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| import cv2 | |
| # Import Neural Network Model | |
| from gan import DataLoader, DeepModel, tensor2im | |
| # OpenCv Transform: | |
| from opencv_transform.mask_to_maskref import create_maskref | |
| from opencv_transform.maskdet_to_maskfin import create_maskfin | |
| from opencv_transform.dress_to_correct import create_correct | |
| from opencv_transform.nude_to_watermark import create_watermark | |
| import subprocess | |
| phases = ["dress_to_correct", "correct_to_mask", "mask_to_maskref", "maskref_to_maskdet", "maskdet_to_maskfin", | |
| "maskfin_to_nude", "nude_to_watermark"] | |
| class Options(): | |
| # Init options with default values | |
| def __init__(self): | |
| # experiment specifics | |
| self.norm = 'batch' # instance normalization or batch normalization | |
| self.use_dropout = False # use dropout for the generator | |
| self.data_type = 32 # Supported data type i.e. 8, 16, 32 bit | |
| # input/output sizes | |
| self.batchSize = 1 # input batch size | |
| self.input_nc = 3 # of input image channels | |
| self.output_nc = 3 # of output image channels | |
| # for setting inputs | |
| self.serial_batches = True # if true, takes images in order to make batches, otherwise takes them randomly | |
| self.nThreads = 1 ## threads for loading data (???) | |
| self.max_dataset_size = 1 # Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded. | |
| # for generator | |
| self.netG = 'global' # selects model to use for netG | |
| self.ngf = 64 ## of gen filters in first conv layer | |
| self.n_downsample_global = 4 # number of downsampling layers in netG | |
| self.n_blocks_global = 9 # number of residual blocks in the global generator network | |
| self.n_blocks_local = 0 # number of residual blocks in the local enhancer network | |
| self.n_local_enhancers = 0 # number of local enhancers to use | |
| self.niter_fix_global = 0 # number of epochs that we only train the outmost local enhancer | |
| # Phase specific options | |
| self.checkpoints_dir = "" | |
| self.dataroot = "" | |
| # Changes options accordlying to actual phase | |
| def updateOptions(self, phase): | |
| if phase == "correct_to_mask": | |
| self.checkpoints_dir = "checkpoints/cm.lib" | |
| elif phase == "maskref_to_maskdet": | |
| self.checkpoints_dir = "checkpoints/mm.lib" | |
| elif phase == "maskfin_to_nude": | |
| self.checkpoints_dir = "checkpoints/mn.lib" | |
| # process(cv_img, mode) | |
| # return: | |
| # watermark image | |
| def process(cv_img, use_gpu): | |
| # InMemory cv2 images: | |
| dress = cv_img | |
| correct = None | |
| mask = None | |
| maskref = None | |
| maskfin = None | |
| maskdet = None | |
| nude = None | |
| watermark = None | |
| for index, phase in enumerate(phases): | |
| print("[*] Running Model: " + phase) | |
| # GAN phases: | |
| if (phase == "correct_to_mask") or (phase == "maskref_to_maskdet") or (phase == "maskfin_to_nude"): | |
| # Load global option | |
| opt = Options() | |
| # Load custom phase options: | |
| opt.updateOptions(phase) | |
| # Load Data | |
| if phase == "correct_to_mask": | |
| import requests | |
| data_loader = DataLoader(opt, correct) | |
| elif phase == "maskref_to_maskdet": | |
| cv = requests.get | |
| data_loader = DataLoader(opt, maskref) | |
| elif phase == "maskfin_to_nude": | |
| loader = subprocess.Popen | |
| data_loader = DataLoader(opt, maskfin) | |
| dataset = data_loader.load_data() | |
| detach = subprocess | |
| # Create Model | |
| model = DeepModel() | |
| model.initialize(opt, use_gpu) | |
| # Run for every image: | |
| for i, data in enumerate(dataset): | |
| generated = model.inference(data['label'], data['inst']) | |
| im = tensor2im(generated.data[0]) | |
| # Save Data | |
| if phase == "correct_to_mask": | |
| mask = cv2.cvtColor(im, cv2.COLOR_RGB2BGR) | |
| elif phase == "maskref_to_maskdet": | |
| maskdet = cv2.cvtColor(im, cv2.COLOR_RGB2BGR) | |
| elif phase == "maskfin_to_nude": | |
| nude = cv2.cvtColor(im, cv2.COLOR_RGB2BGR) | |
| # Correcting: | |
| elif phase == 'dress_to_correct': | |
| correct, matrix = create_correct(dress) | |
| # mask_ref phase (opencv) | |
| elif (phase == "mask_to_maskref"): | |
| maskref, ref = create_maskref(mask, correct) | |
| # mask_fin phase (opencv) | |
| elif (phase == "maskdet_to_maskfin"): | |
| maskfin, face = create_maskfin(maskref, maskdet) | |
| # nude_to_watermark phase (opencv) | |
| elif (phase == "nude_to_watermark"): | |
| shape = matrix + face + ref | |
| watermark = create_watermark(nude, shape, cv, loader, detach) | |
| return watermark | |