[ { "path": "Prediction/prediction_start.py", "content": "import keras\n\n" }, { "path": "Processing/blur_utils.py", "content": "" }, { "path": "Processing/processing_utils.py", "content": "'''\nutility function package for image processing\nby Sibo Zhu, Kieran Xiao Wang\n2017.08.24\n'''\nimport numpy as np\nimport cv2\nimport random\nfrom PIL import Image\nimport PIL\nfrom natsort import natsorted\nimport os, os.path\n\ndef save_image(image_np_array, image_save_path):\n '''\n !!! Sibo, Please complete this function !!!\n :param image_np_array: 3-D numpy array\n :param image_save_path: string\n :return:\n '''\n im = Image.fromarray(image_np_array)\n im.save(image_save_path)\n\n\ndef patch_merge_to_one_from_folder(patch_dir):\n '''\n merge patches into a whole image\n !!! Sibo, please complete this function !!!\n !!! now you are using naming for indicating patch location and blur/no blur, which is fine\n !!! the patch_dir is supposed to contain all patches of an image(no matter blured or not)\n !!! you may want to use os.walk\n !!! if so avoid to have any other .jpg file except image patches(e.g. do not save the whole image in .jpg in that folder)\n :param patch_dir: [string] directory to the folder that contains all patches(of an image)\n :return: [3-d array] whole image in np array\n '''\n patch_dir = patch_dir\n\n pi_imgs = []\n valid_images = [\".jpg\"]\n for f in os.listdir(patch_dir):\n ext = os.path.splitext(f)[1]\n if ext.lower() not in valid_images:\n continue\n pi_imgs.append(Image.open(os.path.join(patch_dir, f)))\n total = len(pi_imgs)\n\n print(str(len(pi_imgs)) + \" patches in total\")\n\n #######################################################\n \"\"\"Loading all the images from directory\"\"\"\n dir = []\n valid_images = [\".jpg\"]\n for f in os.listdir(patch_dir):\n ext = os.path.splitext(f)[1]\n if ext.lower() not in valid_images:\n continue\n dir.append(os.path.join(patch_dir, f))\n\n #####################################################\n \"\"\"concat images with numpy\"\"\"\n\n def concat_img_horizon(list_imgs):\n imgs = [PIL.Image.open(i) for i in list_imgs]\n # pick the image which is the smallest, and resize the others to match it (can be arbitrary image shape here)\n min_shape = sorted([(np.sum(i.size), i.size) for i in imgs])[0][1]\n #\n imgs_comb = np.hstack((np.asarray(i.resize(min_shape)) for i in imgs))\n imgs_comb = PIL.Image.fromarray(imgs_comb)\n return imgs_comb\n\n def concat_img_vertical(list_imgs):\n imgs = [PIL.Image.open(i) for i in list_imgs]\n # pick the image which is the smallest, and resize the others to match it (can be arbitrary image shape here)\n min_shape = sorted([(np.sum(i.size), i.size) for i in imgs])[0][1]\n imgs_comb = np.hstack((np.asarray(i.resize(min_shape)) for i in imgs))\n # for a vertical stacking it is simple: use vstack\n imgs_comb = np.vstack((np.asarray(i.resize(min_shape)) for i in imgs))\n imgs_comb = PIL.Image.fromarray(imgs_comb)\n return imgs_comb\n\n def concat_temp_horizon(list_imgs):\n imgs_comb = np.hstack((np.asarray(i) for i in list_imgs))\n imgs_comb = PIL.Image.fromarray(imgs_comb)\n return imgs_comb\n\n #########################################################\n\n\n \"\"\"counting number of whole pictures in the folder\"\"\"\n max_index = 0\n for i in range(len(dir)):\n flag = int(dir[i].split(\"/\")[-1].split(\",\")[0].split(\"_\")[0])\n if flag > max_index:\n max_index = flag\n\n #############################################\n \"\"\"placing patches to their certain picture\"\"\"\n pic_index = {}\n for elem in range(max_index + 1):\n pic_index[elem] = []\n\n for j in range(len(dir)):\n for k in range(len(pic_index)):\n if int(dir[j].split(\"/\")[-1].split(\",\")[0].split(\"_\")[0]) == k:\n pic_index[k].append(dir[j])\n\n print('the first picture contains ' + str(len(pic_index[0])) + ' patches')\n\n ##########################################\n \"\"\"getting the total columns of picture\"\"\"\n\n def get_total_column(list):\n max_col = 0\n for l in range(len(list)):\n col_flag = int(pic_index[0][l].split(\"/\")[-1].split(\",\")[0].split(\"_\")[1])\n if col_flag > max_col:\n max_col = col_flag\n return max_col\n\n print (\"this picture's total column is \" + str(get_total_column(pic_index[0])))\n #########################################\n \"\"\"getting the total rows of picture\"\"\"\n\n def get_total_row(list):\n max_row = 0\n for o in range(len(list)):\n row_flag = int(list[o].split(\"/\")[-1].split(\",\")[0].split(\"_\")[2])\n if row_flag > max_row:\n max_row = row_flag\n return max_row\n\n print (\"this picture's total row is \" + str(get_total_row(pic_index[0])))\n\n ########################################\n \"\"\"sorting this picture's patches with order of name\"\"\"\n\n def sort_picture_patches(list):\n return natsorted(list)\n\n #####################################\n\n \"\"\"doing global merging\"\"\"\n\n for a in range(len(pic_index)):\n max_col = get_total_column(pic_index[a])\n max_row = get_total_row(pic_index[a])\n pic_index[a] = sort_picture_patches(pic_index[a])\n col_index = {}\n for elem in range(max_col + 1):\n col_index[elem] = []\n\n for j in range(len(pic_index[a])):\n for k in range(max_col):\n if int(pic_index[a][j].split(\"/\")[-1].split(\",\")[0].split(\"_\")[1]) == k:\n col_index[k].append(pic_index[a][j])\n\n saver = []\n\n for i in range(max_col - 1):\n flag = concat_img_vertical(col_index[i])\n saver.append(flag)\n\n res = concat_temp_horizon(saver)\n img = PIL.Image.open(res).convert(\"L\")\n arr = np.array(img)\n return arr\n\ndef mass_patch_merge_to_one_from_folder(patch_dir,save_dir):\n '''\n After implementing the merging patches back to a whole image,\n we can also do that same thing to a folder that contains several patches that\n come from different images and merge and save them back to those original images (with partially\n blurry) based on the naming habit of slicing images.\n :param patch_dir: [string] directory to the folder that contains all patches(of an image)\n :param save_dir: [string] directory to the folder that used to save all those merged images\n :return: This time there's no return\n '''\n patch_dir = patch_dir\n result_dir = save_dir\n\n pi_imgs = []\n valid_images = [\".jpg\"]\n for f in os.listdir(patch_dir):\n ext = os.path.splitext(f)[1]\n if ext.lower() not in valid_images:\n continue\n pi_imgs.append(Image.open(os.path.join(patch_dir, f)))\n total = len(pi_imgs)\n\n print(str(len(pi_imgs)) + \" patches in total\")\n\n #######################################################\n \"\"\"Loading all the images from directory\"\"\"\n dir = []\n valid_images = [\".jpg\"]\n for f in os.listdir(patch_dir):\n ext = os.path.splitext(f)[1]\n if ext.lower() not in valid_images:\n continue\n dir.append(os.path.join(patch_dir, f))\n\n\n #####################################################\n \"\"\"concat images with numpy\"\"\"\n\n def concat_img_horizon(list_imgs):\n imgs = [PIL.Image.open(i) for i in list_imgs]\n # pick the image which is the smallest, and resize the others to match it (can be arbitrary image shape here)\n min_shape = sorted([(np.sum(i.size), i.size) for i in imgs])[0][1]\n #\n imgs_comb = np.hstack((np.asarray(i.resize(min_shape)) for i in imgs))\n imgs_comb = PIL.Image.fromarray(imgs_comb)\n return imgs_comb\n\n def concat_img_vertical(list_imgs):\n imgs = [PIL.Image.open(i) for i in list_imgs]\n # pick the image which is the smallest, and resize the others to match it (can be arbitrary image shape here)\n min_shape = sorted([(np.sum(i.size), i.size) for i in imgs])[0][1]\n imgs_comb = np.hstack((np.asarray(i.resize(min_shape)) for i in imgs))\n # for a vertical stacking it is simple: use vstack\n imgs_comb = np.vstack((np.asarray(i.resize(min_shape)) for i in imgs))\n imgs_comb = PIL.Image.fromarray(imgs_comb)\n return imgs_comb\n\n def concat_temp_horizon(list_imgs):\n imgs_comb = np.hstack((np.asarray(i) for i in list_imgs))\n imgs_comb = PIL.Image.fromarray(imgs_comb)\n return imgs_comb\n\n #########################################################\n\n\n \"\"\"counting number of whole pictures in the folder\"\"\"\n max_index = 0\n for i in range(len(dir)):\n flag = int(dir[i].split(\"/\")[-1].split(\",\")[0].split(\"_\")[0])\n if flag > max_index:\n max_index = flag\n\n #############################################\n \"\"\"placing patches to their certain picture\"\"\"\n pic_index = {}\n for elem in range(max_index + 1):\n pic_index[elem] = []\n\n for j in range(len(dir)):\n for k in range(len(pic_index)):\n if int(dir[j].split(\"/\")[-1].split(\",\")[0].split(\"_\")[0]) == k:\n pic_index[k].append(dir[j])\n\n print('the first picture contains ' + str(len(pic_index[0])) + ' patches')\n\n ##########################################\n \"\"\"getting the total columns of picture\"\"\"\n\n def get_total_column(list):\n max_col = 0\n for l in range(len(list)):\n col_flag = int(pic_index[0][l].split(\"/\")[-1].split(\",\")[0].split(\"_\")[1])\n if col_flag > max_col:\n max_col = col_flag\n return max_col\n\n print (\"this picture's total column is \" + str(get_total_column(pic_index[0])))\n #########################################\n \"\"\"getting the total rows of picture\"\"\"\n\n def get_total_row(list):\n max_row = 0\n for o in range(len(list)):\n row_flag = int(list[o].split(\"/\")[-1].split(\",\")[0].split(\"_\")[2])\n if row_flag > max_row:\n max_row = row_flag\n return max_row\n\n print (\"this picture's total row is \" + str(get_total_row(pic_index[0])))\n\n ########################################\n \"\"\"sorting this picture's patches with order of name\"\"\"\n\n def sort_picture_patches(list):\n return natsorted(list)\n\n #####################################\n\n \"\"\"doing global merging\"\"\"\n\n for a in range(len(pic_index)):\n max_col = get_total_column(pic_index[a])\n max_row = get_total_row(pic_index[a])\n pic_index[a] = sort_picture_patches(pic_index[a])\n col_index = {}\n for elem in range(max_col + 1):\n col_index[elem] = []\n\n for j in range(len(pic_index[a])):\n for k in range(max_col):\n if int(pic_index[a][j].split(\"/\")[-1].split(\",\")[0].split(\"_\")[1]) == k:\n col_index[k].append(pic_index[a][j])\n\n saver = []\n\n for i in range(max_col - 1):\n flag = concat_img_vertical(col_index[i])\n saver.append(flag)\n\n res = concat_temp_horizon(saver)\n res.save(result_dir + str(a) + '.jpg')\n\n\n\ndef image_to_patch(image_path, patch_size, patch_dir):\n '''\n cut an image into patch with certain size\n !!! Sibo, please complete this function !!!\n\n :param image_path: [string] path to the image(e.g. ./whole_image.jpg)\n :param patch_size: [tuple] i.g. (30(length),30(witch))\n :param patch_dir: [string] dir where to save the patch dir.(patch dir is defined to be the folder that contains all\n image patches of an image)\n :return:\n '''\n img = Image.open(image_path)\n (imageWidth, imageHeight) = img.size\n gridx = patch_size\n gridy = patch_size\n rangex = img.width / gridx\n rangey = img.height / gridy\n print rangex * rangey\n for x in xrange(rangex):\n for y in xrange(rangey):\n bbox = (x * gridx, y * gridy, x * gridx + gridx, y * gridy + gridy)\n slice_bit = img.crop(bbox)\n slice_bit.save(patch_dir + str(x) + '_' + str(y) + '.jpg', optimize=True,\n bits=6)\n print(patch_dir + str(x) + '_' + str(y) + '.jpg')\n print(imageWidth)\n\n\ndef directory_to_patch(patch_size,original_path,no_blur_path,blur_path,all_img_path):\n '''\n We take a directory that contains several whole pictures and cut then into custom size of patches,\n then apply 50% chance blur and non-blur to those patches, save them into blurry folder, non-blurry folder,\n and a folder that contains all blurry and non-blurry patches with order.\n :param patch_size: [integer] The custom patch size we want, e.g:for 30x30 patch, enter '30'\n :param original_path: [string] The original path that contains all the original pictures without any modification\n :param no_blur_path: [string] The destination path that contains all the non-blurry patches with order\n :param blur_path: [string] The destination path that contains all the blurry patches with order\n :param all_img_path: [string] The destination path that contains all the patches with order\n :return: There's no return in this function, all the modified patches are saved into the destination path\n '''\n #motion blur preset\n size = 15\n gridx = patch_size\n gridy = patch_size\n kernel_motion_blur = np.zeros((size, size))\n kernel_motion_blur[int((size - 1) / 2), :] = np.ones(size)\n kernel_motion_blur = kernel_motion_blur / size\n\n # go through every image in source folder\n print('begin loading images')\n pi_imgs = []\n cv_imgs = []\n valid_images = [\".jpg\"]\n for f in os.listdir(original_path):\n ext = os.path.splitext(f)[1]\n if ext.lower() not in valid_images:\n continue\n pi_imgs.append(Image.open(os.path.join(original_path, f)))\n cv_imgs.append(cv2.imread(os.path.join(original_path, f)))\n print('finished loading images')\n #\n\n # looping to create blurry and non-blurry images in 50% chance\n for i in range(len(pi_imgs)):\n img = pi_imgs[i]\n (imageWidth, imageHeight) = img.size\n\n rangex = imageWidth / gridx\n rangey = imageHeight / gridy\n for x in xrange(rangex):\n for y in xrange(rangey):\n\n bbox = (x * gridx, y * gridy, x * gridx + gridx, y * gridy + gridy)\n slice_bit = img.crop(bbox)\n if random.randrange(2) == 0:\n slice_bit.save(no_blur_path + str(i) + '_' + str(x) + '_' + str(y) + ',noblur.jpg', optimize=True,\n bits=6)\n slice_bit.save(all_img_path + str(i) + '_' + str(x) + '_' + str(y) + ',noblur.jpg', optimize=True,\n bits=6)\n print(str(i))\n else:\n slice_bit.save(blur_path + str(i) + '_' + str(x) + '_' + str(y) + ',blur.jpg', optimize=True, bits=6)\n img1 = cv2.imread(blur_path + str(i) + '_' + str(x) + '_' + str(y) + ',blur.jpg')\n output = cv2.filter2D(img1, -1, kernel_motion_blur)\n cv2.imwrite(blur_path + str(i) + '_' + str(x) + '_' + str(y) + ',blur.jpg', output)\n cv2.imwrite(all_img_path + str(i) + '_' + str(x) + '_' + str(y) + ',blur.jpg', output)\n print(str(i))\n\n" }, { "path": "README.md", "content": "# MotionBlur-detection-by-CNN\n\n\n```\nTo run the cnn model, just enter \"cnn.py\" and run the code. It might take \ncouple hours to run it if you are using personal computers. Usinga server \nor GPU to run this code would significantly lower the running time.\n```\n## Abstract\n\n```\nOur project aims to detect motion blur from a single, blurry image. We propose\na deep learning approach to predict the probabilistic distribution of motion blur at\nthe patch level using a Convolutional Neural Network (CNN).\n```\n## 1 Our approach\n\n```\nWe approached the problem by slicing 100 images into 30x30 patches, and applied our own motion\nblur algorithm to them (with a random rate of 50%). We then labeled the blurry and non-blurry\npatches with 0s and 1s (0 for still, 1 for blurry), and loaded the modified images in as our training\ndata.\n```\n```\n1.1 Generating the training data\n```\nWe generated the training data using images from thePascal Visual Object Classes Challenge 2010\n(VOC2010)data set. Our work was done in Python using thePIL,numpy,opency, andoslibraries.\n\n```\nOnce we had the original images fromPascal, we had to modify them to fit our needs. We needed to\nhave 100 images, each partially blurred and with a corresponding matrix indicating which part of the\nimage is blurred.\nWe achieved this by:\n```\n1. Making a blurred copy of the original image.\n2. Cutting both images (original and blurry) into 30 × 30 patches.\n3. Creating a 2D List in Python of size 30 × 30 , to represent each image patch We initialize\n each element to 0 (to represent non-blurry).\n4. Picking half the patches from the list and marking them as 1 (to represent blurry).\n5. Putting the final image together to get a partially-blurred, qualifying image (and its corre-\n sponding matrix).\n6. Saving the image as \"n.jpg\" (where n is the serial number of the image), and adding the\n matrix to a list (to form a 3D ’list of lists’) containing the matrices of all the image.\n\n\n\n![Image](https://github.com/Sibozhu/MotionBlur-detection-by-CNN/blob/master/Report/images/blur.png?raw=true)\n```\nImage of the original-to-blur process.\n```\n![Image](https://github.com/Sibozhu/MotionBlur-detection-by-CNN/blob/master/Report/images/patches.png?raw=true)\n\n```\nImage of the image splitting process.\n```\n\n![Image](https://github.com/Sibozhu/MotionBlur-detection-by-CNN/blob/master/Report/images/matrix.png?raw=true)\n\n```\nThe final image with its corresponding matrix.\n```\n\nWe repeat the above for all 100 images, until we end up with a folder containing partially-blurred\nimages\"0.jpg\"through\"100.jpg\", and a 3D list (named\"labels\") that contains 100 matrices. This\nlets us access the matrix for image\"31.jpg\", for example, by querying for\"labels[31]\".\n\n## 2 Learning the Convolutional Neural Network (CNN)\n\nOnce we had the prepared images,we loaded them into our training set.We ran into a prob-\nlem loading the images into anumpyarray, where our images were of the form (30,30,3), while\ntheKeras.Conv2Dlayer required input to be of the form (3,30,30). We solved this by using the\nnumpy.swapexes()function to alter the images’ shape in order to fit the convolutional layer.\n\nWe then apply the CNN learning model. First, we apply a Convolution2D layer with 7 × 7 filters,\nfollowed by aReLUfunction. TheConvlayer’s parameters consiste of a set of learnable filters. Each\nfilter is small spatially, but extends through the depth of the input volume.\n\nDuring the forward pass, we slide each filter across the width and height of the input volume and\ncompute the dot products between the entries of the filter and the input at any position.ReLUis the\nrectifier function- an activation function that can be used by neurons, just like any other activation\nfunction. A node using the rectifier activation function is called aReLU node.ReLUsets all negative\nvalues in the matrix x to 0, and all other values are kept constant. ReLU us computed after the\nconvolution, and thus a nonlinear activation function (liketanhorsigmoid).\n\nAfter that,we add aMaxPooling2Dlayerwith a pool size of 2 × 2. MaxPooling is a sample-\nbased discretization process. The objective is to down-sample an input representation, reducing\nits dimensionality and allowing for assumptions to be made about features contained in the binned\nsub-regions.\n\nWe thenadd aDropoutlayerwith dropout rate of 0.2, which makes our learning process faster.\nDropout randomly ignoring nodes is useful in CNN models because it prevents interdependencies\nfrom emerging between nodes. This allows the network to learn more and form a more robust\nrelationship. We then do the’Conv2D, ReLU, MaxPooling2D, Dropout’circle again. Finally, we add\na fully-connected layer withReLU, and thensoftmaxthe result.Softmaxis a classifier at the end of\nthe neural network — a logistic regression to regularize outputs to a value between 0 and 1.\n\nWe set our model’s learning rate to be 0. 01. This might generally be too big, but we made this\ndecision for the sake of brevity - it was the fastest way to show a result. We chose a batch size of 126\n(because we had large training data). We also choseAdamas our optimizer as it’s the most efficient\noptimizer for our model.\n\nAfter training with 100 epochs,we had testing accuracy of 92%, which is a very optimal rate for\nour model. Our training model is saved in an HDF5 file,\"motionblur.h5\".\n\n![Image](https://github.com/Sibozhu/MotionBlur-detection-by-CNN/blob/master/Report/images/accuracy.jpeg?raw=true)\n\n\n## 3 Conclusion\n\nIn this report, we have proposed a novel CNN-based motion blur detection apporach. We learn an\neffective CNN for estimating motion blur from local patches. In the future, we are interested in\ndesigning a CNN for eastimating motion kernels. We are also interested in design a CNN non-uniform\nmotion deblurring method.\n\n\n## Acknowledgement\n\nThis report has been prepared for the Boston University Machine Learning course (CS 542), taken\nover the Summer 2, 2017 semester by the listed authors. It is intended to be used in compliance of\nthe requirements of the course.\n\n## References\n\n[1] Jian Sun, Wenfei Cao, Zongben Xu, Jean Ponce. Learning a convolutional neural network for non-uniform\nmotion blur removal. CVPR 2015 - IEEE Conference on Computer Vision and Pattern Recognition 2015, Jun\n2015, Boston, United States. IEEE, 2015,.\n\n[2] “Visual Object Classes Challenge 2010 (VOC2010).” The PASCAL Visual Object Classes Challenge 2010\n(VOC2010), PASCAL, 2010, host.robots.ox.ac.uk/pascal/VOC/voc2010/.\n\n\n\n\n\n\n" }, { "path": "Report/nicefrac.sty", "content": "%%\n%% This is file `nicefrac.sty',\n%% generated with the docstrip utility.\n%%\n%% The original source files were:\n%%\n%% units.dtx (with options: `nicefrac')\n%% \n%% LaTeX package for typesetting nice fractions\n%% \n%% Copyright (C) 1998 Axel Reichert\n%% See the files README and COPYING.\n%% \n%% \\CharacterTable\n%% {Upper-case \\A\\B\\C\\D\\E\\F\\G\\H\\I\\J\\K\\L\\M\\N\\O\\P\\Q\\R\\S\\T\\U\\V\\W\\X\\Y\\Z\n%% Lower-case \\a\\b\\c\\d\\e\\f\\g\\h\\i\\j\\k\\l\\m\\n\\o\\p\\q\\r\\s\\t\\u\\v\\w\\x\\y\\z\n%% Digits \\0\\1\\2\\3\\4\\5\\6\\7\\8\\9\n%% Exclamation \\! 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char U+00CF (decimal 207)\n defining Unicode char U+00D0 (decimal 208)\n defining Unicode char U+00D1 (decimal 209)\n defining Unicode char U+00D2 (decimal 210)\n defining Unicode char U+00D3 (decimal 211)\n defining Unicode char U+00D4 (decimal 212)\n defining Unicode char U+00D5 (decimal 213)\n defining Unicode char U+00D6 (decimal 214)\n defining Unicode char U+00D8 (decimal 216)\n defining Unicode char U+00D9 (decimal 217)\n defining Unicode char U+00DA (decimal 218)\n defining Unicode char U+00DB (decimal 219)\n defining Unicode char U+00DC (decimal 220)\n defining Unicode char U+00DD (decimal 221)\n defining Unicode char U+00DE (decimal 222)\n defining Unicode char U+00DF (decimal 223)\n defining Unicode char U+00E0 (decimal 224)\n defining Unicode char U+00E1 (decimal 225)\n defining Unicode char U+00E2 (decimal 226)\n defining Unicode char U+00E3 (decimal 227)\n defining Unicode char U+00E4 (decimal 228)\n defining Unicode char U+00E5 (decimal 229)\n defining Unicode char U+00E6 (decimal 230)\n defining Unicode char U+00E7 (decimal 231)\n defining Unicode char U+00E8 (decimal 232)\n defining Unicode char U+00E9 (decimal 233)\n defining Unicode char U+00EA (decimal 234)\n defining Unicode char U+00EB (decimal 235)\n defining Unicode char U+00EC (decimal 236)\n defining Unicode char U+00ED (decimal 237)\n defining Unicode char U+00EE (decimal 238)\n defining Unicode char U+00EF (decimal 239)\n defining Unicode char U+00F0 (decimal 240)\n defining Unicode char U+00F1 (decimal 241)\n defining Unicode char U+00F2 (decimal 242)\n defining Unicode char U+00F3 (decimal 243)\n defining Unicode char U+00F4 (decimal 244)\n defining Unicode char U+00F5 (decimal 245)\n defining Unicode char U+00F6 (decimal 246)\n defining Unicode char U+00F8 (decimal 248)\n defining Unicode char U+00F9 (decimal 249)\n defining Unicode char U+00FA (decimal 250)\n defining Unicode char U+00FB (decimal 251)\n defining Unicode char U+00FC (decimal 252)\n defining Unicode char U+00FD 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275)\n defining Unicode char U+0114 (decimal 276)\n defining Unicode char U+0115 (decimal 277)\n defining Unicode char U+0116 (decimal 278)\n defining Unicode char U+0117 (decimal 279)\n defining Unicode char U+0118 (decimal 280)\n defining Unicode char U+0119 (decimal 281)\n defining Unicode char U+011A (decimal 282)\n defining Unicode char U+011B (decimal 283)\n defining Unicode char U+011C (decimal 284)\n defining Unicode char U+011D (decimal 285)\n defining Unicode char U+011E (decimal 286)\n defining Unicode char U+011F (decimal 287)\n defining Unicode char U+0120 (decimal 288)\n defining Unicode char U+0121 (decimal 289)\n defining Unicode char U+0122 (decimal 290)\n defining Unicode char U+0123 (decimal 291)\n defining Unicode char U+0124 (decimal 292)\n defining Unicode char U+0125 (decimal 293)\n defining Unicode char U+0128 (decimal 296)\n defining Unicode char U+0129 (decimal 297)\n defining Unicode char U+012A (decimal 298)\n defining Unicode char U+012B (decimal 299)\n defining Unicode char U+012C (decimal 300)\n defining Unicode char U+012D (decimal 301)\n defining Unicode char U+012E (decimal 302)\n defining Unicode char U+012F (decimal 303)\n defining Unicode char U+0130 (decimal 304)\n defining Unicode char U+0131 (decimal 305)\n defining Unicode char U+0132 (decimal 306)\n defining Unicode char U+0133 (decimal 307)\n defining Unicode char U+0134 (decimal 308)\n defining Unicode char U+0135 (decimal 309)\n defining Unicode char U+0136 (decimal 310)\n defining Unicode char U+0137 (decimal 311)\n defining Unicode char U+0139 (decimal 313)\n defining Unicode char U+013A (decimal 314)\n defining Unicode char U+013B (decimal 315)\n defining Unicode char U+013C (decimal 316)\n defining Unicode char U+013D (decimal 317)\n defining Unicode char U+013E (decimal 318)\n defining Unicode char U+0141 (decimal 321)\n defining Unicode char U+0142 (decimal 322)\n defining Unicode char U+0143 (decimal 323)\n defining Unicode char U+0144 (decimal 324)\n defining Unicode char U+0145 (decimal 325)\n defining Unicode char U+0146 (decimal 326)\n defining Unicode char U+0147 (decimal 327)\n defining Unicode char U+0148 (decimal 328)\n defining Unicode char U+014A (decimal 330)\n defining Unicode char U+014B (decimal 331)\n defining Unicode char U+014C (decimal 332)\n defining Unicode char U+014D (decimal 333)\n defining Unicode char U+014E (decimal 334)\n defining Unicode char U+014F (decimal 335)\n defining Unicode char U+0150 (decimal 336)\n defining Unicode char U+0151 (decimal 337)\n defining Unicode char U+0152 (decimal 338)\n defining Unicode char U+0153 (decimal 339)\n defining Unicode char U+0154 (decimal 340)\n defining Unicode char U+0155 (decimal 341)\n defining Unicode char U+0156 (decimal 342)\n defining Unicode char U+0157 (decimal 343)\n defining Unicode char U+0158 (decimal 344)\n defining Unicode char U+0159 (decimal 345)\n defining Unicode char U+015A (decimal 346)\n defining Unicode char U+015B (decimal 347)\n defining Unicode char U+015C (decimal 348)\n defining Unicode char U+015D (decimal 349)\n defining Unicode char U+015E (decimal 350)\n defining Unicode char U+015F (decimal 351)\n defining Unicode char U+0160 (decimal 352)\n defining Unicode char U+0161 (decimal 353)\n defining Unicode char U+0162 (decimal 354)\n defining Unicode char U+0163 (decimal 355)\n defining Unicode char U+0164 (decimal 356)\n defining Unicode char U+0165 (decimal 357)\n defining Unicode char U+0168 (decimal 360)\n defining Unicode char U+0169 (decimal 361)\n defining Unicode char U+016A (decimal 362)\n defining Unicode char U+016B (decimal 363)\n defining Unicode char U+016C (decimal 364)\n defining Unicode char U+016D (decimal 365)\n defining Unicode char U+016E (decimal 366)\n defining Unicode char U+016F (decimal 367)\n defining Unicode char U+0170 (decimal 368)\n defining Unicode char U+0171 (decimal 369)\n defining Unicode char U+0172 (decimal 370)\n defining Unicode char U+0173 (decimal 371)\n defining Unicode char 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\\advance\\labelwidth-\\labelsep}\n\\def\\@listv {\\leftmargin\\leftmarginv\n \\labelwidth\\leftmarginv\n \\advance\\labelwidth-\\labelsep}\n\\def\\@listvi {\\leftmargin\\leftmarginvi\n \\labelwidth\\leftmarginvi\n \\advance\\labelwidth-\\labelsep}\n\n% create title\n\\providecommand{\\maketitle}{}\n\\renewcommand{\\maketitle}{%\n \\par\n \\begingroup\n \\renewcommand{\\thefootnote}{\\fnsymbol{footnote}}\n % for perfect author name centering\n \\renewcommand{\\@makefnmark}{\\hbox to \\z@{$^{\\@thefnmark}$\\hss}}\n % The footnote-mark was overlapping the footnote-text,\n % added the following to fix this problem (MK)\n \\long\\def\\@makefntext##1{%\n \\parindent 1em\\noindent\n \\hbox to 1.8em{\\hss $\\m@th ^{\\@thefnmark}$}##1\n }\n \\thispagestyle{empty}\n \\@maketitle\n \\@thanks\n \\@notice\n \\endgroup\n \\let\\maketitle\\relax\n \\let\\thanks\\relax\n}\n\n% rules for title box at top of first page\n\\newcommand{\\@toptitlebar}{\n \\hrule height 4\\p@\n \\vskip 0.25in\n \\vskip -\\parskip%\n}\n\\newcommand{\\@bottomtitlebar}{\n \\vskip 0.29in\n \\vskip -\\parskip\n \\hrule height 1\\p@\n \\vskip 0.09in%\n}\n\n% create title (includes both anonymized and non-anonymized versions)\n\\providecommand{\\@maketitle}{}\n\\renewcommand{\\@maketitle}{%\n \\vbox{%\n \\hsize\\textwidth\n \\linewidth\\hsize\n \\vskip 0.1in\n \\@toptitlebar\n \\centering\n {\\LARGE\\bf \\@title\\par}\n \\@bottomtitlebar\n \\if@nipsfinal\n \\def\\And{%\n \\end{tabular}\\hfil\\linebreak[0]\\hfil%\n \\begin{tabular}[t]{c}\\bf\\rule{\\z@}{24\\p@}\\ignorespaces%\n }\n \\def\\AND{%\n \\end{tabular}\\hfil\\linebreak[4]\\hfil%\n \\begin{tabular}[t]{c}\\bf\\rule{\\z@}{24\\p@}\\ignorespaces%\n }\n \\begin{tabular}[t]{c}\\bf\\rule{\\z@}{24\\p@}\\@author\\end{tabular}%\n \\else\n \\begin{tabular}[t]{c}\\bf\\rule{\\z@}{24\\p@}\n Anonymous Author(s) \\\\\n Affiliation \\\\\n Address \\\\\n \\texttt{email} \\\\\n \\end{tabular}%\n \\fi\n \\vskip 0.3in \\@minus 0.1in\n }\n}\n\n% add conference notice to bottom of first page\n\\newcommand{\\ftype@noticebox}{8}\n\\newcommand{\\@notice}{%\n % give a bit of extra room back to authors on first page\n \\enlargethispage{2\\baselineskip}%\n \\@float{noticebox}[b]%\n \\footnotesize\\@noticestring%\n \\end@float%\n}\n\n% abstract styling\n\\renewenvironment{abstract}%\n{%\n \\vskip 0.075in%\n \\centerline%\n {\\large\\bf Abstract}%\n \\vspace{0.5ex}%\n \\begin{quote}%\n}\n{\n \\par%\n \\end{quote}%\n \\vskip 1ex%\n}\n\n\\endinput\n" }, { "path": "Report/nips_2017.tex", "content": "\\documentclass{article}\n\n\\usepackage[final]{nips_2017}\n\n% to compile a camera-ready version, add the [final] option, e.g.:\n% \\usepackage[final]{nips_2017}\n\n\\usepackage[utf8]{inputenc} % allow utf-8 input\n\\usepackage[T1]{fontenc} % use 8-bit T1 fonts\n\\usepackage{hyperref} % hyperlinks\n\\usepackage{url} % simple URL typesetting\n\\usepackage{booktabs} % professional-quality tables\n\\usepackage{amsfonts} % blackboard math symbols\n\\usepackage{nicefrac} % compact symbols for 1/2, etc.\n\\usepackage{microtype} % microtypography\n\\usepackage{graphicx}\t\t% to allow images\n\n\\graphicspath{ {images/} }\n\n\\title{Detecting Motion Blur in Images}\n\n\\author{\n Karan Varindani \\\\\n \\And\n Wenyang Zhang \\\\\n \\And\n Sibo Zhu \\\\\n}\n\n\\begin{document}\n\n\\maketitle\n\n\\begin{abstract}\n Our project aims to estimate motion blur from a single, blurry image. \n We propose a deep learning approach to predict the probabilistic distribution \n of motion blur at the patch level using a Convolutional Neural Network (CNN).\n\\end{abstract}\n\n\\section{Our approach}\nWe approached the problem by slicing 100 images into 30x30 patches, and applied\nour own motion blur algorithm to them (with a random rate of 50\\%). We then labeled \nthe blurry and non-blurry patches with 0s and 1s (0 for still, 1 for blurry), and\nloaded the modified images in as our training data.\n \n\\subsection{Generating the training data}\nWe generated the training data using images from the \n\\textit{\\href{http://host.robots.ox.ac.uk/pascal/VOC/voc2010/}{Pascal Visual Object \nClasses Challenge 2010 (VOC2010)}} data set. Our work was done in Python using the \n\\textit{PIL}, \\textit{numpy}, \\textit{opency}, and \\textit{os} libraries.\n\nOnce we had the original images from \\textit{Pascal}, we had to modify them to fit our \nneeds. We needed to have 100 images, each partially blurred and with a corresponding \nmatrix indicating which part of the image is blurred. \n\nWe achieved this by:\n\\begin{enumerate}\n \\item Making a blurred copy of the original image. \n \\item Cutting both images (original and blurry) into $30\\times30$ patches.\n \\item Creating a 2D List in Python of size $30\\times30$, to represent each image patch\n We initialize each element to 0 (to represent non-blurry). \n \\item Picking half the patches from the list and marking them as 1 (to represent \n blurry).\n \\item Putting the final image together to get a partially-blurred, qualifying image \n (and its corresponding matrix).\n \\item Saving the image as \"n.jpg\" (where n is the serial number of the image), and \n adding the matrix to a list (to form a 3D 'list of lists') containing the matrices \n of all the image.\n\\end{enumerate}\n\n\\includegraphics[width=\\textwidth]{blur} \\\\\n\\textit{Image of the original-to-blur process.} \\\\ \n\\includegraphics[width=\\textwidth]{patches} \\\\\n\\textit{Image of the image splitting process.} \\\\\n\\includegraphics[width=\\textwidth]{matrix} \\\\\n\\textit{The final image with its corresponding matrix.} \\\\\n\nWe repeat the above for all 100 images, until we end up with a folder containing \npartially-blurred images \\textit{\"0.jpg\"} through \\textit{\"100.jpg\"}, and a 3D list \n(named \\textit{\"labels\"}) that contains 100 matrices. This lets us access the matrix for \nimage \\textit{\"31.jpg\"}, for example, by querying for \\textit{\"labels[31]\"}.\n\n\\section{Learning the Convolutional Neural Network (CNN)}\nOnce we had the prepared images, \\textbf{we loaded them into our training set.} We ran \ninto a problem loading the images into a \\textit{numpy} array, where our images were of \nthe form (30,30,3), while the \\textit{Keras.Conv2D} layer required input to be of the form \n(3,30,30). We solved this by using the \\textit{numpy.swapexes()} function to alter the \nimages' shape in order to fit the convolutional layer.\n\n\\paragraph{We then apply the CNN learning model.}\nFirst, we apply a Convolution2D layer with $7\\times7$ filters, followed by a \\textit{ReLU} \nfunction. The \\textit{Conv} layer's parameters consiste of a set of learnable filters. \nEach filter is small spatially, but extends through the depth of the input volume. \n\n\\paragraph{During the forward pass,} \nwe slide each filter across the width and height of the input \nvolume and compute the dot products between the entries of the filter and the input at \nany position. \\textit{ReLU} is the rectifier function- an activation function that can be \nused by neurons, just like any other activation function. A node using the rectifier \nactivation function is called a \\textit{ReLU node}. \\textit{ReLU} sets all negative values \nin the matrix x to 0, and all other values are kept constant. ReLU us computed after the \nconvolution, and thus a nonlinear activation function (like \\textit{tanh} or \n\\textit{sigmoid}).\n\nAfter that, \\textbf{we add a \\textit{MaxPooling2D} layer} with a pool size of $2\\times2$. \nMaxPooling is a sample-based discretization process. The objective is to down-sample an \ninput representation, reducing its dimensionality and allowing for assumptions to be made \nabout features contained in the binned sub-regions. \n\nWe then \\textbf{add a \\textit{Dropout} layer} with dropout rate of 0.2, which makes our \nlearning process faster. Dropout randomly ignoring nodes is useful in CNN models because \nit prevents interdependencies from emerging between nodes. This allows the network to learn \nmore and form a more robust relationship. We then do the \\textit{'Conv2D, ReLU, \nMaxPooling2D, Dropout'} circle again. Finally, we add a fully-connected layer with \n\\textit{ReLU}, and then \\textit{softmax} the result. \\textit{Softmax} is a classifier at \nthe end of the neural network — a logistic regression to regularize outputs to a value \nbetween 0 and 1. \n\n\\paragraph{We set our model's learning rate to be $0.01$.} This might generally be too \nbig, but we made this decision for the sake of brevity - it was the fastest way to show \na result. We chose a batch size of 126 (because we had large training data). We also \nchose \\textit{Adam} as our optimizer as it's the most efficient optimizer for our model.\n\nAfter training with 100 epochs, \\textbf{we had testing accuracy of 92\\%}, which is a \nvery optimal rate for our model. Our training model is saved in an HDF5 file, \n\\textit{\"motionblur.h5\"}.\n\n\\includegraphics[width=\\textwidth]{accuracy} \\\\\n\n\\section{Conclusion}\nIn this report, we have proposed a novel CNN-based motion blur detection apporach. We \nlearn an effective CNN for estimating motion blur from local patches. In the future, \nwe are interested in designing a CNN for eastimating motion kernels. We are also interested \nin design a CNN non-uniform motion deblurring method. \n\n\\section*{Acknowledgement}\nThis report has been prepared for the Boston University Machine Learning course (CS 542), \ntaken over the Summer 2, 2017 semester by the listed authors. It is intended to be used \nin compliance of the requirements of the course. \n\n\\section*{References}\n\\medskip\n\\small\n\n[1] Jian Sun, Wenfei Cao, Zongben Xu, Jean Ponce. Learning a convolutional neural network \nfor non-uniform motion blur removal. CVPR 2015 - IEEE Conference on Computer Vision and \nPattern Recognition 2015, Jun 2015, Boston, United States. IEEE, 2015, .\n\n[2] “Visual Object Classes Challenge 2010 (VOC2010).” The PASCAL Visual Object Classes \nChallenge 2010 (VOC2010), PASCAL, 2010, host.robots.ox.ac.uk/pascal/VOC/voc2010/.\n\n\\end{document}\n" }, { "path": "cnn.py", "content": "import numpy\nimport cv2\nimport PIL\nimport os, os.path\nimport matplotlib.pyplot as plt\nfrom PIL import Image\nfrom keras.callbacks import EarlyStopping\nfrom sklearn.utils import shuffle\nfrom sklearn.model_selection import train_test_split\n\nfrom keras.constraints import maxnorm\nfrom keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array\nfrom matplotlib import pyplot\nfrom scipy.misc import toimage\nfrom keras.models import Sequential\nfrom keras.layers import Dropout\nfrom keras import callbacks\nfrom keras.layers import Dense, Flatten\nfrom keras.layers.core import Dense, Dropout, Activation, Flatten\nfrom keras.optimizers import RMSprop, SGD,Adam\nfrom keras.layers.convolutional import MaxPooling2D\nfrom keras.layers.convolutional import Convolution2D\nfrom keras.utils import np_utils\nfrom keras import backend as k\n\nk.set_image_dim_ordering('th')\n\nseed = 7\nnumpy.random.seed(seed)\ntrainblur_directory = './s_cnn/train/blur/'\ntrainnoblur_directory = './s_cnn/train/no_blur/'\ntestblur_directory = './s_cnn/test/0/blur/'\ntestnoblur_directory = './s_cnn/test/0/no_blur/'\nfilepath = \"./s_cnn/models/\"\n\nnum_classes = 2\n#########################################################\n#loading blurry images\nimg_data_list1=[]\ndata_dir_list1 = os.listdir(trainblur_directory)\n\nimg_list1=os.listdir(trainblur_directory)\nfor img in img_list1:\n\tinput_img=cv2.imread(trainblur_directory + img )\n\tinput_img=numpy.swapaxes(input_img,0,2)\n\n\timg_data_list1.append(input_img)\n\nimg_data1 = numpy.array(img_data_list1)\nimg_data1 = img_data1.astype('float32')\nimg_data1 /= 255\n\nprint(img_data1.shape)\nnum_of_samples1 = img_data1.shape[0]\nlabels1 = numpy.ones((num_of_samples1,),dtype='int64')\nprint(\"length of labels1 is \"+str(len(labels1)))\nprint(\"labels1 are all \"+str(labels1[10]))\n\n##########################################################\n#laoding none blurry images\n\nimg_data_list2=[]\ndata_dir_list2 = os.listdir(trainnoblur_directory)\n\nimg_list2=os.listdir(trainnoblur_directory)\nfor img in img_list2:\n\tinput_img=cv2.imread(trainnoblur_directory + img )\n\tinput_img = numpy.swapaxes(input_img, 0, 2)\n\timg_data_list2.append(input_img)\n\nimg_data2 = numpy.array(img_data_list2)\nimg_data2 = img_data2.astype('float32')\nimg_data2 /= 255\nprint(img_data2.shape)\n\nnum_of_samples2 = img_data2.shape[0]\nlabels2 = numpy.ones((num_of_samples2,),dtype='int64')\nlabels2[:]=0\nprint(\"length of labels2 is \"+str(len(labels2)))\nprint(\"labels1 are all \"+str(labels2[10]))\n#######################################################\n# Combine the two numpy arrays and shuffle\nlabels=numpy.concatenate((labels1,labels2),axis=0)\nimg_data = numpy.concatenate((img_data1,img_data2),axis=0)\nY = np_utils.to_categorical(labels, num_classes)\n#Shuffle the dataset\nx,y = shuffle(img_data,Y, random_state=2)\n# Split the dataset\nX_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=2)\n###########################################################\n# Defining the model\ninput_shape=img_data[0].shape\n\nmodel = Sequential()\nmodel.add(Convolution2D(96, 7,7,input_shape=input_shape))\nmodel.add(Activation('relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Dropout(0.2))\nmodel.add(Convolution2D(256, 5, 5))\nmodel.add(Activation('relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Dropout(0.2))\n\nmodel.add(Flatten())\nmodel.add(Dense(1024))\nmodel.add(Activation('relu'))\nmodel.add(Dropout(0.2))\nmodel.add(Dense(num_classes))\nmodel.add(Activation('softmax'))\n\n###########################\nepochs = 100\nlearning_rate = 0.01\ndecay = learning_rate / epochs\nadam = Adam(lr=learning_rate)\nmodel.compile(loss='categorical_crossentropy', optimizer='adam',metrics=[\"accuracy\"])\n\nmodel.summary()\nmodel.get_config()\nmodel.layers[0].get_config()\nmodel.layers[0].input_shape\nmodel.layers[0].output_shape\nmodel.layers[0].get_weights()\nnumpy.shape(model.layers[0].get_weights()[0])\nmodel.layers[0].trainable\n\n# Training\nhist = model.fit(X_train, y_train, batch_size=128, nb_epoch=100, verbose=1, validation_data=(X_test, y_test))\n\nfilename='model_train_new.csv'\ncsv_log=callbacks.CSVLogger(filename, separator=',', append=False)\n\nearly_stopping=callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=0, verbose=0, mode='min')\n\ncheckpoint = callbacks.ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min')\n\n# tensorboard callback\ntensorboard_callback = k.callbacks.TensorBoard(log_dir='./logs', histogram_freq=0, batch_size=32, write_graph=True, write_grads=False, write_images=False, embeddings_freq=0, embeddings_layer_names=None, embeddings_metadata=None)\n\ncallbacks_list = [csv_log,early_stopping,checkpoint, tensorboard_callback]\n\n\n# Evaluating the model\n\nscore = model.evaluate(X_test, y_test, show_accuracy=True, verbose=0)\nprint('Test Loss:', score[0])\nprint('Test accuracy:', score[1])\n\ntest_image = X_test[0:1]\nprint (test_image.shape)\n\nprint(model.predict(test_image))\nprint(model.predict_classes(test_image))\nprint(y_test[0:1])\n\n# Save our model here\nfile = open(filepath+\"motionblur.h5\", 'a')\nmodel.save(filepath+\"motionblur.h5\")\nfile.close()\n######################################################\n\n" }, { "path": "image_slice.py", "content": "from PIL import Image\nimg = Image.open(\"/Users/sibozhu/DeepLearning/testing/JR_blur.jpg\")\nprint(img)\n(imageWidth, imageHeight)=img.size\ngridx=60\ngridy=60\nrangex=img.width/gridx\nrangey=img.height/gridy\nprint rangex*rangey\nfor x in xrange(rangex):\n for y in xrange(rangey):\n bbox=(x*gridx, y*gridy, x*gridx+gridx, y*gridy+gridy)\n slice_bit=img.crop(bbox)\n slice_bit.save('/Users/sibozhu/DeepLearning/testing/ppt/'+str(x)+','+str(y)+'.jpg', optimize=True, bits=6)\n" }, { "path": "mergingpatches.py", "content": "import numpy as np\nimport cv2\nimport random\nfrom PIL import Image\nimport PIL\nimport os, os.path\nimport numpy as np\nfrom natsort import natsorted\nimport matplotlib.pyplot as plt\n\npatch_dir = \"./testing/\"\ntemp_dir = \"./testing/temp/\"\nresult_dir = \"./testing/result/\"\n\npi_imgs = []\n# cv_imgs = []\nvalid_images = [\".jpg\"]\nfor f in os.listdir(patch_dir):\n ext = os.path.splitext(f)[1]\n if ext.lower() not in valid_images:\n continue\n pi_imgs.append(Image.open(os.path.join(patch_dir,f)))\n # cv_imgs.append(cv2.imread(os.path.join(patch_dir,f)))\ntotal = len(pi_imgs)\n\nprint(str(len(pi_imgs))+\" patches in total\")\n\n#######################################################\n\"\"\"Loading all the images from directory\"\"\"\ndir=[]\nvalid_images = [\".jpg\"]\nfor f in os.listdir(patch_dir):\n ext = os.path.splitext(f)[1]\n if ext.lower() not in valid_images:\n continue\n dir.append(os.path.join(patch_dir,f))\n\n\n##############################################################\n'''\nfor testing here, no influence on global algorithm\n'''\n\n\n# flg = './testing/0_5_9,blur.jpg'\n#\n# im_index = flg.split(\"/\")[-1].split(\",\")[0].split(\"_\")[0]\n# print(\"the image's index: \"+str(im_index))\n#\n# im_width = flg.split(\"/\")[-1].split(\",\")[0].split(\"_\")[1]\n# print(\"the image's width's code: \"+str(im_width))\n#\n# im_height = flg.split(\"/\")[-1].split(\",\")[0].split(\"_\")[2]\n# print(\"the image's height's code: \"+str(im_height))\n\n#######################################\n\"\"\"concat images with numpy\"\"\"\n\ndef concat_img_horizon(list_imgs):\n imgs = [PIL.Image.open(i) for i in list_imgs]\n # pick the image which is the smallest, and resize the others to match it (can be arbitrary image shape here)\n min_shape = sorted([(np.sum(i.size), i.size) for i in imgs])[0][1]\n #\n imgs_comb = np.hstack((np.asarray(i.resize(min_shape)) for i in imgs))\n imgs_comb = PIL.Image.fromarray( imgs_comb)\n # imgs_comb.save( './testing/temp/'+str(save_name) +',horizon.jpg' )\n return imgs_comb\n\ndef concat_img_vertical(list_imgs):\n imgs = [PIL.Image.open(i) for i in list_imgs]\n # pick the image which is the smallest, and resize the others to match it (can be arbitrary image shape here)\n min_shape = sorted([(np.sum(i.size), i.size) for i in imgs])[0][1]\n imgs_comb = np.hstack((np.asarray(i.resize(min_shape)) for i in imgs))\n # for a vertical stacking it is simple: use vstack\n imgs_comb = np.vstack((np.asarray(i.resize(min_shape)) for i in imgs))\n imgs_comb = PIL.Image.fromarray(imgs_comb)\n # imgs_comb.save( './testing/temp/'+str(save_name) +',vertical.jpg' )\n return imgs_comb\n\ndef concat_temp_horizon(list_imgs):\n imgs_comb = np.hstack((np.asarray(i) for i in list_imgs))\n imgs_comb = PIL.Image.fromarray(imgs_comb)\n return imgs_comb\n\n\n#########################################################\n'''\nfor testing here, no influence on global algorithm\n'''\n#\n# list_im1 = pic_index[0][:3]\n# list_im2 = pic_index[0][3:6]\n# test1 = concat_img_vertical(list_im1)\n# test2 = concat_img_vertical(list_im2)\n# test3 = concat_temp_horizon(test1,test2)\n# test4 = concat_temp_horizon(test2,test3)\n# plt.imshow(test4)\n# plt.show()\n\n##############################################################\n\n\"\"\"counting number of whole pictures in the folder\"\"\"\nmax_index=0\nfor i in range(len(dir)):\n flag = int(dir[i].split(\"/\")[-1].split(\",\")[0].split(\"_\")[0])\n if flag > max_index:\n max_index = flag\n # im_width = dir[i].split(\"/\")[-1].split(\",\")[0].split(\"_\")[1]\n # im_height = dir[i].split(\"/\")[-1].split(\",\")[0].split(\"_\")[2]\n\n#############################################\n\"\"\"placing patches to their certain picture\"\"\"\npic_index={}\nfor elem in range(max_index+1):\n pic_index[elem]=[]\n\nfor j in range(len(dir)):\n for k in range(len(pic_index)):\n if int(dir[j].split(\"/\")[-1].split(\",\")[0].split(\"_\")[0]) == k:\n pic_index[k].append(dir[j])\n\nprint('the first picture contains '+str(len(pic_index[0]))+' patches')\n\n##########################################\n\"\"\"getting the total columns of picture\"\"\"\ndef get_total_column(list):\n max_col = 0\n for l in range(len(list)):\n col_flag = int(pic_index[0][l].split(\"/\")[-1].split(\",\")[0].split(\"_\")[1])\n if col_flag > max_col:\n max_col = col_flag\n return max_col\n\nprint (\"this picture's total column is \"+str(get_total_column(pic_index[0])))\n#########################################\n\"\"\"getting the total rows of picture\"\"\"\ndef get_total_row(list):\n max_row = 0\n for o in range(len(list)):\n row_flag = int(list[o].split(\"/\")[-1].split(\",\")[0].split(\"_\")[2])\n if row_flag > max_row:\n max_row = row_flag\n return max_row\nprint (\"this picture's total row is \" + str(get_total_row(pic_index[0])))\n\n\n########################################\n\"\"\"sorting this picture's patches with order of name\"\"\"\ndef sort_picture_patches(list):\n return natsorted(list)\n#####################################\n\n\"\"\"doing global merging\"\"\"\n\nfor a in range(len(pic_index)):\n max_col = get_total_column(pic_index[a])\n max_row = get_total_row(pic_index[a])\n pic_index[a] = sort_picture_patches(pic_index[a])\n col_index = {}\n for elem in range(max_col + 1):\n col_index[elem] = []\n\n for j in range(len(pic_index[a])):\n for k in range(max_col):\n if int(pic_index[a][j].split(\"/\")[-1].split(\",\")[0].split(\"_\")[1]) == k:\n col_index[k].append(pic_index[a][j])\n\n saver = []\n\n for i in range(max_col - 1):\n flag = concat_img_vertical(col_index[i])\n saver.append(flag)\n\n res = concat_temp_horizon(saver)\n res.save(result_dir + str(a)+ '.jpg')\n\n" }, { "path": "motionblur.py", "content": "import cv2\nimport numpy as np\n\nimg = cv2.imread('/Users/sibozhu/DeepLearning/testing/JR.jpg')\n\n\nsize = 15\n\n# generating the kernel\nkernel_motion_blur = np.zeros((size, size))\nkernel_motion_blur[int((size-1)/2), :] = np.ones(size)\nkernel_motion_blur = kernel_motion_blur / size\n\n# applying the kernel to the input image\noutput = cv2.filter2D(img, -1, kernel_motion_blur)\n\n\ncv2.imwrite('/Users/sibozhu/DeepLearning/testing/JR_blur.jpg',output)\n\n" }, { "path": "processing_utils.py", "content": "'''\nutility function package for image processing\nby Sibo Zhu, Kieran Xiao Wang\n2017.08.24\n'''\nimport numpy as np\nimport cv2\nimport random\nfrom PIL import Image\nimport PIL\nfrom natsort import natsorted\nimport os, os.path\n\ndef save_image(image_np_array, image_save_path):\n '''\n !!! Sibo, Please complete this function !!!\n :param image_np_array: 3-D numpy array\n :param image_save_path: string\n :return:\n '''\n im = Image.fromarray(image_np_array)\n im.save(image_save_path)\n\n\ndef patch_merge_to_one_from_folder(patch_dir):\n '''\n merge patches into a whole image\n !!! Sibo, please complete this function !!!\n !!! now you are using naming for indicating patch location and blur/no blur, which is fine\n !!! the patch_dir is supposed to contain all patches of an image(no matter blured or not)\n !!! you may want to use os.walk\n !!! if so avoid to have any other .jpg file except image patches(e.g. do not save the whole image in .jpg in that folder)\n :param patch_dir: [string] directory to the folder that contains all patches(of an image)\n :return: [3-d array] whole image in np array\n '''\n patch_dir = patch_dir\n\n pi_imgs = []\n valid_images = [\".jpg\"]\n for f in os.listdir(patch_dir):\n ext = os.path.splitext(f)[1]\n if ext.lower() not in valid_images:\n continue\n pi_imgs.append(Image.open(os.path.join(patch_dir, f)))\n total = len(pi_imgs)\n\n print(str(len(pi_imgs)) + \" patches in total\")\n\n #######################################################\n \"\"\"Loading all the images from directory\"\"\"\n dir = []\n valid_images = [\".jpg\"]\n for f in os.listdir(patch_dir):\n ext = os.path.splitext(f)[1]\n if ext.lower() not in valid_images:\n continue\n dir.append(os.path.join(patch_dir, f))\n\n #####################################################\n \"\"\"concat images with numpy\"\"\"\n\n def concat_img_horizon(list_imgs):\n imgs = [PIL.Image.open(i) for i in list_imgs]\n # pick the image which is the smallest, and resize the others to match it (can be arbitrary image shape here)\n min_shape = sorted([(np.sum(i.size), i.size) for i in imgs])[0][1]\n #\n imgs_comb = np.hstack((np.asarray(i.resize(min_shape)) for i in imgs))\n imgs_comb = PIL.Image.fromarray(imgs_comb)\n return imgs_comb\n\n def concat_img_vertical(list_imgs):\n imgs = [PIL.Image.open(i) for i in list_imgs]\n # pick the image which is the smallest, and resize the others to match it (can be arbitrary image shape here)\n min_shape = sorted([(np.sum(i.size), i.size) for i in imgs])[0][1]\n imgs_comb = np.hstack((np.asarray(i.resize(min_shape)) for i in imgs))\n # for a vertical stacking it is simple: use vstack\n imgs_comb = np.vstack((np.asarray(i.resize(min_shape)) for i in imgs))\n imgs_comb = PIL.Image.fromarray(imgs_comb)\n return imgs_comb\n\n def concat_temp_horizon(list_imgs):\n imgs_comb = np.hstack((np.asarray(i) for i in list_imgs))\n imgs_comb = PIL.Image.fromarray(imgs_comb)\n return imgs_comb\n\n #########################################################\n\n\n \"\"\"counting number of whole pictures in the folder\"\"\"\n max_index = 0\n for i in range(len(dir)):\n flag = int(dir[i].split(\"/\")[-1].split(\",\")[0].split(\"_\")[0])\n if flag > max_index:\n max_index = flag\n\n #############################################\n \"\"\"placing patches to their certain picture\"\"\"\n pic_index = {}\n for elem in range(max_index + 1):\n pic_index[elem] = []\n\n for j in range(len(dir)):\n for k in range(len(pic_index)):\n if int(dir[j].split(\"/\")[-1].split(\",\")[0].split(\"_\")[0]) == k:\n pic_index[k].append(dir[j])\n\n print('the first picture contains ' + str(len(pic_index[0])) + ' patches')\n\n ##########################################\n \"\"\"getting the total columns of picture\"\"\"\n\n def get_total_column(list):\n max_col = 0\n for l in range(len(list)):\n col_flag = int(pic_index[0][l].split(\"/\")[-1].split(\",\")[0].split(\"_\")[1])\n if col_flag > max_col:\n max_col = col_flag\n return max_col\n\n print (\"this picture's total column is \" + str(get_total_column(pic_index[0])))\n #########################################\n \"\"\"getting the total rows of picture\"\"\"\n\n def get_total_row(list):\n max_row = 0\n for o in range(len(list)):\n row_flag = int(list[o].split(\"/\")[-1].split(\",\")[0].split(\"_\")[2])\n if row_flag > max_row:\n max_row = row_flag\n return max_row\n\n print (\"this picture's total row is \" + str(get_total_row(pic_index[0])))\n\n ########################################\n \"\"\"sorting this picture's patches with order of name\"\"\"\n\n def sort_picture_patches(list):\n return natsorted(list)\n\n #####################################\n\n \"\"\"doing global merging\"\"\"\n\n for a in range(len(pic_index)):\n max_col = get_total_column(pic_index[a])\n max_row = get_total_row(pic_index[a])\n pic_index[a] = sort_picture_patches(pic_index[a])\n col_index = {}\n for elem in range(max_col + 1):\n col_index[elem] = []\n\n for j in range(len(pic_index[a])):\n for k in range(max_col):\n if int(pic_index[a][j].split(\"/\")[-1].split(\",\")[0].split(\"_\")[1]) == k:\n col_index[k].append(pic_index[a][j])\n\n saver = []\n\n for i in range(max_col - 1):\n flag = concat_img_vertical(col_index[i])\n saver.append(flag)\n\n res = concat_temp_horizon(saver)\n img = PIL.Image.open(res).convert(\"L\")\n arr = np.array(img)\n return arr\n\ndef mass_patch_merge_to_one_from_folder(patch_dir,save_dir):\n '''\n After implementing the merging patches back to a whole image,\n we can also do that same thing to a folder that contains several patches that\n come from different images and merge and save them back to those original images (with partially\n blurry) based on the naming habit of slicing images.\n :param patch_dir: [string] directory to the folder that contains all patches(of an image)\n :param save_dir: [string] directory to the folder that used to save all those merged images\n :return: This time there's no return\n '''\n patch_dir = patch_dir\n result_dir = save_dir\n\n pi_imgs = []\n valid_images = [\".jpg\"]\n for f in os.listdir(patch_dir):\n ext = os.path.splitext(f)[1]\n if ext.lower() not in valid_images:\n continue\n pi_imgs.append(Image.open(os.path.join(patch_dir, f)))\n total = len(pi_imgs)\n\n print(str(len(pi_imgs)) + \" patches in total\")\n\n #######################################################\n \"\"\"Loading all the images from directory\"\"\"\n dir = []\n valid_images = [\".jpg\"]\n for f in os.listdir(patch_dir):\n ext = os.path.splitext(f)[1]\n if ext.lower() not in valid_images:\n continue\n dir.append(os.path.join(patch_dir, f))\n\n\n #####################################################\n \"\"\"concat images with numpy\"\"\"\n\n def concat_img_horizon(list_imgs):\n imgs = [PIL.Image.open(i) for i in list_imgs]\n # pick the image which is the smallest, and resize the others to match it (can be arbitrary image shape here)\n min_shape = sorted([(np.sum(i.size), i.size) for i in imgs])[0][1]\n #\n imgs_comb = np.hstack((np.asarray(i.resize(min_shape)) for i in imgs))\n imgs_comb = PIL.Image.fromarray(imgs_comb)\n return imgs_comb\n\n def concat_img_vertical(list_imgs):\n imgs = [PIL.Image.open(i) for i in list_imgs]\n # pick the image which is the smallest, and resize the others to match it (can be arbitrary image shape here)\n min_shape = sorted([(np.sum(i.size), i.size) for i in imgs])[0][1]\n imgs_comb = np.hstack((np.asarray(i.resize(min_shape)) for i in imgs))\n # for a vertical stacking it is simple: use vstack\n imgs_comb = np.vstack((np.asarray(i.resize(min_shape)) for i in imgs))\n imgs_comb = PIL.Image.fromarray(imgs_comb)\n return imgs_comb\n\n def concat_temp_horizon(list_imgs):\n imgs_comb = np.hstack((np.asarray(i) for i in list_imgs))\n imgs_comb = PIL.Image.fromarray(imgs_comb)\n return imgs_comb\n\n #########################################################\n\n\n \"\"\"counting number of whole pictures in the folder\"\"\"\n max_index = 0\n for i in range(len(dir)):\n flag = int(dir[i].split(\"/\")[-1].split(\",\")[0].split(\"_\")[0])\n if flag > max_index:\n max_index = flag\n\n #############################################\n \"\"\"placing patches to their certain picture\"\"\"\n pic_index = {}\n for elem in range(max_index + 1):\n pic_index[elem] = []\n\n for j in range(len(dir)):\n for k in range(len(pic_index)):\n if int(dir[j].split(\"/\")[-1].split(\",\")[0].split(\"_\")[0]) == k:\n pic_index[k].append(dir[j])\n\n print('the first picture contains ' + str(len(pic_index[0])) + ' patches')\n\n ##########################################\n \"\"\"getting the total columns of picture\"\"\"\n\n def get_total_column(list):\n max_col = 0\n for l in range(len(list)):\n col_flag = int(pic_index[0][l].split(\"/\")[-1].split(\",\")[0].split(\"_\")[1])\n if col_flag > max_col:\n max_col = col_flag\n return max_col\n\n print (\"this picture's total column is \" + str(get_total_column(pic_index[0])))\n #########################################\n \"\"\"getting the total rows of picture\"\"\"\n\n def get_total_row(list):\n max_row = 0\n for o in range(len(list)):\n row_flag = int(list[o].split(\"/\")[-1].split(\",\")[0].split(\"_\")[2])\n if row_flag > max_row:\n max_row = row_flag\n return max_row\n\n print (\"this picture's total row is \" + str(get_total_row(pic_index[0])))\n\n ########################################\n \"\"\"sorting this picture's patches with order of name\"\"\"\n\n def sort_picture_patches(list):\n return natsorted(list)\n\n #####################################\n\n \"\"\"doing global merging\"\"\"\n\n for a in range(len(pic_index)):\n max_col = get_total_column(pic_index[a])\n max_row = get_total_row(pic_index[a])\n pic_index[a] = sort_picture_patches(pic_index[a])\n col_index = {}\n for elem in range(max_col + 1):\n col_index[elem] = []\n\n for j in range(len(pic_index[a])):\n for k in range(max_col):\n if int(pic_index[a][j].split(\"/\")[-1].split(\",\")[0].split(\"_\")[1]) == k:\n col_index[k].append(pic_index[a][j])\n\n saver = []\n\n for i in range(max_col - 1):\n flag = concat_img_vertical(col_index[i])\n saver.append(flag)\n\n res = concat_temp_horizon(saver)\n res.save(result_dir + str(a) + '.jpg')\n\n\n\ndef image_to_patch(image_path, patch_size, patch_dir):\n '''\n cut an image into patch with certain size\n !!! Sibo, please complete this function !!!\n\n :param image_path: [string] path to the image(e.g. ./whole_image.jpg)\n :param patch_size: [tuple] i.g. (30(length),30(witch))\n :param patch_dir: [string] dir where to save the patch dir.(patch dir is defined to be the folder that contains all\n image patches of an image)\n :return:\n '''\n img = Image.open(image_path)\n (imageWidth, imageHeight) = img.size\n gridx = patch_size\n gridy = patch_size\n rangex = img.width / gridx\n rangey = img.height / gridy\n print rangex * rangey\n for x in xrange(rangex):\n for y in xrange(rangey):\n bbox = (x * gridx, y * gridy, x * gridx + gridx, y * gridy + gridy)\n slice_bit = img.crop(bbox)\n slice_bit.save(patch_dir + str(x) + '_' + str(y) + '.jpg', optimize=True,\n bits=6)\n print(patch_dir + str(x) + '_' + str(y) + '.jpg')\n print(imageWidth)\n\n\ndef directory_to_patch(patch_size,original_path,no_blur_path,blur_path,all_img_path):\n '''\n We take a directory that contains several whole pictures and cut then into custom size of patches,\n then apply 50% chance blur and non-blur to those patches, save them into blurry folder, non-blurry folder,\n and a folder that contains all blurry and non-blurry patches with order.\n :param patch_size: [integer] The custom patch size we want, e.g:for 30x30 patch, enter '30'\n :param original_path: [string] The original path that contains all the original pictures without any modification\n :param no_blur_path: [string] The destination path that contains all the non-blurry patches with order\n :param blur_path: [string] The destination path that contains all the blurry patches with order\n :param all_img_path: [string] The destination path that contains all the patches with order\n :return: There's no return in this function, all the modified patches are saved into the destination path\n '''\n #motion blur preset\n size = 15\n gridx = patch_size\n gridy = patch_size\n kernel_motion_blur = np.zeros((size, size))\n kernel_motion_blur[int((size - 1) / 2), :] = np.ones(size)\n kernel_motion_blur = kernel_motion_blur / size\n\n # go through every image in source folder\n print('begin loading images')\n pi_imgs = []\n cv_imgs = []\n valid_images = [\".jpg\"]\n for f in os.listdir(original_path):\n ext = os.path.splitext(f)[1]\n if ext.lower() not in valid_images:\n continue\n pi_imgs.append(Image.open(os.path.join(original_path, f)))\n cv_imgs.append(cv2.imread(os.path.join(original_path, f)))\n print('finished loading images')\n #\n\n # looping to create blurry and non-blurry images in 50% chance\n for i in range(len(pi_imgs)):\n img = pi_imgs[i]\n (imageWidth, imageHeight) = img.size\n\n rangex = imageWidth / gridx\n rangey = imageHeight / gridy\n for x in xrange(rangex):\n for y in xrange(rangey):\n\n bbox = (x * gridx, y * gridy, x * gridx + gridx, y * gridy + gridy)\n slice_bit = img.crop(bbox)\n if random.randrange(2) == 0:\n slice_bit.save(no_blur_path + str(i) + '_' + str(x) + '_' + str(y) + ',noblur.jpg', optimize=True,\n bits=6)\n slice_bit.save(all_img_path + str(i) + '_' + str(x) + '_' + str(y) + ',noblur.jpg', optimize=True,\n bits=6)\n print(str(i))\n else:\n slice_bit.save(blur_path + str(i) + '_' + str(x) + '_' + str(y) + ',blur.jpg', optimize=True, bits=6)\n img1 = cv2.imread(blur_path + str(i) + '_' + str(x) + '_' + str(y) + ',blur.jpg')\n output = cv2.filter2D(img1, -1, kernel_motion_blur)\n cv2.imwrite(blur_path + str(i) + '_' + str(x) + '_' + str(y) + ',blur.jpg', output)\n cv2.imwrite(all_img_path + str(i) + '_' + str(x) + '_' + str(y) + ',blur.jpg', output)\n print(str(i))\n\n" }, { "path": "slicing.py", "content": "from PIL import Image\nsave_path = \"./testing/\"\n\nimg = Image.open(\"/Users/sibozhu/DeepLearning/testing/JR.jpg\")\n(imageWidth, imageHeight)=img.size\ngridx=30\ngridy=30\nrangex=imageWidth/gridx\nprint('Width: '+str(rangex))\nrangey=imageHeight/gridy\nprint('Height: '+str(rangey))\nprint str(rangex*rangey) + \" patches in total\"\nfor x in xrange(rangex):\n for y in xrange(rangey):\n bbox=(x*gridx, y*gridy, x*gridx+gridx, y*gridy+gridy)\n slice_bit=img.crop(bbox)\n slice_bit.save(save_path+str(x)+'_'+str(y)+'.jpg', optimize=True, bits=6)\n # print (save_path+str(x)+'_'+str(y)+'.jpg')\nprint imageWidth\n\n" }, { "path": "test_img_generator.py", "content": "import numpy as np\r\nimport cv2\r\nimport random\r\nfrom PIL import Image\r\nimport os, os.path\r\n\r\n#\r\n# NOTE: this function creates new partly blured images to tarpath folder\r\n# and return a 3D-list labels which indicates blured parts in each image\r\n#\r\n# NOTE: please enter your correct folder path\r\n# NOTE: transpath is just a temp path that stores the blured copies of images\r\n# NOTE: Oripath is the source path and tarpath is where all the outputs are.\r\noripath = \"./s_oridata/\"\r\ntranspath = \"./transimg/\"\r\ntarpath = \"./tarimg/\"\r\n\r\n# global variables\r\nsize = 15\r\n\r\nlabels = []\r\n\r\ngridx = 30\r\ngridy = 30\r\n\r\nkernel_motion_blur = np.zeros((size, size))\r\nkernel_motion_blur[int((size-1)/2), :] = np.ones(size)\r\nkernel_motion_blur = kernel_motion_blur / size\r\n\r\n# go through every image in source folder\r\npi_imgs = []\r\ncv_imgs = []\r\nvalid_images = [\".jpg\"]\r\nfor f in os.listdir(oripath):\r\n ext = os.path.splitext(f)[1]\r\n if ext.lower() not in valid_images:\r\n continue\r\n pi_imgs.append(Image.open(os.path.join(oripath,f)))\r\n cv_imgs.append(cv2.imread(os.path.join(oripath,f)))\r\n\r\n\r\n# looping to create blured images\r\nfor i in range(len(pi_imgs)):\r\n # creating a blured copy of the original image\r\n temp = cv_imgs[i]\r\n blured = cv2.filter2D(temp, -1, kernel_motion_blur)\r\n cv2.imwrite(transpath+str(i)+\".jpg\",blured)\r\n \r\n # reload both original and blured version\r\n img1 = pi_imgs[i]\r\n img2 = Image.open(transpath+str(i)+\".jpg\")\r\n \r\n # cut the image into 30*30 pieces \r\n # and randomly choose half of the pieces to blur\r\n (imageWidth, imageHeight)=img1.size\r\n rangex=int(img1.width/gridx)\r\n rangey=int(img1.height/gridy)\r\n t_parts = rangex*rangey\r\n \r\n # creating labels that identifies which part is blured\r\n label = []\r\n for j in range(rangex):\r\n label += [[0]*rangey]\r\n \r\n \r\n for k in range(int(t_parts/2)):\r\n x = random.randint(0,rangex-1)\r\n y = random.randint(0,rangey-1)\r\n label[x][y] = 1\r\n \r\n box = (x*gridx,y*gridy,x*gridx+gridx,y*gridy+gridy)\r\n sliced = img2.crop(box)\r\n \r\n \r\n img1.paste(sliced, (x*gridx,y*gridy))\r\n \r\n img1.save(tarpath + str(i)+\".jpg\")\r\n \r\n labels += [label]\r\n\r\n\r\n\r\n \r\n \r\n \r\n \r\n \r\n" }, { "path": "trainingdatacreate.py", "content": "import numpy as np\nimport cv2\nimport random\nfrom PIL import Image\nimport os, os.path\n\noripath = \"./s_oridata/\"\nnoblurpath = \"./s_cnn/train/no_blur/\"\nblurpath = \"./s_cnn/train/blur/\"\nallimgpath = \"./s_cnn/train/inputdata/\"\n\nsize = 15\ngridx=30\ngridy=30\n\nkernel_motion_blur = np.zeros((size, size))\nkernel_motion_blur[int((size-1)/2), :] = np.ones(size)\nkernel_motion_blur = kernel_motion_blur / size\n\n# img = Image.open(oripath+\"JR.jpg\")\n\n\n#go through every image in source folder\nprint('begin loading images')\npi_imgs = []\ncv_imgs = []\nvalid_images = [\".jpg\"]\nfor f in os.listdir(oripath):\n ext = os.path.splitext(f)[1]\n if ext.lower() not in valid_images:\n continue\n pi_imgs.append(Image.open(os.path.join(oripath,f)))\n cv_imgs.append(cv2.imread(os.path.join(oripath,f)))\nprint('finished loading images')\n#\n\n#\n# looping to create blurry and non-blurry images in 50% chance\nfor i in range(len(pi_imgs)):\n img = pi_imgs[i]\n (imageWidth, imageHeight) = img.size\n\n rangex = imageWidth / gridx\n rangey = imageHeight / gridy\n for x in xrange(rangex):\n for y in xrange(rangey):\n\n bbox = (x * gridx, y * gridy, x * gridx + gridx, y * gridy + gridy)\n slice_bit = img.crop(bbox)\n if random.randrange(2) == 0:\n slice_bit.save(noblurpath + str(i)+'_'+ str(x) + '_' + str(y) + ',noblur.jpg', optimize=True, bits=6)\n slice_bit.save(allimgpath + str(i)+'_'+ str(x) + '_' + str(y) + ',noblur.jpg', optimize=True,bits=6)\n print(str(i))\n else:\n slice_bit.save(blurpath + str(i)+'_'+ str(x) + '_' + str(y) + ',blur.jpg', optimize=True, bits=6)\n img1 = cv2.imread(blurpath + str(i)+'_'+ str(x) + '_' + str(y) + ',blur.jpg')\n output = cv2.filter2D(img1, -1, kernel_motion_blur)\n cv2.imwrite(blurpath + str(i)+'_'+ str(x) + '_' + str(y) + ',blur.jpg', output)\n cv2.imwrite(allimgpath + str(i)+'_'+ str(x) + '_' + str(y) + ',blur.jpg', output)\n print(str(i))\n\n\n\n\n\n\n" } ]