def test_debug(self, image, detections, debug_boxes, boxes, ratio, coco,
step):
detections = detections.reshape(-1, 8)
detections[:, 0:4:2] /= ratio[0]
detections[:, 1:4:2] /= ratio[1]
debug_boxes = debug_boxes.reshape(-1, 4)
debug_boxes[:, 0:4:2] /= ratio[0]
debug_boxes[:, 1:4:2] /= ratio[1]
classes = detections[..., -1].astype(np.int64)
# reject detections with negative scores
keep_inds = (detections[:, 4] > -1)
detections = detections[keep_inds]
classes = classes[keep_inds]
top_bboxes = {}
for j in range(self.categories):
keep_inds = (classes == j)
top_bboxes[j + 1] = detections[keep_inds][:,
0:7].astype(np.float32)
if self.merge_bbox:
top_bboxes[j + 1] = soft_nms_merge(top_bboxes[j + 1],
Nt=0.5,
method=2,
weight_exp=8)
else:
top_bboxes[j + 1] = soft_nms(top_bboxes[j + 1],
Nt=0.5,
method=2)
top_bboxes[j + 1] = top_bboxes[j + 1][:, 0:5]
scores = np.hstack(
[top_bboxes[j][:, -1] for j in range(1, self.categories + 1)])
if len(scores) > self.max_per_image:
kth = len(scores) - self.max_per_image
thresh = np.partition(scores, kth)[kth]
for j in range(1, self.categories + 1):
keep_inds = (top_bboxes[j][:, -1] >= thresh)
top_bboxes[j] = top_bboxes[j][keep_inds]
# if len(top_bboxes[j])!=0:
# print(top_bboxes[j].shape)
image = (image * 255).astype(np.uint8)
bboxes = {}
for j in range(1, self.categories + 1):
#if step>10000:
keep_inds = (top_bboxes[j][:, -1] > 0.5)
top_bboxes[j] = top_bboxes[j][keep_inds]
cat_name = coco.class_name(j)
cat_size = cv2.getTextSize(cat_name, cv2.FONT_HERSHEY_SIMPLEX, 0.5,
2)[0]
color = np.random.random((3, )) * 0.6 + 0.4
color = color * 255
color = color.astype(np.int32).tolist()
for bbox in top_bboxes[j]:
bbox = bbox[0:4].astype(np.int32)
if bbox[1] - cat_size[1] - 2 < 0:
cv2.rectangle(
image, (bbox[0], bbox[1] + 2),
(bbox[0] + cat_size[0], bbox[1] + cat_size[1] + 2),
color, -1)
cv2.putText(image,
cat_name, (bbox[0], bbox[1] + cat_size[1] + 2),
cv2.FONT_HERSHEY_SIMPLEX,
0.5, (0, 0, 0),
thickness=1)
else:
cv2.rectangle(image, (bbox[0], bbox[1] - cat_size[1] - 2),
(bbox[0] + cat_size[0], bbox[1] - 2), color,
-1)
cv2.putText(image,
cat_name, (bbox[0], bbox[1] - 2),
cv2.FONT_HERSHEY_SIMPLEX,
0.5, (0, 0, 0),
thickness=1)
cv2.rectangle(image, (bbox[0], bbox[1]), (bbox[2], bbox[3]),
color, 2)
for b in boxes:
cv2.rectangle(image, (b[0], b[1]), (b[2], b[3]), (0, 0, 255),
1)
for i in range(len(debug_boxes)):
color = np.random.random((3, )) * 0.6 + 0.4
color = color * 255
color = color.astype(np.int32).tolist()
cv2.circle(image, (debug_boxes[i][0], debug_boxes[i][1]), 2, color,
2)
cv2.circle(image, (debug_boxes[i][2], debug_boxes[i][3]), 2, color,
2)
cv2.imwrite(os.path.join(self.debug_dir, str(step) + '.jpg'), image)
def test(self, sess):
debug_dir = os.path.join(result_dir, "debug")
if not os.path.exists(debug_dir):
os.makedirs(debug_dir)
img_names = self.coco.get_all_img()
num = len(img_names)
for img_name in tqdm(img_names):
img = self.coco.read_img(img_name)
height, width = img.shape[0:2]
detections = []
for scale in test_scales:
new_height = int(height * scale)
new_width = int(width * scale)
new_center = np.array([new_height // 2, new_width // 2])
inp_height = new_height | 127
inp_width = new_width | 127
images = np.zeros((1, inp_height, inp_width, 3),
dtype=np.float32)
ratios = np.zeros((1, 2), dtype=np.float32)
borders = np.zeros((1, 4), dtype=np.float32)
sizes = np.zeros((1, 2), dtype=np.float32)
out_height, out_width = (inp_height + 1) // 4, (inp_width +
1) // 4
height_ratio = out_height / inp_height
width_ratio = out_width / inp_width
resized_image = cv2.resize(image, (new_width, new_height))
resized_image, border, offset = crop_image(
resized_image, new_center, [inp_height, inp_width])
resized_image = resized_image / 255.
#normalize_(resized_image, db.mean, db.std)
images[0] = resized_image
borders[0] = border
sizes[0] = [int(height * scale), int(width * scale)]
ratios[0] = [height_ratio, width_ratio]
images = np.concatenate((images, images[:, :, ::-1, :]),
axis=0)
images = tf.convert_to_tensor(images)
is_training = tf.convert_to_tensor(False)
outs = self.net.corner_net(images, is_training=is_training)
dets_tensor = self.net.decode(*outs[-6:])
dets = sess.run(dets_tensor)
dets = dets.reshape(2, -1, 8)
dets[1, :, [0, 2]] = out_width - dets[1, :, [2, 0]]
dets = dets.reshape(1, -1, 8)
dets = rescale_dets(dets, ratios, borders, sizes)
dets[:, :, 0:4] /= scale
detections.append(dets)
detections = np.concatenate(detections, axis=1)
classes = detections[..., -1]
classes = classes[0]
detections = detections[0]
# reject detections with negative scores
keep_inds = (detections[:, 4] > -1)
detections = detections[keep_inds]
classes = classes[keep_inds]
top_bboxes[image_id] = {}
for j in range(categories):
keep_inds = (classes == j)
top_bboxes[image_id][j +
1] = detections[keep_inds][:, 0:7].astype(
np.float32)
if merge_bbox:
top_bboxes[image_id][j + 1] = soft_nms_merge(
top_bboxes[image_id][j + 1],
Nt=nms_threshold,
method=2,
weight_exp=weight_exp)
else:
top_bboxes[image_id][j + 1] = soft_nms(
top_bboxes[image_id][j + 1],
Nt=nms_threshold,
method=nms_algorithm)
top_bboxes[image_id][j + 1] = top_bboxes[image_id][j + 1][:,
0:5]
scores = np.hstack([
top_bboxes[image_id][j][:, -1]
for j in range(1, categories + 1)
])
if len(scores) > max_per_image:
kth = len(scores) - max_per_image
thresh = np.partition(scores, kth)[kth]
for j in range(1, categories + 1):
keep_inds = (top_bboxes[image_id][j][:, -1] >= thresh)
top_bboxes[image_id][j] = top_bboxes[image_id][j][
keep_inds]
if debug:
image = self.coco.read_img(img_name)
bboxes = {}
for j in range(1, categories + 1):
keep_inds = (top_bboxes[image_id][j][:, -1] > 0.5)
cat_name = self.coco.class_name(j)
cat_size = cv2.getTextSize(cat_name,
cv2.FONT_HERSHEY_SIMPLEX, 0.5,
2)[0]
color = np.random.random((3, )) * 0.6 + 0.4
color = color * 255
color = color.astype(np.int32).tolist()
for bbox in top_bboxes[image_id][j][keep_inds]:
bbox = bbox[0:4].astype(np.int32)
if bbox[1] - cat_size[1] - 2 < 0:
cv2.rectangle(image, (bbox[0], bbox[1] + 2),
(bbox[0] + cat_size[0],
bbox[1] + cat_size[1] + 2), color,
-1)
cv2.putText(image,
cat_name,
(bbox[0], bbox[1] + cat_size[1] + 2),
cv2.FONT_HERSHEY_SIMPLEX,
0.5, (0, 0, 0),
thickness=1)
else:
cv2.rectangle(image,
(bbox[0], bbox[1] - cat_size[1] - 2),
(bbox[0] + cat_size[0], bbox[1] - 2),
color, -1)
cv2.putText(image,
cat_name, (bbox[0], bbox[1] - 2),
cv2.FONT_HERSHEY_SIMPLEX,
0.5, (0, 0, 0),
thickness=1)
cv2.rectangle(image, (bbox[0], bbox[1]),
(bbox[2], bbox[3]), color, 2)
debug_file = os.path.join(debug_dir, {}.format(img_name))
# result_json = os.path.join(result_dir, "results.json")
# detections = db.convert_to_coco(top_bboxes)
# with open(result_json, "w") as f:
# json.dump(detections, f)
# cls_ids = list(range(1, categories + 1))
# image_ids = [db.image_ids(ind) for ind in db_inds]
# db.evaluate(result_json, cls_ids, image_ids)
return 0
def test(self,sess):
self.batch_i=cfg.batch_size
images, tags_tl, tags_br,heatmaps_tl, heatmaps_br, tags_mask, offsets_tl, offsets_br,boxes,ratio=self.data_train.get_batch_data(self.batch_i)
outs,test_outs=self.net.corner_net(images,tags_tl,tags_br,is_training=False)
dets_tensor =self.net.decode(*test_outs)
dets=sess.run(dets_tensor)
dets = dets.reshape(2, -1, 8)
dets[1, :, [0, 2]] = out_width - dets[1, :, [2, 0]]
dets = dets.reshape(1, -1, 8)
print(dets)
return
result_dir = os.path.join(self.result_dir, "result")
if not os.path.exists(result_dir):
os.makedirs(result_dir)
img_names=self.coco.get_all_img()
num=len(img_names)
for img_name in tqdm(img_names):
img=self.coco.read_img(img_name)
height, width = img.shape[0:2]
detections=[]
for scale in self.test_scales:
new_height = int(height * scale)
new_width = int(width * scale)
new_center = np.array([new_height // 2, new_width // 2])
inp_height = new_height | 127
inp_width = new_width | 127
images = np.zeros((1, inp_height, inp_width, 3), dtype=np.float32)
ratios = np.zeros((1, 2), dtype=np.float32)
borders = np.zeros((1, 4), dtype=np.float32)
sizes = np.zeros((1, 2), dtype=np.float32)
out_height, out_width = (inp_height + 1) // 4, (inp_width + 1) // 4
height_ratio = out_height / inp_height
width_ratio = out_width / inp_width
resized_image = cv2.resize(images[0], (new_width, new_height))
resized_image, border, offset = crop_image(resized_image, new_center, [inp_height, inp_width])
resized_image = resized_image / 255.
#normalize_(resized_image, db.mean, db.std)
images[0] = resized_image
borders[0] = border
sizes[0] = [int(height * scale), int(width * scale)]
ratios[0] = [height_ratio, width_ratio]
print(images.shape)
images = np.concatenate((images, images[:, :, ::-1, :]), axis=0)
images = tf.convert_to_tensor(images)
is_training=tf.convert_to_tensor(False)
outs=self.net.corner_net(images,gt_tag_tl=None,is_training=is_training)
dets_tensor=self.net.decode(*outs[-6:])
dets=sess.run(dets_tensor)
dets = dets.reshape(2, -1, 8)
dets[1, :, [0, 2]] = out_width - dets[1, :, [2, 0]]
dets = dets.reshape(1, -1, 8)
dets=rescale_dets(dets, ratios, borders, sizes)
dets[:, :, 0:4] /= scale
detections.append(dets)
detections = np.concatenate(detections, axis=1)
classes = detections[..., -1]
classes = classes[0]
detections = detections[0]
# reject detections with negative scores
keep_inds = (detections[:, 4] > -1)
detections = detections[keep_inds]
classes = classes[keep_inds]
top_bboxes[image_id] = {}
for j in range(categories):
keep_inds = (classes == j)
top_bboxes[image_id][j + 1] = detections[keep_inds][:, 0:7].astype(np.float32)
if merge_bbox:
top_bboxes[image_id][j + 1]=soft_nms_merge(top_bboxes[image_id][j + 1], Nt=nms_threshold, method=2, weight_exp=weight_exp)
else:
top_bboxes[image_id][j + 1]=soft_nms(top_bboxes[image_id][j + 1], Nt=nms_threshold, method=nms_algorithm)
top_bboxes[image_id][j + 1] = top_bboxes[image_id][j + 1][:, 0:5]
scores = np.hstack([
top_bboxes[image_id][j][:, -1]
for j in range(1, categories + 1)
])
if len(scores) > max_per_image:
kth = len(scores) - max_per_image
thresh = np.partition(scores, kth)[kth]
for j in range(1, categories + 1):
keep_inds = (top_bboxes[image_id][j][:, -1] >= thresh)
top_bboxes[image_id][j] = top_bboxes[image_id][j][keep_inds]
result_json = os.path.join(result_dir, "results.json")
detections = db.convert_to_coco(top_bboxes)
with open(result_json, "w") as f:
json.dump(detections, f)
cls_ids = list(range(1, categories + 1))
image_ids = [db.image_ids(ind) for ind in db_inds]
db.evaluate(result_json, cls_ids, image_ids)
return 0