294 lines
11 KiB
Python
Executable File
294 lines
11 KiB
Python
Executable File
import glob
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import math
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import os
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import random
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from sys import platform
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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 ImageFolder(): # for eval-only
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def __init__(self, path, batch_size=1, img_size=416):
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if os.path.isdir(path):
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self.files = sorted(glob.glob('%s/*.*' % path))
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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.nB = math.ceil(self.nF / batch_size) # number of batches
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self.batch_size = batch_size
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self.height = img_size
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assert self.nF > 0, 'No images found in path %s' % path
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# RGB normalization values
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# self.rgb_mean = np.array([60.134, 49.697, 40.746], dtype=np.float32).reshape((3, 1, 1))
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# self.rgb_std = np.array([29.99, 24.498, 22.046], dtype=np.float32).reshape((3, 1, 1))
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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.nB:
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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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img = cv2.imread(img_path) # BGR
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# Padded resize
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img, _, _, _ = resize_square(img, height=self.height, color=(127.5, 127.5, 127.5))
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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 -= self.rgb_mean
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# img /= self.rgb_std
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img /= 255.0
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return [img_path], img
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def __len__(self):
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return self.nB # number of batches
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class ListDataset(): # for training
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def __init__(self, path, batch_size=1, img_size=608):
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self.path = path
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# self.img_files = sorted(glob.glob('%s/*.*' % path))
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with open(path, 'r') as file:
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self.img_files = file.readlines()
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if platform == 'darwin': # macos
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self.img_files = [path.replace('\n', '').replace('/images', '/Users/glennjocher/Downloads/DATA/coco/images')
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for path in self.img_files]
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else: # linux (gcp cloud)
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self.img_files = [path.replace('\n', '').replace('/images', '../coco/images') for path in self.img_files]
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self.label_files = [path.replace('images', 'labels').replace('.png', '.txt').replace('.jpg', '.txt') for path in
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self.img_files]
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self.nF = len(self.img_files) # number of image files
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self.nB = math.ceil(self.nF / batch_size) # number of batches
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self.batch_size = batch_size
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# assert self.nB > 0, 'No images found in path %s' % path
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self.height = img_size
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# RGB normalization values
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# self.rgb_mean = np.array([60.134, 49.697, 40.746], dtype=np.float32).reshape((1, 3, 1, 1))
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# self.rgb_std = np.array([29.99, 24.498, 22.046], dtype=np.float32).reshape((1, 3, 1, 1))
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def __iter__(self):
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self.count = -1
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self.shuffled_vector = np.random.permutation(self.nF) # shuffled vector
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# self.shuffled_vector = np.arange(self.nF) # not shuffled
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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.nB:
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raise StopIteration
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ia = self.count * self.batch_size
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ib = min((self.count + 1) * self.batch_size, self.nF)
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height = self.height
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img_all = []
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labels_all = []
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for index, files_index in enumerate(range(ia, ib)):
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img_path = self.img_files[self.shuffled_vector[files_index]]
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label_path = self.label_files[self.shuffled_vector[files_index]]
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img = cv2.imread(img_path) # BGR
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if img is None:
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continue
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augment_hsv = True
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if 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 = resize_square(img, height=height, color=(127.5, 127.5, 127.5))
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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, 5)
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# Normalized xywh to pixel xyxy format
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labels = labels0.copy()
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labels[:, 1] = ratio * w * (labels0[:, 1] - labels0[:, 3] / 2) + padw
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labels[:, 2] = ratio * h * (labels0[:, 2] - labels0[:, 4] / 2) + padh
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labels[:, 3] = ratio * w * (labels0[:, 1] + labels0[:, 3] / 2) + padw
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labels[:, 4] = ratio * h * (labels0[:, 2] + labels0[:, 4] / 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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img, labels, M = random_affine(img, targets=labels, degrees=(-5, 5), translate=(0.2, 0.2),
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scale=(0.8, 1.2)) # RGB
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plotFlag = False
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if plotFlag:
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import matplotlib.pyplot as plt
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plt.figure(figsize=(10, 10)) if index == 0 else None
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plt.subplot(4, 4, index + 1).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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nL = len(labels)
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if nL > 0:
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# convert xyxy to xywh
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labels[:, 1:5] = xyxy2xywh(labels[:, 1:5].copy()) / height
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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[:, 1] = 1 - labels[:, 1]
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# random up-down flip
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ud_flip = False
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if ud_flip & (random.random() > 0.5):
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img = np.flipud(img)
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if nL > 0:
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labels[:, 2] = 1 - labels[:, 2]
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img_all.append(img)
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labels_all.append(torch.from_numpy(labels))
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# Normalize
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img_all = np.stack(img_all)[:, :, :, ::-1].transpose(0, 3, 1, 2) # BGR to RGB and cv2 to pytorch
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img_all = np.ascontiguousarray(img_all, dtype=np.float32)
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# img_all -= self.rgb_mean
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# img_all /= self.rgb_std
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img_all /= 255.0
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return torch.from_numpy(img_all), labels_all
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def __len__(self):
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return self.nB # number of batches
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def resize_square(img, height=416, color=(0, 0, 0)): # resize a rectangular image to a padded square
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shape = img.shape[:2] # shape = [height, width]
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ratio = float(height) / max(shape)
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new_shape = [round(shape[0] * ratio), round(shape[1] * ratio)]
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dw = height - new_shape[1] # width padding
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dh = height - new_shape[0] # height padding
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top, bottom = dh // 2, dh - (dh // 2)
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left, right = dw // 2, dw - (dw // 2)
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img = cv2.resize(img, (new_shape[1], new_shape[0]), interpolation=cv2.INTER_AREA)
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return cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color), ratio, dw // 2, dh // 2
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def random_affine(img, targets=None, degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-3, 3),
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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 = max(img.shape[0], img.shape[1]) + border * 2
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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]) # random 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 # ORDER IS IMPORTANT HERE!!
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imw = cv2.warpPerspective(img, M, dsize=(height, height), flags=cv2.INTER_LINEAR,
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borderValue=borderValue) # BGR order (YUV-equalized BGR means)
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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[:, 1:5].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, height, out=xy)
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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[:, 1:5] = 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 convert_tif2bmp(p='/Users/glennjocher/Downloads/DATA/xview/val_images_bmp'):
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import glob
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import cv2
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files = sorted(glob.glob('%s/*.tif' % p))
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for i, f in enumerate(files):
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print('%g/%g' % (i + 1, len(files)))
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img = cv2.imread(f)
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cv2.imwrite(f.replace('.tif', '.bmp'), img)
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os.system('rm -rf ' + f)
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