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import numpy as np
import cv2
import random
from typing import List
def hex2bgr(hex):
gmask = 254 << 8
rmask = 254
b = hex >> 16
g = (hex & gmask) >> 8
r = hex & rmask
return np.stack([b, g, r]).transpose()
def union_area(bboxa, bboxb):
x1 = max(bboxa[0], bboxb[0])
y1 = max(bboxa[1], bboxb[1])
x2 = min(bboxa[2], bboxb[2])
y2 = min(bboxa[3], bboxb[3])
if y2 < y1 or x2 < x1:
return -1
return (y2 - y1) * (x2 - x1)
def get_yololabel_strings(clslist, labellist):
content = ''
for cls, xywh in zip(clslist, labellist):
content += str(int(cls)) + ' ' + ' '.join([str(e) for e in xywh]) + '\n'
if len(content) != 0:
content = content[:-1]
return content
# 4 points bbox to 8 points polygon
def xywh2xyxypoly(xywh, to_int=True):
xyxypoly = np.tile(xywh[:, [0, 1]], 4)
xyxypoly[:, [2, 4]] += xywh[:, [2]]
xyxypoly[:, [5, 7]] += xywh[:, [3]]
if to_int:
xyxypoly = xyxypoly.astype(np.int64)
return xyxypoly
def xyxy2yolo(xyxy, w: int, h: int):
if xyxy == [] or xyxy == np.array([]) or len(xyxy) == 0:
return None
if isinstance(xyxy, list):
xyxy = np.array(xyxy)
if len(xyxy.shape) == 1:
xyxy = np.array([xyxy])
yolo = np.copy(xyxy).astype(np.float64)
yolo[:, [0, 2]] = yolo[:, [0, 2]] / w
yolo[:, [1, 3]] = yolo[:, [1, 3]] / h
yolo[:, [2, 3]] -= yolo[:, [0, 1]]
yolo[:, [0, 1]] += yolo[:, [2, 3]] / 2
return yolo
def yolo_xywh2xyxy(xywh: np.array, w: int, h: int, to_int=True):
if xywh is None:
return None
if len(xywh) == 0:
return None
if len(xywh.shape) == 1:
xywh = np.array([xywh])
xywh[:, [0, 2]] *= w
xywh[:, [1, 3]] *= h
xywh[:, [0, 1]] -= xywh[:, [2, 3]] / 2
xywh[:, [2, 3]] += xywh[:, [0, 1]]
if to_int:
xywh = xywh.astype(np.int64)
return xywh
def letterbox(im, new_shape=(640, 640), color=(0, 0, 0), auto=False, scaleFill=False, scaleup=True, stride=128):
# Resize and pad image while meeting stride-multiple constraints
shape = im.shape[:2] # current shape [height, width]
if not isinstance(new_shape, tuple):
new_shape = (new_shape, new_shape)
# Scale ratio (new / old)
r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
if not scaleup: # only scale down, do not scale up (for better val mAP)
r = min(r, 1.0)
# Compute padding
ratio = r, r # width, height ratios
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
elif scaleFill: # stretch
dw, dh = 0.0, 0.0
new_unpad = (new_shape[1], new_shape[0])
ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios
# dw /= 2 # divide padding into 2 sides
# dh /= 2
dh, dw = int(dh), int(dw)
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))
im = cv2.copyMakeBorder(im, 0, dh, 0, dw, cv2.BORDER_CONSTANT, value=color) # add border
return im, ratio, (dw, dh)
def resize_keepasp(im, new_shape=640, scaleup=True, interpolation=cv2.INTER_LINEAR, stride=None):
shape = im.shape[:2] # current shape [height, width]
if new_shape is not None:
if not isinstance(new_shape, tuple):
new_shape = (new_shape, new_shape)
else:
new_shape = shape
# Scale ratio (new / old)
r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
if not scaleup: # only scale down, do not scale up (for better val mAP)
r = min(r, 1.0)
new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
if stride is not None:
h, w = new_unpad
if new_shape[0] % stride != 0:
new_h = (stride - (new_shape[0] % stride)) + h
else:
new_h = h
if w % stride != 0:
new_w = (stride - (w % stride)) + w
else:
new_w = w
new_unpad = (new_h, new_w)
if shape[::-1] != new_unpad: # resize
im = cv2.resize(im, new_unpad, interpolation=interpolation)
return im
def enlarge_window(rect, im_w, im_h, ratio=2.5, aspect_ratio=1.0) -> List:
assert ratio > 1.0
x1, y1, x2, y2 = rect
w = x2 - x1
h = y2 - y1
# https://numpy.org/doc/stable/reference/generated/numpy.roots.html
coeff = [aspect_ratio, w+h*aspect_ratio, (1-ratio)*w*h]
roots = np.roots(coeff)
roots.sort()
delta = int(round(roots[-1] / 2 ))
delta_w = int(delta * aspect_ratio)
delta_w = min(x1, im_w - x2, delta_w)
delta = min(y1, im_h - y2, delta)
rect = np.array([x1-delta_w, y1-delta, x2+delta_w, y2+delta], dtype=np.int64)
return rect.tolist()
def draw_connected_labels(num_labels, labels, stats, centroids, names="draw_connected_labels", skip_background=True):
labdraw = np.zeros((labels.shape[0], labels.shape[1], 3), dtype=np.uint8)
max_ind = 0
if isinstance(num_labels, int):
num_labels = range(num_labels)
# for ind, lab in enumerate((range(num_labels))):
for lab in num_labels:
if skip_background and lab == 0:
continue
randcolor = (random.randint(0,255), random.randint(0,255), random.randint(0,255))
labdraw[np.where(labels==lab)] = randcolor
maxr, minr = 0.5, 0.001
maxw, maxh = stats[max_ind][2] * maxr, stats[max_ind][3] * maxr
minarea = labdraw.shape[0] * labdraw.shape[1] * minr
stat = stats[lab]
bboxarea = stat[2] * stat[3]
if stat[2] < maxw and stat[3] < maxh and bboxarea > minarea:
pix = np.zeros((labels.shape[0], labels.shape[1]), dtype=np.uint8)
pix[np.where(labels==lab)] = 255
rect = cv2.minAreaRect(cv2.findNonZero(pix))
box = np.int0(cv2.boxPoints(rect))
labdraw = cv2.drawContours(labdraw, [box], 0, randcolor, 2)
labdraw = cv2.circle(labdraw, (int(centroids[lab][0]),int(centroids[lab][1])), radius=5, color=(random.randint(0,255), random.randint(0,255), random.randint(0,255)), thickness=-1)
cv2.imshow(names, labdraw)
return labdraw
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