103 lines
3.3 KiB
Python
103 lines
3.3 KiB
Python
|
"""visualization.py
|
||
|
|
|
||
|
|
The BBoxVisualization class implements drawing of nice looking
|
||
|
|
bounding boxes based on object detection results.
|
||
|
|
"""
|
||
|
|
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
import cv2
|
||
|
|
|
||
|
|
|
||
|
|
# Constants
|
||
|
|
ALPHA = 0.5
|
||
|
|
FONT = cv2.FONT_HERSHEY_PLAIN
|
||
|
|
TEXT_SCALE = 1.0
|
||
|
|
TEXT_THICKNESS = 1
|
||
|
|
BLACK = (0, 0, 0)
|
||
|
|
WHITE = (255, 255, 255)
|
||
|
|
|
||
|
|
|
||
|
|
def gen_colors(num_colors):
|
||
|
|
"""Generate different colors.
|
||
|
|
|
||
|
|
# Arguments
|
||
|
|
num_colors: total number of colors/classes.
|
||
|
|
|
||
|
|
# Output
|
||
|
|
bgrs: a list of (B, G, R) tuples which correspond to each of
|
||
|
|
the colors/classes.
|
||
|
|
"""
|
||
|
|
import random
|
||
|
|
import colorsys
|
||
|
|
|
||
|
|
hsvs = [[float(x) / num_colors, 1., 0.7] for x in range(num_colors)]
|
||
|
|
random.seed(1234)
|
||
|
|
random.shuffle(hsvs)
|
||
|
|
rgbs = list(map(lambda x: list(colorsys.hsv_to_rgb(*x)), hsvs))
|
||
|
|
bgrs = [(int(rgb[2] * 255), int(rgb[1] * 255), int(rgb[0] * 255))
|
||
|
|
for rgb in rgbs]
|
||
|
|
return bgrs
|
||
|
|
|
||
|
|
|
||
|
|
def draw_boxed_text(img, text, topleft, color):
|
||
|
|
"""Draw a transluent boxed text in white, overlayed on top of a
|
||
|
|
colored patch surrounded by a black border. FONT, TEXT_SCALE,
|
||
|
|
TEXT_THICKNESS and ALPHA values are constants (fixed) as defined
|
||
|
|
on top.
|
||
|
|
|
||
|
|
# Arguments
|
||
|
|
img: the input image as a numpy array.
|
||
|
|
text: the text to be drawn.
|
||
|
|
topleft: XY coordinate of the topleft corner of the boxed text.
|
||
|
|
color: color of the patch, i.e. background of the text.
|
||
|
|
|
||
|
|
# Output
|
||
|
|
img: note the original image is modified inplace.
|
||
|
|
"""
|
||
|
|
assert img.dtype == np.uint8
|
||
|
|
img_h, img_w, _ = img.shape
|
||
|
|
if topleft[0] >= img_w or topleft[1] >= img_h:
|
||
|
|
return img
|
||
|
|
margin = 3
|
||
|
|
size = cv2.getTextSize(text, FONT, TEXT_SCALE, TEXT_THICKNESS)
|
||
|
|
w = size[0][0] + margin * 2
|
||
|
|
h = size[0][1] + margin * 2
|
||
|
|
# the patch is used to draw boxed text
|
||
|
|
patch = np.zeros((h, w, 3), dtype=np.uint8)
|
||
|
|
patch[...] = color
|
||
|
|
cv2.putText(patch, text, (margin+1, h-margin-2), FONT, TEXT_SCALE,
|
||
|
|
WHITE, thickness=TEXT_THICKNESS, lineType=cv2.LINE_8)
|
||
|
|
cv2.rectangle(patch, (0, 0), (w-1, h-1), BLACK, thickness=1)
|
||
|
|
w = min(w, img_w - topleft[0]) # clip overlay at image boundary
|
||
|
|
h = min(h, img_h - topleft[1])
|
||
|
|
# Overlay the boxed text onto region of interest (roi) in img
|
||
|
|
roi = img[topleft[1]:topleft[1]+h, topleft[0]:topleft[0]+w, :]
|
||
|
|
cv2.addWeighted(patch[0:h, 0:w, :], ALPHA, roi, 1 - ALPHA, 0, roi)
|
||
|
|
return img
|
||
|
|
|
||
|
|
|
||
|
|
class BBoxVisualization():
|
||
|
|
"""BBoxVisualization class implements nice drawing of boudning boxes.
|
||
|
|
|
||
|
|
# Arguments
|
||
|
|
cls_dict: a dictionary used to translate class id to its name.
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self, cls_dict):
|
||
|
|
self.cls_dict = cls_dict
|
||
|
|
self.colors = gen_colors(len(cls_dict))
|
||
|
|
|
||
|
|
def draw_bboxes(self, img, boxes, confs, clss):
|
||
|
|
"""Draw detected bounding boxes on the original image."""
|
||
|
|
for bb, cf, cl in zip(boxes, confs, clss):
|
||
|
|
cl = int(cl)
|
||
|
|
x_min, y_min, x_max, y_max = bb[0], bb[1], bb[2], bb[3]
|
||
|
|
color = self.colors[cl]
|
||
|
|
cv2.rectangle(img, (x_min, y_min), (x_max, y_max), color, 2)
|
||
|
|
txt_loc = (max(x_min+2, 0), max(y_min+2, 0))
|
||
|
|
cls_name = self.cls_dict.get(cl, 'CLS{}'.format(cl))
|
||
|
|
txt = '{} {:.2f}'.format(cls_name, cf)
|
||
|
|
img = draw_boxed_text(img, txt, txt_loc, color)
|
||
|
|
return img
|