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8655cd6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | import cv2
from PIL import Image
import numpy as np
def add_bounding_boxes(img, pred_cls, boxes, pred_score=None, thresh=0.35, rect_th=2, text_size=0.5, text_th=1,
color_box=(1, 1, 1), return_pil=True):
"""
Returns the image with boxes and labels as PIL
:param thresh:
:param img: The image
:param pred_cls: Array of classes (as numbers)
:param pred_score: array of probabilities
:param boxes: Array of boxes
:param rect_th: thickness of the rectangle
:param text_size: Text size
:param text_th: thickness of the text
:param color_box: Box and text color
:param return_pil: Boolean indicate whether to return a pil
:return: Returns the image with boxes and labels as PIL
"""
# turn classification tensors to numpy
pred_cls = pred_cls.numpy()
if pred_score is not None:
pred_score = pred_score.numpy()
for i in range(len(boxes)):
if pred_score is not None and pred_score[i] < thresh:
continue
cv2.rectangle(img, (int(boxes[i][0]), int(boxes[i][1])),
(int(boxes[i][2]),
int(boxes[i][3])),
color=color_box, thickness=rect_th)
print_text = f"fish{pred_cls[i]}"
print_text = print_text if pred_score is None else print_text + ":{}".format(str(round(pred_score[i],3)))
cv2.putText(img, print_text, (int(boxes[i][0] + rect_th), int(boxes[i][1])+20),
cv2.FONT_HERSHEY_SIMPLEX, text_size, color_box, thickness=text_th)
# turn to PIL
if return_pil:
return Image.fromarray((img * 255).astype(np.uint8))
return img
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