def visualize_boxes_and_labels_on_image_array(current_frame_number,
image,
mode,
color_recognition_status,
boxes,
classes,
scores,
category_index,
y_reference=None,
deviation=None,
instance_masks=None,
keypoints=None,
use_normalized_coordinates=False,
max_boxes_to_draw=20,
min_score_thresh=.5,
agnostic_mode=False,
line_thickness=4):
"""Overlay labeled boxes on an image with formatted scores and label names.
This function groups boxes that correspond to the same location
and creates a display string for each detection and overlays these
on the image. Note that this function modifies the image in place, and returns
that same image.
Args:
image: uint8 numpy array with shape (img_height, img_width, 3)
boxes: a numpy array of shape [N, 4]
classes: a numpy array of shape [N]. Note that class indices are 1-based,
and match the keys in the label map.
scores: a numpy array of shape [N] or None. If scores=None, then
this function assumes that the boxes to be plotted are groundtruth
boxes and plot all boxes as black with no classes or scores.
category_index: a dict containing category dictionaries (each holding
category index `id` and category name `name`) keyed by category indices.
instance_masks: a numpy array of shape [N, image_height, image_width], can
be None
keypoints: a numpy array of shape [N, num_keypoints, 2], can
be None
use_normalized_coordinates: whether boxes is to be interpreted as
normalized coordinates or not.
max_boxes_to_draw: maximum number of boxes to visualize. If None, draw
all boxes.
min_score_thresh: minimum score threshold for a box to be visualized
agnostic_mode: boolean (default: False) controlling whether to evaluate in
class-agnostic mode or not. This mode will display scores but ignore
classes.
line_thickness: integer (default: 4) controlling line width of the boxes.
Returns:
uint8 numpy array with shape (img_height, img_width, 3) with overlaid boxes.
"""
# Create a display string (and color) for every box location, group any boxes
# that correspond to the same location.
csv_line_util = "not_available"
counter = 0
ROI_POSITION.insert(0, y_reference)
DEVIATION.insert(0, deviation)
is_vehicle_detected = []
mode_number.insert(0, mode)
is_color_recognition_enable.insert(0, color_recognition_status)
box_to_display_str_map = collections.defaultdict(list)
box_to_color_map = collections.defaultdict(str)
box_to_instance_masks_map = {}
box_to_keypoints_map = collections.defaultdict(list)
if not max_boxes_to_draw:
max_boxes_to_draw = boxes.shape[0]
for i in range(min(max_boxes_to_draw, boxes.shape[0])):
if scores is None or scores[i] > min_score_thresh:
box = tuple(boxes[i].tolist())
if instance_masks is not None:
box_to_instance_masks_map[box] = instance_masks[i]
if keypoints is not None:
box_to_keypoints_map[box].extend(keypoints[i])
if scores is None:
box_to_color_map[box] = 'black'
else:
if not agnostic_mode:
if classes[i] in category_index.keys():
class_name = category_index[classes[i]]['name']
else:
class_name = 'N/A'
display_str = '{}: {}%'.format(class_name,
int(100 * scores[i]))
else:
display_str = 'score: {}%'.format(int(100 * scores[i]))
box_to_display_str_map[box].append(display_str)
if agnostic_mode:
box_to_color_map[box] = 'DarkOrange'
else:
box_to_color_map[box] = STANDARD_COLORS[
classes[i] % len(STANDARD_COLORS)]
if (mode == 1):
counting_mode = ""
# Draw all boxes onto image.
for box, color in box_to_color_map.items():
ymin, xmin, ymax, xmax = box
if instance_masks is not None:
draw_mask_on_image_array(image,
box_to_instance_masks_map[box],
color=color)
display_str_list = box_to_display_str_map[box]
if (mode == 1):
counting_mode = counting_mode + str(display_str_list)
# we are interested just vehicles (i.e. cars and trucks)
if (("car" not in display_str_list[0])
or ("truck" not in display_str_list[0])
or ("bus" not in display_str_list[0])):
is_vehicle_detected, csv_line, update_csv = draw_bounding_box_on_image_array(
current_frame_number,
image,
ymin,
xmin,
ymax,
xmax,
color=color,
thickness=line_thickness,
display_str_list=box_to_display_str_map[box],
use_normalized_coordinates=use_normalized_coordinates)
if keypoints is not None:
draw_keypoints_on_image_array(
image,
box_to_keypoints_map[box],
color=color,
radius=line_thickness / 2,
use_normalized_coordinates=use_normalized_coordinates)
if (1 in is_vehicle_detected):
counter = 1
del is_vehicle_detected[:]
is_vehicle_detected = []
if (class_name == "boat"):
class_name = "truck"
csv_line_util = class_name + "," + csv_line
if (mode == 1):
counting_mode = counting_mode.replace("['", " ").replace("']",
" ").replace(
"%", "")
counting_mode = ''.join([
i for i in counting_mode.replace("['", " ").replace(
"']", " ").replace("%", "") if not i.isdigit()
])
counting_mode = str(custom_string_util.word_count(counting_mode))
counting_mode = counting_mode.replace("{", "").replace("}", "")
return counter, csv_line_util, counting_mode
else:
return counter, csv_line_util
def visualize_boxes_and_labels_on_image_array(bboxes,current_frame_number,
image,
mode,
boxes,
classes,
scores,
category_index,
targeted_objects=None,
y_reference=None,
deviation=None,
instance_masks=None,
keypoints=None,
use_normalized_coordinates=False,
max_boxes_to_draw=None,
min_score_thresh=.5,
agnostic_mode=False,
line_thickness=4):
"""Overlay labeled boxes on an image with formatted scores and label names.
This function groups boxes that correspond to the same location
and creates a display string for each detection and overlays these
on the image. Note that this function modifies the image in place, and returns
that same image.
Args:
image: uint8 numpy array with shape (img_height, img_width, 3)
boxes: a numpy array of shape [N, 4]
classes: a numpy array of shape [N]. Note that class indices are 1-based,
and match the keys in the label map.
scores: a numpy array of shape [N] or None. If scores=None, then
this function assumes that the boxes to be plotted are groundtruth
boxes and plot all boxes as black with no classes or scores.
category_index: a dict containing category dictionaries (each holding
category index `id` and category name `name`) keyed by category indices.
instance_masks: a numpy array of shape [N, image_height, image_width], can
be None
keypoints: a numpy array of shape [N, num_keypoints, 2], can
be None
use_normalized_coordinates: whether boxes is to be interpreted as
normalized coordinates or not.
max_boxes_to_draw: maximum number of boxes to visualize. If None, draw
all boxes.
min_score_thresh: minimum score threshold for a box to be visualized
agnostic_mode: boolean (default: False) controlling whether to evaluate in
class-agnostic mode or not. This mode will display scores but ignore
classes.
line_thickness: integer (default: 4) controlling line width of the boxes.
Returns:
uint8 numpy array with shape (img_height, img_width, 3) with overlaid boxes.
"""
# Create a display string (and color) for every box location, group any boxes
# that correspond to the same location.
csv_line_util = "not_available"
counter = 0
ROI_POSITION.insert(0,y_reference)
DEVIATION.insert(0,deviation)
is_vehicle_detected = []
mode_number.insert(0,mode)
box_to_display_str_map = collections.defaultdict(list)
box_to_color_map = collections.defaultdict(str)
box_to_instance_masks_map = {}
box_to_keypoints_map = collections.defaultdict(list)
if not max_boxes_to_draw:
max_boxes_to_draw = boxes.shape[0]
for i in range(min(max_boxes_to_draw, boxes.shape[0])):
if scores is None or scores[i] > min_score_thresh:
box = tuple(boxes[i].tolist())
if instance_masks is not None:
box_to_instance_masks_map[box] = instance_masks[i]
if keypoints is not None:
box_to_keypoints_map[box].extend(keypoints[i])
if scores is None:
box_to_color_map[box] = 'black'
else:
if not agnostic_mode:
if classes[i] in category_index.keys():
class_name = category_index[classes[i]]['name']
else:
class_name = 'N/A'
display_str = '{}: {}%'.format(class_name,int(100*scores[i]))
else:
display_str = 'score: {}%'.format(int(100 * scores[i]))
box_to_display_str_map[box].append(display_str)
if agnostic_mode:
box_to_color_map[box] = 'DarkOrange'
else:
box_to_color_map[box] = STANDARD_COLORS[
classes[i] % len(STANDARD_COLORS)]
if(mode == 1):
counting_mode = ""
count=["","","","","",""]
# Draw all boxes onto image.
for box, color in box_to_color_map.items():
ymin, xmin, ymax, xmax = box
'''if instance_masks is not None:
draw_mask_on_image_array(
image,
box_to_instance_masks_map[box],
color=color
)'''
im_height, im_width, channels = image.shape
#print(xmin*im_width,ymin*im_height,xmax*im_width,ymax*im_height)
for i in range(len(bboxes)):
if(xmin*im_width>=bboxes[i][0] and ymin*im_height>=bboxes[i][1] and xmax*im_width<=bboxes[i][0]+bboxes[i][2] and ymax*im_height<=bboxes[i][1]+bboxes[i][3]):
display_str_list=box_to_display_str_map[box]
count[i] = count[i] + str(display_str_list)
track_low.append(int((xmin*im_width+xmax*im_width)/2))
track_high.append(int((ymin*im_height+ymax*im_height)/2))
for j in range(len(track_low)):
#cv2.line(first_frame,track_low[j],track_low[j-1],4, (0, 0, 255), -1)
cv2.circle(first_frame,(track_low[j],track_high[j]), 4, (0, 0, 255), -1)
else:
display_str_list=""
if(mode == 1 and targeted_objects == None):
counting_mode = counting_mode + str(display_str_list)
#cv2.imshow('Path Tracker',first_frame)
if (targeted_objects == None):
is_vehicle_detected, csv_line, update_csv = draw_bounding_box_on_image_array(bboxes,current_frame_number,
image,
ymin,
xmin,
ymax,
xmax,
color=color,
thickness=line_thickness,
display_str_list=box_to_display_str_map[box],
use_normalized_coordinates=use_normalized_coordinates)
if(1 in is_vehicle_detected):
counter = 1
del is_vehicle_detected[:]
is_vehicle_detected = []
csv_line_util = class_name + "," + csv_line
if(mode == 1):
counting_mode = counting_mode.replace("['", " ").replace("']", " ").replace("%", "")
counting_mode = ''.join([i for i in counting_mode.replace("['", " ").replace("']", " ").replace("%", "") if not i.isdigit()])
counting_mode = str(custom_string_util.word_count(counting_mode))
counting_mode = counting_mode.replace("{", "").replace("}", "")
for i in range(len(bboxes)):
count[i] = count[i].replace("['", " ").replace("']", " ").replace("%", "")
count[i] = ''.join([i for i in count[i].replace("['", " ").replace("']", " ").replace("%", "") if not i.isdigit()])
count[i] = str(custom_string_util.word_count(count[i]))
count[i] = count[i].replace("{", "").replace("}", "")
return counter, csv_line_util, counting_mode ,count
else:
return counter, csv_line_util
