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{
"cells": [
{
"cell_type": "heading",
"metadata": {
"collapsed": false
},
"level": 1,
"source": [
"The Notebook for Defect Detection Image Augumentation"
]
},
{
"cell_type": "heading",
"metadata": {},
"level": 2,
"source": [
"Path"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dataPath = \".Data_postiveOnly\""
]
},
{
"cell_type": "heading",
"metadata": {},
"level": 2,
"source": [
"Import"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import imgaug as ia\n",
"from imgaug import augmenters as iaa\n",
"import skimage.io\n",
"import errno\n",
"import numpy as np\n",
"import skimage.color as color\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import os\n",
"from skimage.color import gray2rgb"
]
},
{
"cell_type": "heading",
"metadata": {},
"level": 2,
"source": [
"Helper Functions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def bboxSetupInImage(datapath,txtFile,img):\n",
" \"\"\"\n",
" This is the function that reads in the bounding box files and then using imgaug to set up the bounding box on images\n",
" \n",
" :param txtFile: the txt file that store bounding box information \n",
" :param img: the image file variable to represent the img to be plotted bounding box on it \n",
" :return bbs: the image with bounding box in it\n",
" \"\"\"\n",
" with open( datapath + 'bounding_boxes/' + txtFile,'r') as f:\n",
" content = [line.rstrip('\\n') for line in f]\n",
" iaBBoxList = []\n",
" for bbline in content:\n",
" bbox = bbline.strip().split()\n",
" #print(bbox[1])\n",
" if len(bbox) == 5:\n",
" iaBBoxList.append(ia.BoundingBox(\n",
" x1=float(bbox[2]), \n",
" y1=float(bbox[1]), \n",
" x2=float(bbox[4]), \n",
" y2=float(bbox[3]),\n",
" label = bbox[0]))\n",
" bbs = ia.BoundingBoxesOnImage(iaBBoxList, shape=img.shape)\n",
" return bbs"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"def saveAugbbox2TXT(txtFile,bbs):\n",
" \"\"\"\n",
" This is the function that save the augmented bounding box files into ChainerCV bbox format\n",
" \n",
" :param txtFile: the txt file that want to save\n",
" :param bbs: bounding box lists \n",
" \"\"\"\n",
" with open('' + txtFile,'w') as f:\n",
" for i in range(len(bbs.bounding_boxes)):\n",
" bb = bbs_aug.bounding_boxes[i]\n",
" # print(\"%s %.2f %.2f %.2f %.2f\"%(bb.label,bb.y1,bb.x1,bb.y2,bb.x2))\n",
" f.write(\"%s %.2f %.2f %.2f %.2f\\n\"%(bb.label,bb.y1,bb.x1,bb.y2,bb.x2))"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"def getImageList(imageTXT):\n",
" \"\"\"\n",
" Function to loop the testing images for test\n",
" :param imageTXT: the txt that stores the \n",
" :return: imageFileList: the list contains all the original test image list\n",
" \"\"\"\n",
" imageFileList = list()\n",
" with open(imageTXT,'r') as f:\n",
" lines = f.readlines()\n",
" for line in lines:\n",
" imageFileList.append(line.strip())\n",
" return imageFileList"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"def createFolder(folderName):\n",
" \"\"\"\n",
" Safely create folder when needed\n",
" \n",
" :param folderName : the directory that you want to safely create\n",
" :return: None\n",
" \"\"\"\n",
" if not os.path.exists(folderName):\n",
" try:\n",
" os.makedirs(folderName)\n",
" except OSError as exc: # Guard against race condition\n",
" if exc.errno != errno.EEXIST:\n",
" raise"
]
},
{
"cell_type": "heading",
"metadata": {},
"level": 2,
"source": [
"Gaussian Noise"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": true
},
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"source": [
"\n",
"##################################################\n",
"# 1. Define data augmentation operations\n",
"##################################################\n",
"\n",
"trainImageTxtFile = dataPath + \"trainimages.txt\"\n",
"imageList = getImageList(trainImageTxtFile)\n",
"\n",
"current_operation = \"GaussianNoise\"\n",
"\n",
"# Add gaussian noise.\n",
"# For 50% of all images, we sample the noise once per pixel.\n",
"# For the other 50% of all images, we sample the noise per pixel AND\n",
"# channel. This can change the color (not only brightness) of the\n",
"# pixels.\n",
" \n",
"from imgaug import augmenters as iaa\n",
"ia.seed(1)\n",
"seq = iaa.Sequential([\n",
" iaa.AdditiveGaussianNoise(loc=0, \n",
" scale=(0.0, 0.05*255), \n",
" per_channel=0.5)\n",
"])\n",
"\n",
"# seq = iaa.Sequential([ \n",
"# # Adjust contrast by scaling each pixel value to (I_ij/255.0)**gamma.\n",
"# # Values in the range gamma=(0.5, 2.0) seem to be sensible.\n",
"# iaa.GammaContrast((0.5, 1.5))\n",
"# ])\n",
"\n",
"# Make our sequence deterministic.\n",
"# We can now apply it to the image and then to the BBs and it will\n",
"# lead to the same augmentations.\n",
"# IMPORTANT: Call this once PER BATCH, otherwise you will always get the exactly same augmentations for every batch!\n",
"seq_det = seq.to_deterministic()\n",
"\n",
"##################################################\n",
"# 2. loop through images\n",
"##################################################\n",
"\n",
"for img in imageList:\n",
" print(img)\n",
" # Grayscale images must have shape (height, width, 1) each.\n",
" #print(os.listdir(dataPath+'images/'))\n",
" currentimage = skimage.io.imread(dataPath+'images/'+img).astype(np.uint8)\n",
" # gray2rgb() simply duplicates the gray values over the three color channels.\n",
" currentimage = color.gray2rgb(currentimage)\n",
" bbs = bboxSetupInImage(dataPath , img.rstrip('.jpg') + '.txt',currentimage)\n",
" # Augment BBs and images.\n",
" # As we only have one image and list of BBs, we use\n",
" # [image] and [bbs] to turn both into lists (batches) for the# functions and then [0] to reverse that. In a real experiment, your\n",
" # variables would likely already be lists.\n",
" image_aug = seq_det.augment_images([currentimage])[0]\n",
" bbs_aug = seq_det.augment_bounding_boxes([bbs])[0]\n",
" augImgFolder = current_operation + \"Images\"\n",
" augTxTFolder = current_operation + \"TXT\"\n",
" createFolder(augImgFolder)\n",
" createFolder(augTxTFolder)\n",
" # Save aug images and bboxes\n",
" skimage.io.imsave(augImgFolder + '/'+\n",
" img.rstrip('.jpg') + \n",
" '_' + current_operation +\n",
" '.jpg'\n",
" ,image_aug)\n",
" saveAugbbox2TXT(augTxTFolder+ '/'+ \n",
" img.rstrip('.jpg') + \n",
" '_'+ current_operation +\n",
" '.txt',bbs_aug)\n",
" # image with BBs before/after augmentation (shown below)\n",
" # image_before = bbs.draw_on_image(currentimage, thickness=2)\n",
" # image_after = bbs_aug.draw_on_image(image_aug, \n",
" # thickness=2, color=[0, 0, 255])\n",
" # image with BBs before/after augmentation (shown below)\n",
" # plot and save figures before and after data augmentations\n",
" #skimage.io.imshow(image_before)\n",
" #skimage.io.imshow(image_after)\n",
" # for i in range(len(bbs.bounding_boxes)):\n",
" # before = bbs.bounding_boxes[i]\n",
" # after = bbs_aug.bounding_boxes[i]\n",
" # print(\"BB %d: (%.4f, %.4f, %.4f, %.4f) -> (%.4f, %.4f, %.4f, %.4f)\" % (\n",
" # i,\n",
" # before.x1, before.y1, before.x2, before.y2,\n",
" # after.x1, after.y1, after.x2, after.y2)\n",
" # )"
]
},
{
"cell_type": "heading",
"metadata": {},
"level": 1,
"source": [
"GaussianBlur"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
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],
"source": [
"##################################################\n",
"# 1. Define data augmentation operations\n",
"##################################################\n",
"\n",
"trainImageTxtFile = dataPath + \"trainimages.txt\"\n",
"imageList = getImageList(trainImageTxtFile)\n",
"\n",
"current_operation = \"GaussianBlur\"\n",
"\n",
"# blur images with a sigma of 0 to 3.0 \n",
"from imgaug import augmenters as iaa\n",
"ia.seed(1)\n",
"seq = iaa.Sequential([\n",
" iaa.GaussianBlur(sigma=(0, 3)) \n",
"])\n",
"\n",
"# seq = iaa.Sequential([ \n",
"# # Adjust contrast by scaling each pixel value to (I_ij/255.0)**gamma.\n",
"# # Values in the range gamma=(0.5, 2.0) seem to be sensible.\n",
"# iaa.GammaContrast((0.5, 1.5))\n",
"# ])\n",
"\n",
"# Make our sequence deterministic.\n",
"# We can now apply it to the image and then to the BBs and it will\n",
"# lead to the same augmentations.\n",
"# IMPORTANT: Call this once PER BATCH, otherwise you will always get the exactly same augmentations for every batch!\n",
"seq_det = seq.to_deterministic()\n",
"\n",
"##################################################\n",
"# 2. loop through images\n",
"##################################################\n",
"\n",
"for img in imageList:\n",
" print(img)\n",
" # Grayscale images must have shape (height, width, 1) each.\n",
" #print(os.listdir(dataPath+'images/'))\n",
" currentimage = skimage.io.imread(dataPath+'images/'+img).astype(np.uint8)\n",
" # gray2rgb() simply duplicates the gray values over the three color channels.\n",
" currentimage = color.gray2rgb(currentimage)\n",
" bbs = bboxSetupInImage(dataPath , img.rstrip('.jpg') + '.txt',currentimage)\n",
" # Augment BBs and images.\n",
" # As we only have one image and list of BBs, we use\n",
" # [image] and [bbs] to turn both into lists (batches) for the# functions and then [0] to reverse that. In a real experiment, your\n",
" # variables would likely already be lists.\n",
" image_aug = seq_det.augment_images([currentimage])[0]\n",
" bbs_aug = seq_det.augment_bounding_boxes([bbs])[0]\n",
" augImgFolder = current_operation + \"Images\"\n",
" augTxTFolder = current_operation + \"TXT\"\n",
" createFolder(augImgFolder)\n",
" createFolder(augTxTFolder)\n",
" # Save aug images and bboxes\n",
" skimage.io.imsave(augImgFolder + '/'+\n",
" img.rstrip('.jpg') + \n",
" '_' + current_operation +\n",
" '.jpg'\n",
" ,image_aug)\n",
" saveAugbbox2TXT(augTxTFolder+ '/'+ \n",
" img.rstrip('.jpg') + \n",
" '_'+ current_operation +\n",
" '.txt',bbs_aug)\n",
" # image with BBs before/after augmentation (shown below)\n",
" # image_before = bbs.draw_on_image(currentimage, thickness=2)\n",
" # image_after = bbs_aug.draw_on_image(image_aug, \n",
" # thickness=2, color=[0, 0, 255])\n",
" # image with BBs before/after augmentation (shown below)\n",
" # plot and save figures before and after data augmentations\n",
" #skimage.io.imshow(image_before)\n",
" #skimage.io.imshow(image_after)\n",
" # for i in range(len(bbs.bounding_boxes)):\n",
" # before = bbs.bounding_boxes[i]\n",
" # after = bbs_aug.bounding_boxes[i]\n",
" # print(\"BB %d: (%.4f, %.4f, %.4f, %.4f) -> (%.4f, %.4f, %.4f, %.4f)\" % (\n",
" # i,\n",
" # before.x1, before.y1, before.x2, before.y2,\n",
" # after.x1, after.y1, after.x2, after.y2)\n",
" # )"
]
},
{
"cell_type": "heading",
"metadata": {},
"level": 2,
"source": [
"change brightness"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
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],
"source": [
"##################################################\n",
"# 1. Define data augmentation operations\n",
"##################################################\n",
"\n",
"trainImageTxtFile = dataPath + \"trainimages.txt\"\n",
"imageList = getImageList(trainImageTxtFile)\n",
"\n",
"current_operation = \"Brightness\"\n",
"\n",
"# Strengthen or weaken the contrast in each image. \n",
"from imgaug import augmenters as iaa\n",
"ia.seed(1)\n",
"seq = iaa.Sequential([\n",
" iaa.Multiply((1.2, 1.5))\n",
"])\n",
"\n",
"# seq = iaa.Sequential([ \n",
"# # Adjust contrast by scaling each pixel value to (I_ij/255.0)**gamma.\n",
"# # Values in the range gamma=(0.5, 2.0) seem to be sensible.\n",
"# iaa.GammaContrast((0.5, 1.5))\n",
"# ])\n",
"\n",
"# Make our sequence deterministic.\n",
"# We can now apply it to the image and then to the BBs and it will\n",
"# lead to the same augmentations.\n",
"# IMPORTANT: Call this once PER BATCH, otherwise you will always get the exactly same augmentations for every batch!\n",
"seq_det = seq.to_deterministic()\n",
"\n",
"##################################################\n",
"# 2. loop through images\n",
"##################################################\n",
"\n",
"for img in imageList:\n",
" print(img)\n",
" # Grayscale images must have shape (height, width, 1) each.\n",
" #print(os.listdir(dataPath+'images/'))\n",
" currentimage = skimage.io.imread(dataPath+'images/'+img).astype(np.uint8)\n",
" # gray2rgb() simply duplicates the gray values over the three color channels.\n",
" currentimage = color.gray2rgb(currentimage)\n",
" bbs = bboxSetupInImage(dataPath , img.rstrip('.jpg') + '.txt',currentimage)\n",
" # Augment BBs and images.\n",
" # As we only have one image and list of BBs, we use\n",
" # [image] and [bbs] to turn both into lists (batches) for the# functions and then [0] to reverse that. In a real experiment, your\n",
" # variables would likely already be lists.\n",
" image_aug = seq_det.augment_images([currentimage])[0]\n",
" bbs_aug = seq_det.augment_bounding_boxes([bbs])[0]\n",
" augImgFolder = current_operation + \"Images\"\n",
" augTxTFolder = current_operation + \"TXT\"\n",
" createFolder(augImgFolder)\n",
" createFolder(augTxTFolder)\n",
" # Save aug images and bboxes\n",
" skimage.io.imsave(augImgFolder + '/'+\n",
" img.rstrip('.jpg') + \n",
" '_' + current_operation +\n",
" '.jpg'\n",
" ,image_aug)\n",
" saveAugbbox2TXT(augTxTFolder+ '/'+ \n",
" img.rstrip('.jpg') + \n",
" '_'+ current_operation +\n",
" '.txt',bbs_aug)\n",
" # image with BBs before/after augmentation (shown below)\n",
" # image_before = bbs.draw_on_image(currentimage, thickness=2)\n",
" # image_after = bbs_aug.draw_on_image(image_aug, \n",
" # thickness=2, color=[0, 0, 255])\n",
" # image with BBs before/after augmentation (shown below)\n",
" # plot and save figures before and after data augmentations\n",
" #skimage.io.imshow(image_before)\n",
" #skimage.io.imshow(image_after)\n",
" # for i in range(len(bbs.bounding_boxes)):\n",
" # before = bbs.bounding_boxes[i]\n",
" # after = bbs_aug.bounding_boxes[i]\n",
" # print(\"BB %d: (%.4f, %.4f, %.4f, %.4f) -> (%.4f, %.4f, %.4f, %.4f)\" % (\n",
" # i,\n",
" # before.x1, before.y1, before.x2, before.y2,\n",
" # after.x1, after.y1, after.x2, after.y2)\n",
" # )"
]
},
{
"cell_type": "heading",
"metadata": {},
"level": 2,
"source": [
"Contrast Normalization"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
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],
"source": [
"##################################################\n",
"# 1. Define data augmentation operations\n",
"##################################################\n",
"\n",
"trainImageTxtFile = dataPath + \"trainimages.txt\"\n",
"imageList = getImageList(trainImageTxtFile)\n",
"\n",
"current_operation = \"ContrastNormalization\"\n",
"\n",
"# Improve or worsen the contrast of images.\n",
"\n",
"from imgaug import augmenters as iaa\n",
"ia.seed(1)\n",
"seq = iaa.Sequential([\n",
" iaa.ContrastNormalization((0.8, 1.5), per_channel=True)\n",
"])\n",
"\n",
"# seq = iaa.Sequential([ \n",
"# # Adjust contrast by scaling each pixel value to (I_ij/255.0)**gamma.\n",
"# # Values in the range gamma=(0.5, 2.0) seem to be sensible.\n",
"# iaa.GammaContrast((0.5, 1.5))\n",
"# ])\n",
"\n",
"# Make our sequence deterministic.\n",
"# We can now apply it to the image and then to the BBs and it will\n",
"# lead to the same augmentations.\n",
"# IMPORTANT: Call this once PER BATCH, otherwise you will always get the exactly same augmentations for every batch!\n",
"seq_det = seq.to_deterministic()\n",
"\n",
"##################################################\n",
"# 2. loop through images\n",
"##################################################\n",
"\n",
"for img in imageList:\n",
" print(img)\n",
" # Grayscale images must have shape (height, width, 1) each.\n",
" #print(os.listdir(dataPath+'images/'))\n",
" currentimage = skimage.io.imread(dataPath+'images/'+img).astype(np.uint8)\n",
" # gray2rgb() simply duplicates the gray values over the three color channels.\n",
" currentimage = color.gray2rgb(currentimage)\n",
" bbs = bboxSetupInImage(dataPath , img.rstrip('.jpg') + '.txt',currentimage)\n",
" # Augment BBs and images.\n",
" # As we only have one image and list of BBs, we use\n",
" # [image] and [bbs] to turn both into lists (batches) for the# functions and then [0] to reverse that. In a real experiment, your\n",
" # variables would likely already be lists.\n",
" image_aug = seq_det.augment_images([currentimage])[0]\n",
" bbs_aug = seq_det.augment_bounding_boxes([bbs])[0]\n",
" augImgFolder = current_operation + \"Images\"\n",
" augTxTFolder = current_operation + \"TXT\"\n",
" createFolder(augImgFolder)\n",
" createFolder(augTxTFolder)\n",
" # Save aug images and bboxes\n",
" skimage.io.imsave(augImgFolder + '/'+\n",
" img.rstrip('.jpg') + \n",
" '_' + current_operation +\n",
" '.jpg'\n",
" ,image_aug)\n",
" saveAugbbox2TXT(augTxTFolder+ '/'+ \n",
" img.rstrip('.jpg') + \n",
" '_'+ current_operation +\n",
" '.txt',bbs_aug)\n",
" # image with BBs before/after augmentation (shown below)\n",
" # image_before = bbs.draw_on_image(currentimage, thickness=2)\n",
" # image_after = bbs_aug.draw_on_image(image_aug, \n",
" # thickness=2, color=[0, 0, 255])\n",
" # image with BBs before/after augmentation (shown below)\n",
" # plot and save figures before and after data augmentations\n",
" #skimage.io.imshow(image_before)\n",
" #skimage.io.imshow(image_after)\n",
" # for i in range(len(bbs.bounding_boxes)):\n",
" # before = bbs.bounding_boxes[i]\n",
" # after = bbs_aug.bounding_boxes[i]\n",
" # print(\"BB %d: (%.4f, %.4f, %.4f, %.4f) -> (%.4f, %.4f, %.4f, %.4f)\" % (\n",
" # i,\n",
" # before.x1, before.y1, before.x2, before.y2,\n",
" # after.x1, after.y1, after.x2, after.y2)\n",
" # )"
]
},
{
"cell_type": "heading",
"metadata": {},
"level": 2,
"source": [
"Flip left-right"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
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"source": [
"##################################################\n",
"# 1. Define data augmentation operations\n",
"##################################################\n",
"\n",
"trainImageTxtFile = dataPath + \"trainimages.txt\"\n",
"imageList = getImageList(trainImageTxtFile)\n",
"\n",
"current_operation = \"Fliplr\"\n",
"\n",
"# Flip/mirror input images horizontally.\n",
" \n",
"from imgaug import augmenters as iaa\n",
"ia.seed(1)\n",
"seq = iaa.Sequential([\n",
" iaa.Fliplr(1.0)\n",
"])\n",
"\n",
"# seq = iaa.Sequential([ \n",
"# # Adjust contrast by scaling each pixel value to (I_ij/255.0)**gamma.\n",
"# # Values in the range gamma=(0.5, 2.0) seem to be sensible.\n",
"# iaa.GammaContrast((0.5, 1.5))\n",
"# ])\n",
"\n",
"# Make our sequence deterministic.\n",
"# We can now apply it to the image and then to the BBs and it will\n",
"# lead to the same augmentations.\n",
"# IMPORTANT: Call this once PER BATCH, otherwise you will always get the exactly same augmentations for every batch!\n",
"seq_det = seq.to_deterministic()\n",
"\n",
"##################################################\n",
"# 2. loop through images\n",
"##################################################\n",
"\n",
"for img in imageList:\n",
" print(img)\n",
" # Grayscale images must have shape (height, width, 1) each.\n",
" #print(os.listdir(dataPath+'images/'))\n",
" currentimage = skimage.io.imread(dataPath+'images/'+img).astype(np.uint8)\n",
" # gray2rgb() simply duplicates the gray values over the three color channels.\n",
" currentimage = color.gray2rgb(currentimage)\n",
" bbs = bboxSetupInImage(dataPath , img.rstrip('.jpg') + '.txt',currentimage)\n",
" # Augment BBs and images.\n",
" # As we only have one image and list of BBs, we use\n",
" # [image] and [bbs] to turn both into lists (batches) for the# functions and then [0] to reverse that. In a real experiment, your\n",
" # variables would likely already be lists.\n",
" image_aug = seq_det.augment_images([currentimage])[0]\n",
" bbs_aug = seq_det.augment_bounding_boxes([bbs])[0]\n",
" augImgFolder = current_operation + \"Images\"\n",
" augTxTFolder = current_operation + \"TXT\"\n",
" createFolder(augImgFolder)\n",
" createFolder(augTxTFolder)\n",
" # Save aug images and bboxes\n",
" skimage.io.imsave(augImgFolder + '/'+\n",
" img.rstrip('.jpg') + \n",
" '_' + current_operation +\n",
" '.jpg'\n",
" ,image_aug)\n",
" saveAugbbox2TXT(augTxTFolder+ '/'+ \n",
" img.rstrip('.jpg') + \n",
" '_'+ current_operation +\n",
" '.txt',bbs_aug)\n",
" # image with BBs before/after augmentation (shown below)\n",
" # image_before = bbs.draw_on_image(currentimage, thickness=2)\n",
" # image_after = bbs_aug.draw_on_image(image_aug, \n",
" # thickness=2, color=[0, 0, 255])\n",
" # image with BBs before/after augmentation (shown below)\n",
" # plot and save figures before and after data augmentations\n",
" #skimage.io.imshow(image_before)\n",
" #skimage.io.imshow(image_after)\n",
" # for i in range(len(bbs.bounding_boxes)):\n",
" # before = bbs.bounding_boxes[i]\n",
" # after = bbs_aug.bounding_boxes[i]\n",
" # print(\"BB %d: (%.4f, %.4f, %.4f, %.4f) -> (%.4f, %.4f, %.4f, %.4f)\" % (\n",
" # i,\n",
" # before.x1, before.y1, before.x2, before.y2,\n",
" # after.x1, after.y1, after.x2, after.y2)\n",
" # )"
]
},
{
"cell_type": "heading",
"metadata": {},
"level": 2,
"source": [
"Flip up-down"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
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],
"source": [
"##################################################\n",
"# 1. Define data augmentation operations\n",
"##################################################\n",
"\n",
"trainImageTxtFile = dataPath + \"trainimages.txt\"\n",
"imageList = getImageList(trainImageTxtFile)\n",
"\n",
"current_operation = \"Flipud\"\n",
"\n",
"# Flip/mirror input images vertically.\n",
" \n",
"from imgaug import augmenters as iaa\n",
"ia.seed(1)\n",
"seq = iaa.Sequential([\n",
" iaa.Flipud(1.0)\n",
"])\n",
"\n",
"# seq = iaa.Sequential([ \n",
"# # Adjust contrast by scaling each pixel value to (I_ij/255.0)**gamma.\n",
"# # Values in the range gamma=(0.5, 2.0) seem to be sensible.\n",
"# iaa.GammaContrast((0.5, 1.5))\n",
"# ])\n",
"\n",
"# Make our sequence deterministic.\n",
"# We can now apply it to the image and then to the BBs and it will\n",
"# lead to the same augmentations.\n",
"# IMPORTANT: Call this once PER BATCH, otherwise you will always get the exactly same augmentations for every batch!\n",
"seq_det = seq.to_deterministic()\n",
"\n",
"##################################################\n",
"# 2. loop through images\n",
"##################################################\n",
"\n",
"for img in imageList:\n",
" print(img)\n",
" # Grayscale images must have shape (height, width, 1) each.\n",
" #print(os.listdir(dataPath+'images/'))\n",
" currentimage = skimage.io.imread(dataPath+'images/'+img).astype(np.uint8)\n",
" # gray2rgb() simply duplicates the gray values over the three color channels.\n",
" currentimage = color.gray2rgb(currentimage)\n",
" bbs = bboxSetupInImage(dataPath , img.rstrip('.jpg') + '.txt',currentimage)\n",
" # Augment BBs and images.\n",
" # As we only have one image and list of BBs, we use\n",
" # [image] and [bbs] to turn both into lists (batches) for the# functions and then [0] to reverse that. In a real experiment, your\n",
" # variables would likely already be lists.\n",
" image_aug = seq_det.augment_images([currentimage])[0]\n",
" bbs_aug = seq_det.augment_bounding_boxes([bbs])[0]\n",
" augImgFolder = current_operation + \"Images\"\n",
" augTxTFolder = current_operation + \"TXT\"\n",
" createFolder(augImgFolder)\n",
" createFolder(augTxTFolder)\n",
" # Save aug images and bboxes\n",
" skimage.io.imsave(augImgFolder + '/'+\n",
" img.rstrip('.jpg') + \n",
" '_' + current_operation +\n",
" '.jpg'\n",
" ,image_aug)\n",
" saveAugbbox2TXT(augTxTFolder+ '/'+ \n",
" img.rstrip('.jpg') + \n",
" '_'+ current_operation +\n",
" '.txt',bbs_aug)\n",
" # image with BBs before/after augmentation (shown below)\n",
" # image_before = bbs.draw_on_image(currentimage, thickness=2)\n",
" # image_after = bbs_aug.draw_on_image(image_aug, \n",
" # thickness=2, color=[0, 0, 255])\n",
" # image with BBs before/after augmentation (shown below)\n",
" # plot and save figures before and after data augmentations\n",
" #skimage.io.imshow(image_before)\n",
" #skimage.io.imshow(image_after)\n",
" # for i in range(len(bbs.bounding_boxes)):\n",
" # before = bbs.bounding_boxes[i]\n",
" # after = bbs_aug.bounding_boxes[i]\n",
" # print(\"BB %d: (%.4f, %.4f, %.4f, %.4f) -> (%.4f, %.4f, %.4f, %.4f)\" % (\n",
" # i,\n",
" # before.x1, before.y1, before.x2, before.y2,\n",
" # after.x1, after.y1, after.x2, after.y2)\n",
" # )"
]
},
{
"cell_type": "heading",
"metadata": {},
"level": 2,
"source": [
"Rotate 90 or 270 degree"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
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"source": [
"##################################################\n",
"# 1. Define data augmentation operations\n",
"##################################################\n",
"\n",
"trainImageTxtFile = dataPath + \"trainimages.txt\"\n",
"imageList = getImageList(trainImageTxtFile)\n",
"\n",
"current_operation = \"Rot90or270Degree\"\n",
"\n",
"# Rotates all images by 90 or 270 degrees. \n",
" \n",
"from imgaug import augmenters as iaa\n",
"ia.seed(1)\n",
"seq = iaa.Sequential([\n",
" iaa.Rot90([1, 3])\n",
"])\n",
"\n",
"# seq = iaa.Sequential([ \n",
"# # Adjust contrast by scaling each pixel value to (I_ij/255.0)**gamma.\n",
"# # Values in the range gamma=(0.5, 2.0) seem to be sensible.\n",
"# iaa.GammaContrast((0.5, 1.5))\n",
"# ])\n",
"\n",
"# Make our sequence deterministic.\n",
"# We can now apply it to the image and then to the BBs and it will\n",
"# lead to the same augmentations.\n",
"# IMPORTANT: Call this once PER BATCH, otherwise you will always get the exactly same augmentations for every batch!\n",
"seq_det = seq.to_deterministic()\n",
"\n",
"##################################################\n",
"# 2. loop through images\n",
"##################################################\n",
"\n",
"for img in imageList:\n",
" print(img)\n",
" # Grayscale images must have shape (height, width, 1) each.\n",
" #print(os.listdir(dataPath+'images/'))\n",
" currentimage = skimage.io.imread(dataPath+'images/'+img).astype(np.uint8)\n",
" # gray2rgb() simply duplicates the gray values over the three color channels.\n",
" currentimage = color.gray2rgb(currentimage)\n",
" bbs = bboxSetupInImage(dataPath , img.rstrip('.jpg') + '.txt',currentimage)\n",
" # Augment BBs and images.\n",
" # As we only have one image and list of BBs, we use\n",
" # [image] and [bbs] to turn both into lists (batches) for the# functions and then [0] to reverse that. In a real experiment, your\n",
" # variables would likely already be lists.\n",
" image_aug = seq_det.augment_images([currentimage])[0]\n",
" bbs_aug = seq_det.augment_bounding_boxes([bbs])[0]\n",
" augImgFolder = current_operation + \"Images\"\n",
" augTxTFolder = current_operation + \"TXT\"\n",
" createFolder(augImgFolder)\n",
" createFolder(augTxTFolder)\n",
" # Save aug images and bboxes\n",
" skimage.io.imsave(augImgFolder + '/'+\n",
" img.rstrip('.jpg') + \n",
" '_' + current_operation +\n",
" '.jpg'\n",
" ,image_aug)\n",
" saveAugbbox2TXT(augTxTFolder+ '/'+ \n",
" img.rstrip('.jpg') + \n",
" '_'+ current_operation +\n",
" '.txt',bbs_aug)\n",
" # image with BBs before/after augmentation (shown below)\n",
" # image_before = bbs.draw_on_image(currentimage, thickness=2)\n",
" # image_after = bbs_aug.draw_on_image(image_aug, \n",
" # thickness=2, color=[0, 0, 255])\n",
" # image with BBs before/after augmentation (shown below)\n",
" # plot and save figures before and after data augmentations\n",
" #skimage.io.imshow(image_before)\n",
" #skimage.io.imshow(image_after)\n",
" # for i in range(len(bbs.bounding_boxes)):\n",
" # before = bbs.bounding_boxes[i]\n",
" # after = bbs_aug.bounding_boxes[i]\n",
" # print(\"BB %d: (%.4f, %.4f, %.4f, %.4f) -> (%.4f, %.4f, %.4f, %.4f)\" % (\n",
" # i,\n",
" # before.x1, before.y1, before.x2, before.y2,\n",
" # after.x1, after.y1, after.x2, after.y2)\n",
" # )\n",
"\n"
]
},
{
"cell_type": "heading",
"metadata": {},
"level": 1,
"source": [
"## End"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
""
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