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visualization_utils.py
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visualization_utils.py
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#----------------------------------------------
#--- Author : Ahmet Ozlu
#--- Mail : [email protected]
#--- Date : 27th January 2018
#----------------------------------------------
"""A set of functions that are used for visualization.
These functions often receive an image, perform some visualization on the image.
The functions do not return a value, instead they modify the image itself.
"""
# Imports
import collections
import functools
import matplotlib.pyplot as plt
import numpy as np
import PIL.Image as Image
import PIL.ImageColor as ImageColor
import PIL.ImageDraw as ImageDraw
import PIL.ImageFont as ImageFont
import six
import tensorflow as tf
import cv2
import numpy
import os
# string utils - import
from utils.string_utils import custom_string_util
# image utils - image saver import
from utils.image_utils import image_saver
# predicted_speed predicted_color module - import
from utils.object_counting_module import object_counter_y_axis
# predicted_speed predicted_color module - import
from utils.object_counting_module import object_counter_x_axis
# color recognition module - import
from utils.color_recognition_module import color_recognition_api
# Variables
is_object_detected = [0]
roi_position = [0]
deviation_value = [0]
is_color_recognition_enable = [0]
x_axis = [0]
y_axis = [0]
standalone_image = [0]
_TITLE_LEFT_MARGIN = 10
_TITLE_TOP_MARGIN = 10
STANDARD_COLORS = [
'AliceBlue', 'Chartreuse', 'Aqua', 'Aquamarine', 'Azure', 'Beige', 'Bisque',
'BlanchedAlmond', 'BlueViolet', 'BurlyWood', 'CadetBlue', 'AntiqueWhite',
'Chocolate', 'Coral', 'CornflowerBlue', 'Cornsilk', 'Crimson', 'Cyan',
'DarkCyan', 'DarkGoldenRod', 'DarkGrey', 'DarkKhaki', 'DarkOrange',
'DarkOrchid', 'DarkSalmon', 'DarkSeaGreen', 'DarkTurquoise', 'DarkViolet',
'DeepPink', 'DeepSkyBlue', 'DodgerBlue', 'FireBrick', 'FloralWhite',
'ForestGreen', 'Fuchsia', 'Gainsboro', 'GhostWhite', 'Gold', 'GoldenRod',
'Salmon', 'Tan', 'HoneyDew', 'HotPink', 'IndianRed', 'Ivory', 'Khaki',
'Lavender', 'LavenderBlush', 'LawnGreen', 'LemonChiffon', 'LightBlue',
'LightCoral', 'LightCyan', 'LightGoldenRodYellow', 'LightGray', 'LightGrey',
'LightGreen', 'LightPink', 'LightSalmon', 'LightSeaGreen', 'LightSkyBlue',
'LightSlateGray', 'LightSlateGrey', 'LightSteelBlue', 'LightYellow', 'Lime',
'LimeGreen', 'Linen', 'Magenta', 'MediumAquaMarine', 'MediumOrchid',
'MediumPurple', 'MediumSeaGreen', 'MediumSlateBlue', 'MediumSpringGreen',
'MediumTurquoise', 'MediumVioletRed', 'MintCream', 'MistyRose', 'Moccasin',
'NavajoWhite', 'OldLace', 'Olive', 'OliveDrab', 'Orange', 'OrangeRed',
'Orchid', 'PaleGoldenRod', 'PaleGreen', 'PaleTurquoise', 'PaleVioletRed',
'PapayaWhip', 'PeachPuff', 'Peru', 'Pink', 'Plum', 'PowderBlue', 'Purple',
'Red', 'RosyBrown', 'RoyalBlue', 'SaddleBrown', 'Green', 'SandyBrown',
'SeaGreen', 'SeaShell', 'Sienna', 'Silver', 'SkyBlue', 'SlateBlue',
'SlateGray', 'SlateGrey', 'Snow', 'SpringGreen', 'SteelBlue', 'GreenYellow',
'Teal', 'Thistle', 'Tomato', 'Turquoise', 'Violet', 'Wheat', 'White',
'WhiteSmoke', 'Yellow', 'YellowGreen'
]
current_path = os.getcwd()
def _visualize_boxes(image, boxes, classes, scores, category_index, **kwargs):
return visualize_boxes_and_labels_on_image_array_tracker(
image, boxes, classes, scores, category_index=category_index, **kwargs)
def _visualize_boxes_and_masks(image, boxes, classes, scores, masks,
category_index, **kwargs):
return visualize_boxes_and_labels_on_image_array_tracker(
image,
boxes,
classes,
scores,
category_index=category_index,
instance_masks=masks,
**kwargs)
def _visualize_boxes_and_keypoints(image, boxes, classes, scores, keypoints,
category_index, **kwargs):
return visualize_boxes_and_labels_on_image_array_tracker(
image,
boxes,
classes,
scores,
category_index=category_index,
keypoints=keypoints,
**kwargs)
def _visualize_boxes_and_masks_and_keypoints(
image, boxes, classes, scores, masks, keypoints, category_index, **kwargs):
return visualize_boxes_and_labels_on_image_array_tracker(
image,
boxes,
classes,
scores,
category_index=category_index,
instance_masks=masks,
keypoints=keypoints,
**kwargs)
def save_image_array_as_png(image, output_path):
"""Saves an image (represented as a numpy array) to PNG.
Args:
image: a numpy array with shape [height, width, 3].
output_path: path to which image should be written.
"""
image_pil = Image.fromarray(np.uint8(image)).convert('RGB')
with tf.gfile.Open(output_path, 'w') as fid:
image_pil.save(fid, 'PNG')
def encode_image_array_as_png_str(image):
"""Encodes a numpy array into a PNG string.
Args:
image: a numpy array with shape [height, width, 3].
Returns:
PNG encoded image string.
"""
image_pil = Image.fromarray(np.uint8(image))
output = six.BytesIO()
image_pil.save(output, format='PNG')
png_string = output.getvalue()
output.close()
return png_string
def draw_bounding_box_on_image_array(current_frame_number, image,
ymin,
xmin,
ymax,
xmax,
color='red',
thickness=4,
display_str_list=(),
use_normalized_coordinates=True):
"""Adds a bounding box to an image (numpy array).
Args:
image: a numpy array with shape [height, width, 3].
ymin: ymin of bounding box in normalized coordinates (same below).
xmin: xmin of bounding box.
ymax: ymax of bounding box.
xmax: xmax of bounding box.
color: color to draw bounding box. Default is red.
thickness: line thickness. Default value is 4.
display_str_list: list of strings to display in box
(each to be shown on its own line).
use_normalized_coordinates: If True (default), treat coordinates
ymin, xmin, ymax, xmax as relative to the image. Otherwise treat
coordinates as absolute.
"""
image_pil = Image.fromarray(np.uint8(image)).convert('RGB')
is_object_detected, csv_line, update_csv = draw_bounding_box_on_image(current_frame_number,image_pil, ymin, xmin, ymax, xmax, color,
thickness, display_str_list,
use_normalized_coordinates)
np.copyto(image, np.array(image_pil))
return is_object_detected, csv_line, update_csv
def draw_bounding_box_on_image(current_frame_number,image,
ymin,
xmin,
ymax,
xmax,
color='red',
thickness=4,
display_str_list=(),
use_normalized_coordinates=True):
"""Adds a bounding box to an image.
Each string in display_str_list is displayed on a separate line above the
bounding box in black text on a rectangle filled with the input 'color'.
If the top of the bounding box extends to the edge of the image, the strings
are displayed below the bounding box.
Args:
image: a PIL.Image object.
ymin: ymin of bounding box.
xmin: xmin of bounding box.
ymax: ymax of bounding box.
xmax: xmax of bounding box.
color: color to draw bounding box. Default is red.
thickness: line thickness. Default value is 4.
display_str_list: list of strings to display in box
(each to be shown on its own line).
use_normalized_coordinates: If True (default), treat coordinates
ymin, xmin, ymax, xmax as relative to the image. Otherwise treat
coordinates as absolute.
"""
image_temp = numpy.array(image)
csv_line = "" # to create new csv line consists of object type, predicted_speed, color and predicted_direction
update_csv = False # update csv for a new object that are passed from ROI - just one new line for each objects
is_object_detected = [0]
draw = ImageDraw.Draw(image)
im_width, im_height = image.size
if use_normalized_coordinates:
(left, right, top, bottom) = (xmin * im_width, xmax * im_width,
ymin * im_height, ymax * im_height)
else:
(left, right, top, bottom) = (xmin, xmax, ymin, ymax)
draw.line([(left, top), (left, bottom), (right, bottom),
(right, top), (left, top)], width=thickness, fill=color)
predicted_direction = "n.a." # means not available, it is just initialization
detected_object_image = image_temp[int(top):int(bottom), int(left):int(right)]
'''if(bottom > roi_position): # if the object get in ROI area, object predicted_speed predicted_color algorithms are called - 200 is an arbitrary value, for my case it looks very well to set position of ROI line at y pixel 200'''
if(x_axis[0] == 1):
predicted_direction, is_object_detected, update_csv = object_counter_x_axis.count_objects_x_axis(top, bottom, right, left, detected_object_image, roi_position[0], roi_position[0]+deviation_value[0], roi_position[0]+(deviation_value[0]*2), deviation_value[0])
elif(y_axis[0] == 1):
predicted_direction, is_object_detected, update_csv = object_counter_y_axis.count_objects(top, bottom, right, left, detected_object_image, roi_position[0], roi_position[0]+deviation_value[0], roi_position[0]+(deviation_value[0]*2), deviation_value[0])
elif(standalone_image[0] == 1):
image_saver.save_image(detected_object_image) # save detected object image
if(is_color_recognition_enable[0]):
predicted_color = color_recognition_api.color_recognition(detected_object_image)
try:
font = ImageFont.truetype('arial.ttf', 16)
except IOError:
font = ImageFont.load_default()
# If the total height of the display strings added to the top of the bounding
# box exceeds the top of the image, stack the strings below the bounding box
# instead of above.
if(is_color_recognition_enable[0]):
display_str_list[0] = predicted_color + " " + display_str_list[0]
csv_line = predicted_color + "," + str (predicted_direction) # csv line created
else:
display_str_list[0] = display_str_list[0]
csv_line = str (predicted_direction) # csv line created
display_str_heights = [font.getsize(ds)[1] for ds in display_str_list]
# Each display_str has a top and bottom margin of 0.05x.
total_display_str_height = (1 + 2 * 0.05) * sum(display_str_heights)
if top > total_display_str_height:
text_bottom = top
else:
text_bottom = bottom + total_display_str_height
# Reverse list and print from bottom to top.
for display_str in display_str_list[::-1]:
text_width, text_height = font.getsize(display_str)
margin = np.ceil(0.05 * text_height)
draw.rectangle(
[(left, text_bottom - text_height - 2 * margin), (left + text_width,
text_bottom)],
fill=color)
draw.text(
(left + margin, text_bottom - text_height - margin),
display_str,
fill='black',
font=font)
text_bottom -= text_height - 2 * margin
return is_object_detected, csv_line, update_csv
def draw_bounding_boxes_on_image_array(image,
boxes,
color='red',
thickness=4,
display_str_list_list=()):
"""Draws bounding boxes on image (numpy array).
Args:
image: a numpy array object.
boxes: a 2 dimensional numpy array of [N, 4]: (ymin, xmin, ymax, xmax).
The coordinates are in normalized format between [0, 1].
color: color to draw bounding box. Default is red.
thickness: line thickness. Default value is 4.
display_str_list_list: list of list of strings.
a list of strings for each bounding box.
The reason to pass a list of strings for a
bounding box is that it might contain
multiple labels.
Raises:
ValueError: if boxes is not a [N, 4] array
"""
image_pil = Image.fromarray(image)
draw_bounding_boxes_on_image(image_pil, boxes, color, thickness, display_str_list_list)
np.copyto(image, np.array(image_pil))
def draw_bounding_boxes_on_image(image,
boxes,
color='red',
thickness=4,
display_str_list_list=()):
"""Draws bounding boxes on image.
Args:
image: a PIL.Image object.
boxes: a 2 dimensional numpy array of [N, 4]: (ymin, xmin, ymax, xmax).
The coordinates are in normalized format between [0, 1].
color: color to draw bounding box. Default is red.
thickness: line thickness. Default value is 4.
display_str_list_list: list of list of strings.
a list of strings for each bounding box.
The reason to pass a list of strings for a
bounding box is that it might contain
multiple labels.
Raises:
ValueError: if boxes is not a [N, 4] array
"""
boxes_shape = boxes.shape
if not boxes_shape:
return
if len(boxes_shape) != 2 or boxes_shape[1] != 4:
raise ValueError('Input must be of size [N, 4]')
for i in range(boxes_shape[0]):
display_str_list = ()
if display_str_list_list:
display_str_list = display_str_list_list[i]
draw_bounding_box_on_image(image, boxes[i, 0], boxes[i, 1], boxes[i, 2],
boxes[i, 3], color, thickness, display_str_list)
def draw_bounding_boxes_on_image_tensors(images,
boxes,
classes,
scores,
category_index,
max_boxes_to_draw=20,
min_score_thresh=0.2):
"""Draws bounding boxes on batch of image tensors.
Args:
images: A 4D uint8 image tensor of shape [N, H, W, C].
boxes: [N, max_detections, 4] float32 tensor of detection boxes.
classes: [N, max_detections] int tensor of detection classes. Note that
classes are 1-indexed.
scores: [N, max_detections] float32 tensor of detection scores.
category_index: a dict that maps integer ids to category dicts. e.g.
{1: {1: 'dog'}, 2: {2: 'cat'}, ...}
max_boxes_to_draw: Maximum number of boxes to draw on an image. Default 20.
min_score_thresh: Minimum score threshold for visualization. Default 0.2.
Returns:
4D image tensor of type uint8, with boxes drawn on top.
"""
visualize_boxes_fn = functools.partial(
visualize_boxes_and_labels_on_image_array,
category_index=category_index,
instance_masks=None,
keypoints=None,
use_normalized_coordinates=True,
max_boxes_to_draw=max_boxes_to_draw,
min_score_thresh=min_score_thresh,
agnostic_mode=False,
line_thickness=4)
def draw_boxes(image_boxes_classes_scores):
"""Draws boxes on image."""
(image, boxes, classes, scores) = image_boxes_classes_scores
image_with_boxes = tf.py_func(visualize_boxes_fn,
[image, boxes, classes, scores], tf.uint8)
return image_with_boxes
images = tf.map_fn(
draw_boxes, (images, boxes, classes, scores),
dtype=tf.uint8,
back_prop=False)
return images
def draw_keypoints_on_image_array(image,
keypoints,
color='red',
radius=2,
use_normalized_coordinates=True):
"""Draws keypoints on an image (numpy array).
Args:
image: a numpy array with shape [height, width, 3].
keypoints: a numpy array with shape [num_keypoints, 2].
color: color to draw the keypoints with. Default is red.
radius: keypoint radius. Default value is 2.
use_normalized_coordinates: if True (default), treat keypoint values as
relative to the image. Otherwise treat them as absolute.
"""
image_pil = Image.fromarray(np.uint8(image)).convert('RGB')
draw_keypoints_on_image(image_pil, keypoints, color, radius,
use_normalized_coordinates)
np.copyto(image, np.array(image_pil))
def draw_keypoints_on_image(image,
keypoints,
color='red',
radius=2,
use_normalized_coordinates=True):
"""Draws keypoints on an image.
Args:
image: a PIL.Image object.
keypoints: a numpy array with shape [num_keypoints, 2].
color: color to draw the keypoints with. Default is red.
radius: keypoint radius. Default value is 2.
use_normalized_coordinates: if True (default), treat keypoint values as
relative to the image. Otherwise treat them as absolute.
"""
draw = ImageDraw.Draw(image)
im_width, im_height = image.size
keypoints_x = [k[1] for k in keypoints]
keypoints_y = [k[0] for k in keypoints]
if use_normalized_coordinates:
keypoints_x = tuple([im_width * x for x in keypoints_x])
keypoints_y = tuple([im_height * y for y in keypoints_y])
for keypoint_x, keypoint_y in zip(keypoints_x, keypoints_y):
draw.ellipse([(keypoint_x - radius, keypoint_y - radius),
(keypoint_x + radius, keypoint_y + radius)],
outline=color, fill=color)
def draw_mask_on_image_array(image, mask, color='red', alpha=0.7):
"""Draws mask on an image.
Args:
image: uint8 numpy array with shape (img_height, img_height, 3)
mask: a uint8 numpy array of shape (img_height, img_height) with
values between either 0 or 1.
color: color to draw the keypoints with. Default is red.
alpha: transparency value between 0 and 1. (default: 0.7)
Raises:
ValueError: On incorrect data type for image or masks.
"""
if image.dtype != np.uint8:
raise ValueError('`image` not of type np.uint8')
if mask.dtype != np.uint8:
raise ValueError('`mask` not of type np.uint8')
if np.any(np.logical_and(mask != 1, mask != 0)):
raise ValueError('`mask` elements should be in [0, 1]')
rgb = ImageColor.getrgb(color)
pil_image = Image.fromarray(image)
solid_color = np.expand_dims(
np.ones_like(mask), axis=2) * np.reshape(list(rgb), [1, 1, 3])
pil_solid_color = Image.fromarray(np.uint8(solid_color)).convert('RGBA')
pil_mask = Image.fromarray(np.uint8(255.0*alpha*mask)).convert('L')
pil_image = Image.composite(pil_solid_color, pil_image, pil_mask)
np.copyto(image, np.array(pil_image.convert('RGB')))
def visualize_boxes_and_labels_on_image_array(current_frame_number,
image,
color_recognition_status,
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=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_value.insert(0,deviation)
is_object_detected = []
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)]
counting_result = ""
# 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(targeted_objects == None):
counting_result = counting_result + str(display_str_list)
elif(display_str_list[0].split(":")[0] in targeted_objects):
counting_result = counting_result + str(display_str_list)
if ((targeted_objects != None) and (display_str_list[0].split(":")[0] in targeted_objects)):
if instance_masks is not None:
draw_mask_on_image_array(image, box_to_instance_masks_map[box], color=color)
is_object_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)
elif (targeted_objects == None):
if instance_masks is not None:
draw_mask_on_image_array(image, box_to_instance_masks_map[box], color=color)
is_object_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_object_detected):
counter = 1
del is_object_detected[:]
is_object_detected = []
csv_line_util = class_name + "," + csv_line
counting_result = counting_result.replace("['", " ").replace("']", " ").replace("%", "")
counting_result = ''.join([i for i in counting_result.replace("['", " ").replace("']", " ").replace("%", "") if not i.isdigit()])
counting_result = str(custom_string_util.word_count(counting_result))
counting_result = counting_result.replace("{", "").replace("}", "")
return counter, csv_line_util, counting_result
def visualize_boxes_and_labels_on_image_array_x_axis(current_frame_number,
image,
color_recognition_status,
boxes,
classes,
scores,
category_index,
targeted_objects=None,
x_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,x_reference)
deviation_value.insert(0,deviation)
x_axis.insert(0,1)
is_object_detected = []
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)]
counting_result = ""
# 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(targeted_objects == None):
counting_result = counting_result + str(display_str_list)
elif(targeted_objects in display_str_list[0]):
counting_result = counting_result + str(display_str_list)
if ((targeted_objects != None) and (targeted_objects in display_str_list[0])):
if instance_masks is not None:
draw_mask_on_image_array(image, box_to_instance_masks_map[box], color=color)
is_object_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)
elif (targeted_objects == None):
if instance_masks is not None:
draw_mask_on_image_array(image, box_to_instance_masks_map[box], color=color)
is_object_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_object_detected):
counter = 1
del is_object_detected[:]
is_object_detected = []
csv_line_util = class_name + "," + csv_line
counting_result = counting_result.replace("['", " ").replace("']", " ").replace("%", "")
counting_result = ''.join([i for i in counting_result.replace("['", " ").replace("']", " ").replace("%", "") if not i.isdigit()])
counting_result = str(custom_string_util.word_count(counting_result))
counting_result = counting_result.replace("{", "").replace("}", "")
return counter, csv_line_util, counting_result
def visualize_boxes_and_labels_on_image_array_y_axis(current_frame_number,
image,
color_recognition_status,
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=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_value.insert(0,deviation)
is_object_detected = []
y_axis.insert(0,1)
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)]
counting_result = ""
# 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(targeted_objects == None):
counting_result = counting_result + str(display_str_list)
elif(display_str_list[0].split(":")[0] in targeted_objects):
counting_result = counting_result + str(display_str_list)
if ((targeted_objects != None) and (display_str_list[0].split(":")[0] in targeted_objects)):
if instance_masks is not None:
draw_mask_on_image_array(image, box_to_instance_masks_map[box], color=color)
is_object_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)
elif (targeted_objects == None):
if instance_masks is not None:
draw_mask_on_image_array(image, box_to_instance_masks_map[box], color=color)
is_object_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_object_detected):
counter = 1
del is_object_detected[:]
is_object_detected = []
csv_line_util = class_name + "," + csv_line
counting_result = counting_result.replace("['", " ").replace("']", " ").replace("%", "")
counting_result = ''.join([i for i in counting_result.replace("['", " ").replace("']", " ").replace("%", "") if not i.isdigit()])
counting_result = str(custom_string_util.word_count(counting_result))
counting_result = counting_result.replace("{", "").replace("}", "")
return counter, csv_line_util, counting_result
def visualize_boxes_and_labels_on_image_array_tracker(
image,
boxes,
classes,
scores,
category_index,
instance_masks=None,
instance_boundaries=None,
keypoints=None,
use_normalized_coordinates=False,
max_boxes_to_draw=20,
min_score_thresh=.5,
agnostic_mode=False,
line_thickness=4,
groundtruth_box_visualization_color='black',
skip_scores=False,
skip_labels=False):