363 lines
13 KiB
Python
363 lines
13 KiB
Python
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import glob
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import math
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import os
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import random
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import time
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from collections import OrderedDict
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import cv2
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import numpy as np
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import torch
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from torch.utils.data import Dataset
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from utils.utils import xyxy2xywh
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class LoadImages: # for inference
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def __init__(self, path, img_size=(1088, 608)):
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if os.path.isdir(path):
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image_format = ['.jpg', '.jpeg', '.png', '.tif']
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self.files = sorted(glob.glob('%s/*.*' % path))
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self.files = list(filter(lambda x: os.path.splitext(x)[1].lower() in image_format, self.files))
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elif os.path.isfile(path):
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self.files = [path]
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self.nF = len(self.files) # number of image files
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self.width = img_size[0]
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self.height = img_size[1]
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self.count = 0
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assert self.nF > 0, 'No images found in ' + path
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def __iter__(self):
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self.count = -1
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return self
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def __next__(self):
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self.count += 1
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if self.count == self.nF:
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raise StopIteration
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img_path = self.files[self.count]
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# Read image
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img0 = cv2.imread(img_path) # BGR
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assert img0 is not None, 'Failed to load ' + img_path
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# Padded resize
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img, _, _, _ = letterbox(img0, height=self.height, width=self.width)
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# Normalize RGB
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img = img[:, :, ::-1].transpose(2, 0, 1)
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img = np.ascontiguousarray(img, dtype=np.float32)
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img /= 255.0
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# cv2.imwrite(img_path + '.letterbox.jpg', 255 * img.transpose((1, 2, 0))[:, :, ::-1]) # save letterbox image
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return img_path, img, img0
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def __getitem__(self, idx):
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idx = idx % self.nF
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img_path = self.files[idx]
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# Read image
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img0 = cv2.imread(img_path) # BGR
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assert img0 is not None, 'Failed to load ' + img_path
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# Padded resize
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img, _, _, _ = letterbox(img0, height=self.height, width=self.width)
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# Normalize RGB
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img = img[:, :, ::-1].transpose(2, 0, 1)
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img = np.ascontiguousarray(img, dtype=np.float32)
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img /= 255.0
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return img_path, img, img0
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def __len__(self):
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return self.nF # number of files
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class LoadImagesAndLabels: # for training
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def __init__(self, path, img_size=(1088,608), augment=False, transforms=None):
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with open(path, 'r') as file:
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self.img_files = file.readlines()
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self.img_files = [x.replace('\n', '') for x in self.img_files]
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self.img_files = list(filter(lambda x: len(x) > 0, self.img_files))
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self.label_files = [x.replace('images', 'labels_with_ids').replace('.png', '.txt').replace('.jpg', '.txt')
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for x in self.img_files]
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self.nF = len(self.img_files) # number of image files
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self.width = img_size[0]
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self.height = img_size[1]
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self.augment = augment
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self.transforms = transforms
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def __getitem__(self, files_index):
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img_path = self.img_files[files_index]
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label_path = self.label_files[files_index]
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return self.get_data(img_path, label_path)
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def get_data(self, img_path, label_path):
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height = self.height
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width = self.width
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img = cv2.imread(img_path) # BGR
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if img is None:
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raise ValueError('File corrupt {}'.format(img_path))
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augment_hsv = True
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if self.augment and augment_hsv:
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# SV augmentation by 50%
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fraction = 0.50
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img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
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S = img_hsv[:, :, 1].astype(np.float32)
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V = img_hsv[:, :, 2].astype(np.float32)
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a = (random.random() * 2 - 1) * fraction + 1
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S *= a
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if a > 1:
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np.clip(S, a_min=0, a_max=255, out=S)
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a = (random.random() * 2 - 1) * fraction + 1
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V *= a
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if a > 1:
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np.clip(V, a_min=0, a_max=255, out=V)
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img_hsv[:, :, 1] = S.astype(np.uint8)
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img_hsv[:, :, 2] = V.astype(np.uint8)
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cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img)
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h, w, _ = img.shape
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img, ratio, padw, padh = letterbox(img, height=height, width=width)
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# Load labels
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if os.path.isfile(label_path):
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labels0 = np.loadtxt(label_path, dtype=np.float32).reshape(-1, 6)
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# Normalized xywh to pixel xyxy format
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labels = labels0.copy()
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labels[:, 2] = ratio * w * (labels0[:, 2] - labels0[:, 4] / 2) + padw
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labels[:, 3] = ratio * h * (labels0[:, 3] - labels0[:, 5] / 2) + padh
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labels[:, 4] = ratio * w * (labels0[:, 2] + labels0[:, 4] / 2) + padw
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labels[:, 5] = ratio * h * (labels0[:, 3] + labels0[:, 5] / 2) + padh
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else:
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labels = np.array([])
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# Augment image and labels
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if self.augment:
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img, labels, M = random_affine(img, labels, degrees=(-5, 5), translate=(0.10, 0.10), scale=(0.50, 1.20))
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plotFlag = False
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if plotFlag:
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import matplotlib
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matplotlib.use('Agg')
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import matplotlib.pyplot as plt
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plt.figure(figsize=(50, 50))
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plt.imshow(img[:, :, ::-1])
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plt.plot(labels[:, [1, 3, 3, 1, 1]].T, labels[:, [2, 2, 4, 4, 2]].T, '.-')
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plt.axis('off')
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plt.savefig('test.jpg')
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time.sleep(10)
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nL = len(labels)
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if nL > 0:
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# convert xyxy to xywh
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labels[:, 2:6] = xyxy2xywh(labels[:, 2:6].copy()) #/ height
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labels[:, 2] /= width
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labels[:, 3] /= height
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labels[:, 4] /= width
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labels[:, 5] /= height
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if self.augment:
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# random left-right flip
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lr_flip = True
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if lr_flip & (random.random() > 0.5):
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img = np.fliplr(img)
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if nL > 0:
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labels[:, 2] = 1 - labels[:, 2]
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img = np.ascontiguousarray(img[ :, :, ::-1]) # BGR to RGB
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if self.transforms is not None:
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img = self.transforms(img)
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return img, labels, img_path, (h, w)
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def __len__(self):
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return self.nF # number of batches
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def letterbox(img, height=608, width=1088, color=(127.5, 127.5, 127.5)): # resize a rectangular image to a padded rectangular
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shape = img.shape[:2] # shape = [height, width]
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ratio = min(float(height)/shape[0], float(width)/shape[1])
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new_shape = (round(shape[1] * ratio), round(shape[0] * ratio)) # new_shape = [width, height]
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dw = (width - new_shape[0]) / 2 # width padding
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dh = (height - new_shape[1]) / 2 # height padding
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top, bottom = round(dh - 0.1), round(dh + 0.1)
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left, right = round(dw - 0.1), round(dw + 0.1)
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img = cv2.resize(img, new_shape, interpolation=cv2.INTER_AREA) # resized, no border
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img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # padded rectangular
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return img, ratio, dw, dh
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def random_affine(img, targets=None, degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-2, 2),
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borderValue=(127.5, 127.5, 127.5)):
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# torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
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# https://medium.com/uruvideo/dataset-augmentation-with-random-homographies-a8f4b44830d4
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border = 0 # width of added border (optional)
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height = img.shape[0]
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width = img.shape[1]
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# Rotation and Scale
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R = np.eye(3)
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a = random.random() * (degrees[1] - degrees[0]) + degrees[0]
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# a += random.choice([-180, -90, 0, 90]) # 90deg rotations added to small rotations
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s = random.random() * (scale[1] - scale[0]) + scale[0]
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R[:2] = cv2.getRotationMatrix2D(angle=a, center=(img.shape[1] / 2, img.shape[0] / 2), scale=s)
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# Translation
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T = np.eye(3)
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T[0, 2] = (random.random() * 2 - 1) * translate[0] * img.shape[0] + border # x translation (pixels)
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T[1, 2] = (random.random() * 2 - 1) * translate[1] * img.shape[1] + border # y translation (pixels)
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# Shear
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S = np.eye(3)
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S[0, 1] = math.tan((random.random() * (shear[1] - shear[0]) + shear[0]) * math.pi / 180) # x shear (deg)
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S[1, 0] = math.tan((random.random() * (shear[1] - shear[0]) + shear[0]) * math.pi / 180) # y shear (deg)
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M = S @ T @ R # Combined rotation matrix. ORDER IS IMPORTANT HERE!!
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imw = cv2.warpPerspective(img, M, dsize=(width, height), flags=cv2.INTER_LINEAR,
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borderValue=borderValue) # BGR order borderValue
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# Return warped points also
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if targets is not None:
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if len(targets) > 0:
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n = targets.shape[0]
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points = targets[:, 2:6].copy()
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area0 = (points[:, 2] - points[:, 0]) * (points[:, 3] - points[:, 1])
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# warp points
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xy = np.ones((n * 4, 3))
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xy[:, :2] = points[:, [0, 1, 2, 3, 0, 3, 2, 1]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1
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xy = (xy @ M.T)[:, :2].reshape(n, 8)
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# create new boxes
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x = xy[:, [0, 2, 4, 6]]
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y = xy[:, [1, 3, 5, 7]]
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xy = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
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# apply angle-based reduction
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radians = a * math.pi / 180
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reduction = max(abs(math.sin(radians)), abs(math.cos(radians))) ** 0.5
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x = (xy[:, 2] + xy[:, 0]) / 2
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y = (xy[:, 3] + xy[:, 1]) / 2
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w = (xy[:, 2] - xy[:, 0]) * reduction
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h = (xy[:, 3] - xy[:, 1]) * reduction
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xy = np.concatenate((x - w / 2, y - h / 2, x + w / 2, y + h / 2)).reshape(4, n).T
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# reject warped points outside of image
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np.clip(xy[:, 0], 0, width, out=xy[:, 0])
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np.clip(xy[:, 2], 0, width, out=xy[:, 2])
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np.clip(xy[:, 1], 0, height, out=xy[:, 1])
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np.clip(xy[:, 3], 0, height, out=xy[:, 3])
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w = xy[:, 2] - xy[:, 0]
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h = xy[:, 3] - xy[:, 1]
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area = w * h
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ar = np.maximum(w / (h + 1e-16), h / (w + 1e-16))
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i = (w > 4) & (h > 4) & (area / (area0 + 1e-16) > 0.1) & (ar < 10)
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targets = targets[i]
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targets[:, 2:6] = xy[i]
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return imw, targets, M
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else:
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return imw
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def collate_fn(batch):
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imgs, labels, paths, sizes = zip(*batch)
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batch_size = len(labels)
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imgs = torch.stack(imgs, 0)
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max_box_len = max([l.shape[0] for l in labels])
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labels = [torch.from_numpy(l) for l in labels]
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filled_labels = torch.zeros(batch_size, max_box_len, 6)
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labels_len = torch.zeros(batch_size)
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for i in range(batch_size):
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isize = labels[i].shape[0]
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if len(labels[i])>0:
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filled_labels[i, :isize, :] = labels[i]
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labels_len[i] = isize
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return imgs, filled_labels, paths, sizes, labels_len.unsqueeze(1)
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class JointDataset(LoadImagesAndLabels): # for training
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def __init__(self, paths, img_size=(1088,608), augment=False, transforms=None):
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dataset_names = paths.keys()
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self.img_files = OrderedDict()
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self.label_files = OrderedDict()
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self.tid_num = OrderedDict()
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self.tid_start_index = OrderedDict()
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for ds, path in paths.items():
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with open(path, 'r') as file:
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self.img_files[ds] = file.readlines()
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self.img_files[ds] = [x.strip() for x in self.img_files[ds]]
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self.img_files[ds] = list(filter(lambda x: len(x) > 0, self.img_files[ds]))
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self.label_files[ds] = [x.replace('images', 'labels_with_ids').replace('.png', '.txt').replace('.jpg', '.txt')
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for x in self.img_files[ds]]
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for ds, label_paths in self.label_files.items():
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max_index = -1
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for lp in label_paths:
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lb = np.loadtxt(lp)
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if len(lb) < 1:
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continue
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if len(lb.shape) < 2:
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img_max = lb[1]
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else:
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img_max = np.max(lb[:,1])
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if img_max >max_index:
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max_index = img_max
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self.tid_num[ds] = max_index + 1
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last_index = 0
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for i, (k, v) in enumerate(self.tid_num.items()):
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self.tid_start_index[k] = last_index
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last_index += v
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|
||
|
|
self.nID = int(last_index+1)
|
||
|
|
self.nds = [len(x) for x in self.img_files.values()]
|
||
|
|
self.cds = [sum(self.nds[:i]) for i in range(len(self.nds))]
|
||
|
|
self.nF = sum(self.nds)
|
||
|
|
self.width = img_size[0]
|
||
|
|
self.height = img_size[1]
|
||
|
|
self.augment = augment
|
||
|
|
self.transforms = transforms
|
||
|
|
|
||
|
|
print('='*80)
|
||
|
|
print('dataset summary')
|
||
|
|
print(self.tid_num)
|
||
|
|
print('total # identities:', self.nID)
|
||
|
|
print('start index')
|
||
|
|
print(self.tid_start_index)
|
||
|
|
print('='*80)
|
||
|
|
|
||
|
|
|
||
|
|
def __getitem__(self, files_index):
|
||
|
|
|
||
|
|
for i, c in enumerate(self.cds):
|
||
|
|
if files_index >= c:
|
||
|
|
ds = list(self.label_files.keys())[i]
|
||
|
|
start_index = c
|
||
|
|
|
||
|
|
img_path = self.img_files[ds][files_index - start_index]
|
||
|
|
label_path = self.label_files[ds][files_index - start_index]
|
||
|
|
|
||
|
|
imgs, labels, img_path, (h, w) = self.get_data(img_path, label_path)
|
||
|
|
for i, _ in enumerate(labels):
|
||
|
|
if labels[i,1] > -1:
|
||
|
|
labels[i,1] += self.tid_start_index[ds]
|
||
|
|
|
||
|
|
return imgs, labels, img_path, (h, w)
|
||
|
|
|
||
|
|
|