Create run.py

run.py

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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
+
+"""
+run.py
+This script manage the entire transormation.
+Transformation happens in 6 phases:
+	0: dress -> correct [opencv] dress_to_correct
+	1: correct -> mask:  [GAN] correct_to_mask
+	2: mask -> maskref [opencv] mask_to_maskref
+	3: maskref -> maskdet [GAN] maskref_to_maskdet
+	4: maskdet -> maskfin [opencv] maskdet_to_maskfin
+	5: maskfin -> nude [GAN] maskfin_to_nude
+	6: nude -> watermark [opencv] nude_to_watermark
+"""
+
+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):
+
+	#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("Executing phase: " + 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"):
+				data_loader = DataLoader(opt, correct)
+			elif (phase == "maskref_to_maskdet"):
+				data_loader = DataLoader(opt, maskref)
+			elif (phase == "maskfin_to_nude"):
+				data_loader = DataLoader(opt, maskfin)
+			
+			dataset = data_loader.load_data()
+			
+			#Create Model
+			model = DeepModel()
+			model.initialize(opt)
+
+			#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 = create_correct(dress)
+
+		#mask_ref phase (opencv)
+		elif (phase == "mask_to_maskref"):
+			maskref = create_maskref(mask, correct)
+
+		#mask_fin phase (opencv)
+		elif (phase == "maskdet_to_maskfin"):
+			maskfin = create_maskfin(maskref, maskdet)
+
+		#nude_to_watermark phase (opencv)
+		elif (phase == "nude_to_watermark"):
+			watermark = create_watermark(nude)
+
+	return watermark