def test():
base_path = '/Users/shuzhiliu/Google Drive/KyoceraRobotAI/mmdetection_tools/data'
imagefolder_path = '/Users/shuzhiliu/Google Drive/KyoceraRobotAI/mmdetection_tools/LocalData_Images'
dataset_id = '1940091026744'
image_id = '20191119T063709-cca043ed-32fe-4da0-ba75-e4a12b88eef4'
data1 = CocoTools(
json_file=f"{base_path}/{dataset_id}/annotations/train.json",
image_folder_path=imagefolder_path)
img1 = data1.get_original_image(image_id=image_id)
print(data1.images)
bboxes = data1.get_original_bboxes_list(image_id=image_id)
print(bboxes)
# img1 = np.zeros(shape=(720,1280,3), dtype=np.uint8)
for bbox in bboxes:
color = (random.randint(0, 255), random.randint(0, 255),
random.randint(0, 255))
# cv.rectangle(img1,(bbox[0],bbox[1]), (bbox[2],bbox[3]), 255)
# print(bbox)
# cv.rectangle(img=img1,rec=(bbox[1],bbox[0],bbox[3]-bbox[1],bbox[2]-bbox[0]), color=color, thickness=4)
cv.rectangle(img1, (bbox[1], bbox[0]), (bbox[3], bbox[2]), color, 4)
plt.imshow(img1)
plt.show()
g1 = GenCandidateAnchors()
print(len(g1.anchor_candidates_list))
ious = BboxTools.ious(g1.anchor_candidates_list, bboxes[0])
ious[np.argmax(ious)] = 1
print(len(ious))
ious_np = np.reshape(ious, newshape=(23, 40, 9))
index = np.where(ious_np == 1)
print(index)
def gen_target_anchor_bboxes_classes_for_debug(self,
image_id,
debuginfo=False):
bboxes = self.dataset_coco.get_original_bboxes_list(image_id=image_id)
sparse_targets = self.dataset_coco.get_original_category_sparse_list(
image_id=image_id)
bboxes_ious = [] # for each gt_bbox calculate ious with candidates
for bbox in bboxes:
ious = BboxTools.ious(
self.gen_candidate_anchors.anchor_candidates_list, bbox)
ious_temp = np.ones(shape=(len(ious)), dtype=np.float) * 0.5
# other author's implementations are use -1 to indicate ignoring, here use 0.5 to use max
ious_temp = np.where(
np.asarray(ious) > self.threshold_iou_rpn, 1, ious_temp)
ious_temp = np.where(np.asarray(ious) < 0.3, 0, ious_temp)
ious_temp[np.argmax(ious)] = 1
bboxes_ious.append(ious_temp)
# for each gt_box, determine the box reg target
target_anchor_bboxes = []
target_classes = []
for index, bbox_ious in enumerate(bboxes_ious):
ious_temp = np.reshape(
bbox_ious,
newshape=(self.gen_candidate_anchors.h,
self.gen_candidate_anchors.w,
self.gen_candidate_anchors.n_anchors))
candidate_boxes = self.gen_candidate_anchors.anchor_candidates[
np.where(ious_temp == 1)]
n = candidate_boxes.shape[0]
for i in range(n):
target_anchor_bboxes.append(candidate_boxes[i])
target_classes.append(sparse_targets[index])
return target_anchor_bboxes, target_classes
def test():
base_path = ''
imagefolder_path = ''
dataset_id = ''
image_id = ''
data1 = CocoTools(json_file=f"{base_path}/{dataset_id}/annotations/train.json",
image_folder_path=imagefolder_path)
img1 = data1.get_original_image(image_id=image_id)
print(data1.images)
bboxes = data1.get_original_bboxes_list(image_id=image_id)
print(bboxes)
# img1 = np.zeros(shape=(720,1280,3), dtype=np.uint8)
for bbox in bboxes:
color = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
# cv.rectangle(img1,(bbox[0],bbox[1]), (bbox[2],bbox[3]), 255)
# print(bbox)
# cv.rectangle(img=img1,rec=(bbox[1],bbox[0],bbox[3]-bbox[1],bbox[2]-bbox[0]), color=color, thickness=4)
cv.rectangle(img1, (bbox[1], bbox[0]), (bbox[3], bbox[2]), color, 4)
plt.imshow(img1)
plt.show()
g1 = GenCandidateAnchors()
print(len(g1.anchor_candidates_list))
ious = BboxTools.ious(g1.anchor_candidates_list, bboxes[0])
ious[np.argmax(ious)] = 1
print(len(ious))
ious_np = np.reshape(ious, newshape=(23, 40, 9))
index = np.where(ious_np == 1)
print(index)
def gen_train_target_anchor_boxreg_for_rpn(self, image_id, debuginfo=False):
bboxes = self.dataset_coco.get_original_bboxes_list(image_id=image_id)
# === resize ===
bboxes_ious = [] # for each gt_bbox calculate ious with candidates
for bbox in bboxes:
ious = BboxTools.ious(self.gen_candidate_anchors.anchor_candidates_list, bbox)
ious_temp = np.ones(shape=(len(ious)), dtype=np.float) * 0.5
# other author's implementations are use -1 to indicate ignoring, here use 0.5 to use max
ious_temp = np.where(np.asarray(ious) > self.threshold_iou_rpn, 1, ious_temp)
ious_temp = np.where(np.asarray(ious) < 0.3, 0, ious_temp)
ious_temp[np.argmax(ious)] = 1
bboxes_ious.append(ious_temp)
# for each candidate anchor, determine the anchor target
anchors_target = np.array(bboxes_ious)
anchors_target = np.max(anchors_target, axis=0)
anchors_target = np.reshape(anchors_target, newshape=(
self.gen_candidate_anchors.h, self.gen_candidate_anchors.w, self.gen_candidate_anchors.n_anchors))
if debuginfo:
print(f"[Debug INFO] Number of total gt bboxes :{len(bboxes)}")
print(
f"[Debug INFO] Number of total target anchors: {anchors_target[np.where(anchors_target == 1)].shape[0]}")
print(f"[Debug INFO] Shape of anchors_target: {anchors_target.shape}")
print(
f"[Debug INFO] Selected anchors: \n {self.gen_candidate_anchors.anchor_candidates[np.where(anchors_target == 1)]}")
# test
# self.anchor_generator.anchors_candidate[np.where(anchors_target==1)] = self.anchor_generator.anchors_candidate[np.where(anchors_target==1)] +100
# print(f"Selected anchors: \n {self.anchor_generator.anchors_candidate[np.where(anchors_target == 1)]}")
# for each gt_box, determine the box reg target
bbox_reg_target = np.zeros(
shape=(self.gen_candidate_anchors.h, self.gen_candidate_anchors.w, self.gen_candidate_anchors.n_anchors, 4),
dtype=np.float)
for index, bbox_ious in enumerate(bboxes_ious):
ious_temp = np.reshape(bbox_ious, newshape=(
self.gen_candidate_anchors.h, self.gen_candidate_anchors.w, self.gen_candidate_anchors.n_anchors))
gt_box = bboxes[index]
candidate_boxes = self.gen_candidate_anchors.anchor_candidates[np.where(ious_temp == 1)]
# print(candidate_boxes,gt_box)
box_reg = BboxTools.bbox_regression_target(candidate_boxes, gt_box)
# print(box_reg)
# print(bbox_tools.bbox_reg2truebox(candidate_boxes, box_reg))
bbox_reg_target[np.where(ious_temp == 1)] = box_reg
return anchors_target, bbox_reg_target
def gen_train_data_roi_one(self, image_id, bbox_list):
gt_bboxes = self.dataset_coco.get_original_bboxes_list(
image_id=image_id)
sparse_targets = self.dataset_coco.get_original_category_sparse_list(
image_id=image_id)
bboxes_ious = [] # for each gt_bbox calculate ious with candidates
for bbox in gt_bboxes:
ious = BboxTools.ious(bbox_list, bbox)
ious_temp = np.zeros(shape=(len(ious)), dtype=np.float)
# other author's implementations are use -1 to indicate ignoring, here use 0.5 to use max
ious_temp = np.where(
np.asarray(ious) > self.threshold_iou_roi, 1, ious_temp)
ious_temp[np.argmax(ious)] = 1
bboxes_ious.append(ious_temp)
# for each gt_box, determine the box reg target
original_img = self.gen_train_input_one(image_id)
input_images = []
input_box_filtered_by_iou = []
target_classes = []
target_bbox_reg = []
for index_gt, bbox_ious in enumerate(bboxes_ious):
candidate_boxes = np.asarray(bbox_list)[np.where(bbox_ious == 1)]
n = candidate_boxes.shape[0]
for i in range(n):
input_box_filtered_by_iou.append(candidate_boxes[i].astype(
np.float))
box_reg = BboxTools.bbox_regression_target(
pred_boxes=candidate_boxes[i].reshape((1, 4)),
gt_box=gt_bboxes[index_gt])
target_bbox_reg.append(box_reg.ravel())
target_classes.append(sparse_targets[index_gt])
input_images.append(original_img.astype(np.float))
for index_gt, bbox_gt in enumerate(gt_bboxes):
input_images.append(original_img.astype(np.float))
input_box_filtered_by_iou.append(bbox_gt.astype(np.float))
target_classes.append(sparse_targets[index_gt])
target_bbox_reg.append(np.array([0, 0, 0, 0], dtype=np.float))
return np.asarray(input_images).astype(
np.float), np.asarray(input_box_filtered_by_iou), np.asarray(
target_classes), np.asarray(target_bbox_reg)