def get_localization(self, image, visual=False):
# Number of classes the object detector can identify
NUM_CLASSES = 2
# dictionary mapping integers to appropriate string labels would be fine
label_map = label_map_util.load_labelmap(self.PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(
label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)
with self.detection_graph.as_default():
image_expanded = np.expand_dims(image, axis=0)
(boxes, scores, classes, num_detections) = self.sess.run(
[self.boxes, self.scores, self.classes, self.num_detections],
feed_dict={self.image_tensor: image_expanded})
if visual == True:
visualization_utils.visualize_boxes_and_labels_on_image_array_tracker(
image,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
min_score_thresh=.4,
skip_labels=True,
line_thickness=3)
plt.figure(figsize=(9, 6))
plt.imshow(image)
plt.show()
boxes = np.squeeze(boxes)
classes = np.squeeze(classes)
scores = np.squeeze(scores)
cls = classes.tolist()
idx_vec = [i for i, v in enumerate(cls) if ((scores[i] > 0.6))]
# 有變化時會從[0]-->[0,1]
print('show idex_vec::::', idx_vec)
if len(idx_vec) == 0:
print(
'there are not any detections, passing to the next frame...'
)
else:
tmp_object_boxes = []
for idx in idx_vec:
dim = image.shape[0:2]
class_id = classes[idx]
# print(class_id)
box = self.box_normal_to_pixel(boxes[idx], dim, class_id)
box_h = box[2] - box[0]
box_w = box[3] - box[1]
ratio = box_h / (box_w + 0.01)
#if ((ratio < 0.8) and (box_h>20) and (box_w>20)):
tmp_object_boxes.append(box)
#print(box, ', confidence: ', scores[idx], 'ratio:', ratio)
self.object_boxes = tmp_object_boxes
return self.object_boxes
def get_localization(self, image, visual=False):
global category_index
input_image = np.asarray(image)
image_np_expanded = np.expand_dims(input_image, axis=0)
output_dict = dgv.run_inference_for_single_image(
config.HOST, image_np_expanded)
# Run inference
detection_boxes = output_dict['detection_boxes']
detection_scores = output_dict['detection_scores']
detection_classes = output_dict['detection_classes']
num_detections = output_dict['num_detections']
# Actual detection.
(
boxes, scores, classes, num
) = detection_boxes, detection_scores, detection_classes, num_detections
if visual == True:
visualization_utils.visualize_boxes_and_labels_on_image_array_tracker(
image,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
min_score_thresh=threshold,
line_thickness=3)
plt.figure(figsize=(9, 6))
plt.imshow(image)
plt.show()
boxes = np.squeeze(boxes)
classes = np.squeeze(classes)
scores = np.squeeze(scores)
cls = classes.tolist()
idx_vec = [i for i, v in enumerate(cls) if ((scores[i] > threshold))]
if len(idx_vec) == 0:
print('there are not any detections, passing to the next frame...')
else:
tmp_object_boxes = []
for idx in idx_vec:
dim = image.shape[0:2]
box = self.box_normal_to_pixel(boxes[idx], dim)
box_h = box[2] - box[0]
box_w = box[3] - box[1]
ratio = box_h / (box_w + 0.01)
#if ((ratio < 0.8) and (box_h>20) and (box_w>20)):
tmp_object_boxes.append(box)
#print(box, ', confidence: ', scores[idx], 'ratio:', ratio)
self.object_boxes = tmp_object_boxes
return self.object_boxes, output_dict
def get_localization(self, image, visual=False):
category_index = {1: {'id': 1, 'name': u'player'}}
with self.detection_graph.as_default():
image_expanded = np.expand_dims(image, axis=0)
(boxes, scores, classes, num_detections) = self.sess.run(
[self.boxes, self.scores, self.classes, self.num_detections],
feed_dict={self.image_tensor: image_expanded})
if visual == True:
visualization_utils.visualize_boxes_and_labels_on_image_array_tracker(
image,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
min_score_thresh=.4,
line_thickness=3)
plt.figure(figsize=(9, 6))
plt.imshow(image)
plt.show()
boxes = np.squeeze(boxes)
classes = np.squeeze(classes)
scores = np.squeeze(scores)
cls = classes.tolist()
idx_vec = [i for i, v in enumerate(cls) if ((scores[i] > 0.6))]
if len(idx_vec) == 0:
print(
'there are not any detections, passing to the next frame...'
)
else:
tmp_object_boxes = []
for idx in idx_vec:
dim = image.shape[0:2]
box = self.box_normal_to_pixel(boxes[idx], dim)
box_h = box[2] - box[0]
box_w = box[3] - box[1]
ratio = box_h / (box_w + 0.01)
#if ((ratio < 0.8) and (box_h>20) and (box_w>20)):
tmp_object_boxes.append(box)
#print(box, ', confidence: ', scores[idx], 'ratio:', ratio)
self.object_boxes = tmp_object_boxes
return self.object_boxes