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06c9f97 | 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 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 | # Copyright (C) 2022-2025, Pyronear.
# This program is licensed under the Apache License 2.0.
# See LICENSE or go to <https://opensource.org/licenses/Apache-2.0> for full license details.
import cv2
import numpy as np
from tqdm import tqdm
__all__ = ["DownloadProgressBar", "letterbox", "nms", "xywh2xyxy"]
def xywh2xyxy(x: np.ndarray):
y = np.copy(x)
y[..., 0] = x[..., 0] - x[..., 2] / 2 # top left x
y[..., 1] = x[..., 1] - x[..., 3] / 2 # top left y
y[..., 2] = x[..., 0] + x[..., 2] / 2 # bottom right x
y[..., 3] = x[..., 1] + x[..., 3] / 2 # bottom right y
return y
def letterbox(
im: np.ndarray,
new_shape: tuple = (1024, 1024),
color: tuple = (114, 114, 114),
auto: bool = False,
stride: int = 32,
):
"""Letterbox image transform for yolo models
Args:
im (np.ndarray): Input image
new_shape (tuple, optional): Image size. Defaults to (1024, 1024).
color (tuple, optional): Pixel fill value for the area outside the transformed image.
Defaults to (114, 114, 114).
auto (bool, optional): auto padding. Defaults to False.
stride (int, optional): padding stride. Defaults to 32.
Returns:
np.ndarray: Output image
"""
# Resize and pad image while meeting stride-multiple constraints
im = np.array(im)
shape = im.shape[:2] # current shape [height, width]
if isinstance(new_shape, int):
new_shape = (new_shape, new_shape)
# Scale ratio (new / old)
r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
# Compute padding
new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
if auto: # minimum rectangle
dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding
dw /= 2 # divide padding into 2 sides
dh /= 2
if shape[::-1] != new_unpad: # resize
im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
# add border
h, w = im.shape[:2]
im_b = np.zeros((h + top + bottom, w + left + right, 3)) + color
im_b[top : top + h, left : left + w, :] = im
return im_b.astype("uint8"), (left, top)
def box_iou(box1: np.ndarray, box2: np.ndarray, eps: float = 1e-7):
"""
Calculate intersection-over-union (IoU) of boxes.
Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
Based on https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
Args:
box1 (np.ndarray): A numpy array of shape (N, 4) representing N bounding boxes.
box2 (np.ndarray): A numpy array of shape (M, 4) representing M bounding boxes.
eps (float, optional): A small value to avoid division by zero. Defaults to 1e-7.
Returns:
(np.ndarray): An NxM numpy array containing the pairwise IoU values for every element in box1 and box2.
"""
(a1, a2), (b1, b2) = np.split(box1, 2, 1), np.split(box2, 2, 1)
inter = (np.minimum(a2, b2[:, None, :]) - np.maximum(a1, b1[:, None, :])).clip(0).prod(2)
# IoU = inter / (area1 + area2 - inter)
return inter / ((a2 - a1).prod(1) + (b2 - b1).prod(1)[:, None] - inter + eps)
def nms(boxes: np.ndarray, overlapThresh: int = 0):
"""Non maximum suppression
Args:
boxes (np.ndarray): A numpy array of shape (N, 4) representing N bounding boxes in (x1, y1, x2, y2, conf) format
overlapThresh (int, optional): iou threshold. Defaults to 0.
Returns:
boxes: Boxes after NMS
"""
# Return an empty list, if no boxes given
boxes = boxes[boxes[:, -1].argsort()]
if len(boxes) == 0:
return []
indices = np.arange(len(boxes))
rr = box_iou(boxes[:, :4], boxes[:, :4])
for i, box in enumerate(boxes):
temp_indices = indices[indices != i]
if np.any(rr[i, temp_indices] > overlapThresh):
indices = indices[indices != i]
return boxes[indices]
class DownloadProgressBar(tqdm):
def update_to(self, b=1, bsize=1, tsize=None):
if tsize is not None:
self.total = tsize
self.update(b * bsize - self.n)
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