[:self.max_per_image]
cls_scores = cls_scores[top_inds]
cls_boxes = cls_boxes[top_inds, :]
cls_masks = cls_masks[top_inds, :]
# push new scores onto the min heap
for val in cls_scores:
heapq.heappush(top_scores[j], val)
# if we've collected more than the max number of detection,
# then pop items off the min heap and update the class threshold
if len(top_scores[j]) > self.max_per_set:
while len(top_scores[j]) > self.max_per_set:
heapq.heappop(top_scores[j])
thresh[j] = top_scores[j][0]
# Add new boxes into record
box_before_nms = np.hstack((cls_boxes, cls_scores[:, np.newaxis]))\
.astype(np.float32, copy=False)
mask_before_nms = cls_masks.astype(np.float32, copy=False)
all_boxes[j][i], all_masks[j][i] = apply_nms_mask_single(box_before_nms, mask_before_nms, cfg.TEST.NMS)
else:
if cfg.TEST.USE_GPU_MASK_MERGE:
result_mask, result_box = gpu_mask_voting(masks, boxes, seg_scores, self.num_classes,
self.max_per_image, im.shape[1], im.shape[0])
else:
result_box, result_mask = cpu_mask_voting(masks, boxes, seg_scores, self.num_classes,
self.max_per_image, im.shape[1], im.shape[0])
# no need to create a min heap since the output will not exceed max number of detection
for j in xrange(1, self.num_classes):
all_boxes[j][i] = result_box[j-1]
all_masks[j][i] = result_mask[j-1]
print 'process image %d/%d, forward average time %f' % (i, self.num_images,
_t['im_detect'].average_time)
for j in xrange(1, self.num_classes):
for i in xrange(self.num_images):
inds = np.where(all_boxes[j][i][:, -1] > thresh[j])[0]
all_boxes[j][i] = all_boxes[j][i][inds, :]
all_masks[j][i] = all_masks[j][i][inds]
return all_boxes, all_masks
