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#!/usr/bin/env python3
# Copyright (c) Megvii Inc. All rights reserved.
import os
import random
import cv2
import numpy as np
__all__ = [
"mkdir", "nms", "multiclass_nms", "demo_postprocess", "random_color", "visualize_assign"
]
def random_color():
return random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)
def visualize_assign(img, boxes, coords, match_results, save_name=None) -> np.ndarray:
"""visualize label assign result.
Args:
img: img to visualize
boxes: gt boxes in xyxy format
coords: coords of matched anchors
match_results: match results of each gt box and coord.
save_name: name of save image, if None, image will not be saved. Default: None.
"""
for box_id, box in enumerate(boxes):
x1, y1, x2, y2 = box
color = random_color()
assign_coords = coords[match_results == box_id]
if assign_coords.numel() == 0:
# unmatched boxes are red
color = (0, 0, 255)
cv2.putText(
img, "unmatched", (int(x1), int(y1) - 5),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 1
)
else:
for coord in assign_coords:
# draw assigned anchor
cv2.circle(img, (int(coord[0]), int(coord[1])), 3, color, -1)
cv2.rectangle(img, (int(x1), int(y1)), (int(x2), int(y2)), color, 2)
if save_name is not None:
cv2.imwrite(save_name, img)
return img
def mkdir(path):
if not os.path.exists(path):
os.makedirs(path)
def nms(boxes, scores, nms_thr):
"""Single class NMS implemented in Numpy."""
x1 = boxes[:, 0]
y1 = boxes[:, 1]
x2 = boxes[:, 2]
y2 = boxes[:, 3]
areas = (x2 - x1 + 1) * (y2 - y1 + 1)
order = scores.argsort()[::-1]
keep = []
while order.size > 0:
i = order[0]
keep.append(i)
xx1 = np.maximum(x1[i], x1[order[1:]])
yy1 = np.maximum(y1[i], y1[order[1:]])
xx2 = np.minimum(x2[i], x2[order[1:]])
yy2 = np.minimum(y2[i], y2[order[1:]])
w = np.maximum(0.0, xx2 - xx1 + 1)
h = np.maximum(0.0, yy2 - yy1 + 1)
inter = w * h
ovr = inter / (areas[i] + areas[order[1:]] - inter)
inds = np.where(ovr <= nms_thr)[0]
order = order[inds + 1]
return keep
def multiclass_nms(boxes, scores, nms_thr, score_thr, class_agnostic=True):
"""Multiclass NMS implemented in Numpy"""
if class_agnostic:
nms_method = multiclass_nms_class_agnostic
else:
nms_method = multiclass_nms_class_aware
return nms_method(boxes, scores, nms_thr, score_thr)
def multiclass_nms_class_aware(boxes, scores, nms_thr, score_thr):
"""Multiclass NMS implemented in Numpy. Class-aware version."""
final_dets = []
num_classes = scores.shape[1]
for cls_ind in range(num_classes):
cls_scores = scores[:, cls_ind]
valid_score_mask = cls_scores > score_thr
if valid_score_mask.sum() == 0:
continue
else:
valid_scores = cls_scores[valid_score_mask]
valid_boxes = boxes[valid_score_mask]
keep = nms(valid_boxes, valid_scores, nms_thr)
if len(keep) > 0:
cls_inds = np.ones((len(keep), 1)) * cls_ind
dets = np.concatenate(
[valid_boxes[keep], valid_scores[keep, None], cls_inds], 1
)
final_dets.append(dets)
if len(final_dets) == 0:
return None
return np.concatenate(final_dets, 0)
def multiclass_nms_class_agnostic(boxes, scores, nms_thr, score_thr):
"""Multiclass NMS implemented in Numpy. Class-agnostic version."""
cls_inds = scores.argmax(1)
cls_scores = scores[np.arange(len(cls_inds)), cls_inds]
valid_score_mask = cls_scores > score_thr
if valid_score_mask.sum() == 0:
return None
valid_scores = cls_scores[valid_score_mask]
valid_boxes = boxes[valid_score_mask]
valid_cls_inds = cls_inds[valid_score_mask]
keep = nms(valid_boxes, valid_scores, nms_thr)
if keep:
dets = np.concatenate(
[valid_boxes[keep], valid_scores[keep, None], valid_cls_inds[keep, None]], 1
)
return dets
def demo_postprocess(outputs, img_size, p6=False):
grids = []
expanded_strides = []
strides = [8, 16, 32] if not p6 else [8, 16, 32, 64]
hsizes = [img_size[0] // stride for stride in strides]
wsizes = [img_size[1] // stride for stride in strides]
for hsize, wsize, stride in zip(hsizes, wsizes, strides):
xv, yv = np.meshgrid(np.arange(wsize), np.arange(hsize))
grid = np.stack((xv, yv), 2).reshape(1, -1, 2)
grids.append(grid)
shape = grid.shape[:2]
expanded_strides.append(np.full((*shape, 1), stride))
grids = np.concatenate(grids, 1)
expanded_strides = np.concatenate(expanded_strides, 1)
outputs[..., :2] = (outputs[..., :2] + grids) * expanded_strides
outputs[..., 2:4] = np.exp(outputs[..., 2:4]) * expanded_strides
return outputs
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