import glob
import math
import os
import random
from sys import platform
import cv2
import numpy as np
import torch
# from torch.utils.data import Dataset
from utils.utils import xyxy2xywh
class load_images(): # for inference
def __init__(self, path, img_size=416):
if os.path.isdir(path):
image_format = ['.jpg', '.jpeg', '.png', '.tif']
self.files = sorted(glob.glob('%s/*.*' % path))
self.files = list(filter(lambda x: os.path.splitext(x)[1].lower() in image_format, self.files))
elif os.path.isfile(path):
self.files = [path]
self.nF = len(self.files) # number of image files
self.height = img_size
assert self.nF > 0, 'No images found in path %s' % path
def __iter__(self):
self.count = -1
return self
def __next__(self):
self.count += 1
if self.count == self.nF:
raise StopIteration
img_path = self.files[self.count]
# Read image
img0 = cv2.imread(img_path) # BGR
assert img0 is not None, 'Failed to load ' + img_path
# Padded resize
img, ratio, padw, padh = letterbox(img0, height=self.height, color=(127.5, 127.5, 127.5))
print(ratio, padw, padh)
# Normalize RGB
img = img[:, :, ::-1].transpose(2, 0, 1)
img = np.ascontiguousarray(img, dtype=np.float32)
img /= 255.0
# cv2.imwrite(img_path + '.letterbox.jpg', 255 * img.transpose((1, 2, 0))[:, :, ::-1]) # save letterbox image
return img_path, img, img0
def __len__(self):
return self.nF # number of files
class load_images_and_labels(): # for training
def __init__(self, path, batch_size=1, img_size=608, multi_scale=False, augment=False):
self.path = path
# self.img_files = sorted(glob.glob('%s/*.*' % path))
with open(path, 'r') as file:
self.img_files = file.readlines()
self.img_files = [path.replace('\n', '') for path in self.img_files]
self.label_files = [path.replace('images', 'labels').replace('.png', '.txt').replace('.jpg', '.txt') for path in
self.img_files]
self.nF = len(self.img_files) # number of image files
self.nB = math.ceil(self.nF / batch_size) # number of batches
self.batch_size = batch_size
self.height = img_size
self.multi_scale = multi_scale
self.augment = augment
assert self.nB > 0, 'No images found in path %s' % path
def __iter__(self):
self.count = -1
self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF)
return self
def __next__(self):
self.count += 1
if self.count == self.nB:
raise StopIteration
ia = self.count * self.batch_size
ib = min((self.count + 1) * self.batch_size, self.nF)
if self.multi_scale:
# Multi-Scale YOLO Training
height = random.choice(range(10, 20)) * 32 # 320 - 608 pixels
else:
# Fixed-Scale YOLO Training
height = self.height
img_all = []
labels_all = []
for index, files_index in enumerate(range(ia, ib)):
img_path = self.img_files[self.shuffled_vector[files_index]]
label_path = self.label_files[self.shuffled_vector[files_index]]
img = cv2.imread(img_path) # BGR
if img is None:
continue
augment_hsv = True
if self.augment and augment_hsv:
# SV augmentation by 50%
fraction = 0.50
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
S = img_hsv[:, :, 1].astype(np.float32)
V = img_hsv[:, :, 2].astype(np.float32)
a = (random.random() * 2 - 1) * fraction + 1
S *= a
if a > 1:
np.clip(S, a_min=0, a_max=255, out=S)
a = (random.random() * 2 - 1) * fraction + 1
V *= a
if a > 1:
np.clip(V, a_min=0, a_max=255, out=V)
img_hsv[:, :, 1] = S.astype(np.uint8)
img_hsv[:, :, 2] = V.astype(np.uint8)
cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img)
h, w, _ = img.shape
img, ratio, padw, padh = letterbox(img, height=height, color=(127.5, 127.5, 127.5))
# Load labels
if os.path.isfile(label_path):
labels0 = np.loadtxt(label_path, dtype=np.float32).reshape(-1, 5)
# Normalized xywh to pixel xyxy format
labels = labels0.copy()
labels[:, 1] = ratio * w * (labels0[:, 1] - labels0[:, 3] / 2) + padw
labels[:, 2] = ratio * h * (labels0[:, 2] - labels0[:, 4] / 2) + padh
labels[:, 3] = ratio * w * (labels0[:, 1] + labels0[:, 3] / 2) + padw
labels[:, 4] = ratio * h * (labels0[:, 2] + labels0[:, 4] / 2) + padh
else:
labels = np.array([])
# Augment image and labels
if self.augment:
img, labels, M = random_affine(img, labels, degrees=(-5, 5), translate=(0.10, 0.10), scale=(0.90, 1.10))
plotFlag = False
if plotFlag:
import matplotlib.pyplot as plt
plt.figure(figsize=(10, 10)) if index == 0 else None
plt.subplot(4, 4, index + 1).imshow(img[:, :, ::-1])
plt.plot(labels[:, [1, 3, 3, 1, 1]].T, labels[:, [2, 2, 4, 4, 2]].T, '.-')
plt.axis('off')
nL = len(labels)
if nL > 0:
# convert xyxy to xywh
labels[:, 1:5] = xyxy2xywh(labels[:, 1:5].copy()) / height
if self.augment:
# random left-right flip
lr_flip = True
if lr_flip & (random.random() > 0.5):
img = np.fliplr(img)
if nL > 0:
labels[:, 1] = 1 - labels[:, 1]
# random up-down flip
ud_flip = False
if ud_flip & (random.random() > 0.5):
img = np.flipud(img)
if nL > 0:
labels[:, 2] = 1 - labels[:, 2]
img_all.append(img)
labels_all.append(torch.from_numpy(labels))
# Normalize
img_all = np.stack(img_all)[:, :, :, ::-1].transpose(0, 3, 1, 2) # BGR to RGB and cv2 to pytorch
img_all = np.ascontiguousarray(img_all, dtype=np.float32)
img_all /= 255.0
return torch.from_numpy(img_all), labels_all
def __len__(self):
return self.nB # number of batches
def letterbox(img, height=416, color=(0, 0, 0)): # resize a rectangular image to a padded square
shape = img.shape[:2] # shape = [height, width]
ratio = float(height) / max(shape) # ratio = old / new
new_shape = [round(shape[0] * ratio), round(shape[1] * ratio)]
dw = height - new_shape[1] # width padding
dh = height - new_shape[0] # height padding
top, bottom = dh // 2, dh - (dh // 2)
left, right = dw // 2, dw - (dw // 2)
img = cv2.resize(img, (new_shape[1], new_shape[0]), interpolation=cv2.INTER_AREA) # resized, no border
return cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color), ratio, dw // 2, dh // 2
def letterbox_undo(img, height=416, color=(0, 0, 0)): # resize a rectangular image to a padded square
shape = img.shape[:2] # shape = [height, width]
ratio = float(height) / max(shape) # ratio = old / new
new_shape = [round(shape[0] * ratio), round(shape[1] * ratio)]
dw = height - new_shape[1] # width padding
dh = height - new_shape[0] # height padding
top, bottom = dh // 2, dh - (dh // 2)
left, right = dw // 2, dw - (dw // 2)
img = cv2.resize(img, (new_shape[1], new_shape[0]), interpolation=cv2.INTER_AREA) # resized, no border
return cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color), ratio, dw // 2, dh // 2
def random_affine(img, targets=None, degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-2, 2),
borderValue=(127.5, 127.5, 127.5)):
# torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
# https://medium.com/uruvideo/dataset-augmentation-with-random-homographies-a8f4b44830d4
border = 0 # width of added border (optional)
height = max(img.shape[0], img.shape[1]) + border * 2
# Rotation and Scale
R = np.eye(3)
a = random.random() * (degrees[1] - degrees[0]) + degrees[0]
# a += random.choice([-180, -90, 0, 90]) # 90deg rotations added to small rotations
s = random.random() * (scale[1] - scale[0]) + scale[0]
R[:2] = cv2.getRotationMatrix2D(angle=a, center=(img.shape[1] / 2, img.shape[0] / 2), scale=s)
# Translation
T = np.eye(3)
T[0, 2] = (random.random() * 2 - 1) * translate[0] * img.shape[0] + border # x translation (pixels)
T[1, 2] = (random.random() * 2 - 1) * translate[1] * img.shape[1] + border # y translation (pixels)
# Shear
S = np.eye(3)
S[0, 1] = math.tan((random.random() * (shear[1] - shear[0]) + shear[0]) * math.pi / 180) # x shear (deg)
S[1, 0] = math.tan((random.random() * (shear[1] - shear[0]) + shear[0]) * math.pi / 180) # y shear (deg)
M = S @ T @ R # Combined rotation matrix. ORDER IS IMPORTANT HERE!!
imw = cv2.warpPerspective(img, M, dsize=(height, height), flags=cv2.INTER_LINEAR,
borderValue=borderValue) # BGR order borderValue
# Return warped points also
if targets is not None:
if len(targets) > 0:
n = targets.shape[0]
points = targets[:, 1:5].copy()
area0 = (points[:, 2] - points[:, 0]) * (points[:, 3] - points[:, 1])
# warp points
xy = np.ones((n * 4, 3))
xy[:, :2] = points[:, [0, 1, 2, 3, 0, 3, 2, 1]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1
xy = (xy @ M.T)[:, :2].reshape(n, 8)
# create new boxes
x = xy[:, [0, 2, 4, 6]]
y = xy[:, [1, 3, 5, 7]]
xy = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
# apply angle-based reduction
radians = a * math.pi / 180
reduction = max(abs(math.sin(radians)), abs(math.cos(radians))) ** 0.5
x = (xy[:, 2] + xy[:, 0]) / 2
y = (xy[:, 3] + xy[:, 1]) / 2
w = (xy[:, 2] - xy[:, 0]) * reduction
h = (xy[:, 3] - xy[:, 1]) * reduction
xy = np.concatenate((x - w / 2, y - h / 2, x + w / 2, y + h / 2)).reshape(4, n).T
# reject warped points outside of image
np.clip(xy, 0, height, out=xy)
w = xy[:, 2] - xy[:, 0]
h = xy[:, 3] - xy[:, 1]
area = w * h
ar = np.maximum(w / (h + 1e-16), h / (w + 1e-16))
i = (w > 4) & (h > 4) & (area / (area0 + 1e-16) > 0.1) & (ar < 10)
targets = targets[i]
targets[:, 1:5] = xy[i]
return imw, targets, M
else:
return imw
def convert_tif2bmp(p='../xview/val_images_bmp'):
import glob
import cv2
files = sorted(glob.glob('%s/*.tif' % p))
for i, f in enumerate(files):
print('%g/%g' % (i + 1, len(files)))
cv2.imwrite(f.replace('.tif', '.bmp'), cv2.imread(f))
os.system('rm -rf ' + f)