mirror of
https://github.com/deepfakes/faceswap
synced 2025-06-09 04:36:50 -04:00
27a685383e
* Separate predict and implement pool * Add check and raise error to multithreading Box functions to config. Add crop box option. * All masks to mask module. Refactor convert masks Predicted mask passed from model. Cli update * Intesect box with mask and fixes * Use raw NN output for convert Use raw mask for face adjustments. Split adjustments to pre and post warp * Separate out adjustments. Add unmask sharpen * Set sensible defaults. Pre PR Testing * Fix queue sizes. Move masked.py to lib * Fix Skip Frames. Fix GUI Config popup * Sensible queue limits. Add a less resource intensive single processing mode * Fix predicted mask. Amend smooth box defaults * Deterministic ordering for video output * Video to Video convert implemented * Fixups - Remove defaults from folders across all stages - Move match-hist and aca into color adjustments selectable - Handle crashes properly for pooled processes - Fix output directory does not exist error when creating a new output folder - Force output to frames if input is not a video * Add Color Transfer adjustment method Wrap info text in GUI plugin configure popup * Refactor image adjustments. Easier to create plugins Start implementing config options for video encoding * Add video encoding config options Allow video encoding for frames input (must pass in a reference video) Move video and image output writers to plugins * Image writers config options Move scaling to cli Move draw_transparent to images config Add config options for cv2 writer Add Pillow image writer * Add gif filetype to Pillow. Fix draw transparent for Pillow * Add Animated GIF writer standardize opencv/pillow defaults * [speedup] Pre-encode supported writers in the convert pool (opencv, pillow) Move scaling to convert pool Remove dfaker mask * Fix default writer * Bugfixes * Better custom argparse formatting
429 lines
19 KiB
Python
429 lines
19 KiB
Python
#!/usr/bin/env python3
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""" Process training data for model training """
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import logging
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from hashlib import sha1
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from random import shuffle, choice
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import cv2
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import numpy as np
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from scipy.interpolate import griddata
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from lib.model import masks
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from lib.multithreading import FixedProducerDispatcher
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from lib.queue_manager import queue_manager
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from lib.umeyama import umeyama
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logger = logging.getLogger(__name__) # pylint: disable=invalid-name
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class TrainingDataGenerator():
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""" Generate training data for models """
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def __init__(self, model_input_size, model_output_size, training_opts):
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logger.debug("Initializing %s: (model_input_size: %s, model_output_shape: %s, "
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"training_opts: %s, landmarks: %s)",
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self.__class__.__name__, model_input_size, model_output_size,
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{key: val for key, val in training_opts.items() if key != "landmarks"},
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bool(training_opts.get("landmarks", None)))
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self.batchsize = 0
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self.model_input_size = model_input_size
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self.model_output_size = model_output_size
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self.training_opts = training_opts
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self.mask_class = self.set_mask_class()
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self.landmarks = self.training_opts.get("landmarks", None)
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self._nearest_landmarks = None
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self.processing = ImageManipulation(model_input_size,
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model_output_size,
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training_opts.get("coverage_ratio", 0.625))
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logger.debug("Initialized %s", self.__class__.__name__)
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def set_mask_class(self):
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""" Set the mask function to use if using mask """
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mask_type = self.training_opts.get("mask_type", None)
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if mask_type:
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logger.debug("Mask type: '%s'", mask_type)
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mask_class = getattr(masks, mask_type)
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else:
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mask_class = None
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logger.debug("Mask class: %s", mask_class)
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return mask_class
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def minibatch_ab(self, images, batchsize, side, do_shuffle=True, is_timelapse=False):
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""" Keep a queue filled to 8x Batch Size """
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logger.debug("Queue batches: (image_count: %s, batchsize: %s, side: '%s', do_shuffle: %s, "
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"is_timelapse: %s)", len(images), batchsize, side, do_shuffle, is_timelapse)
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self.batchsize = batchsize
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queue_in, queue_out = self.make_queues(side, is_timelapse)
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training_size = self.training_opts.get("training_size", 256)
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batch_shape = list((
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(batchsize, training_size, training_size, 3), # sample images
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(batchsize, self.model_input_size, self.model_input_size, 3),
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(batchsize, self.model_output_size, self.model_output_size, 3)))
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if self.mask_class:
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batch_shape.append((self.batchsize, self.model_output_size, self.model_output_size, 1))
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load_process = FixedProducerDispatcher(
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method=self.load_batches,
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shapes=batch_shape,
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in_queue=queue_in,
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out_queue=queue_out,
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args=(images, side, is_timelapse, do_shuffle, batchsize))
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load_process.start()
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logger.debug("Batching to queue: (side: '%s', is_timelapse: %s)", side, is_timelapse)
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return self.minibatch(side, is_timelapse, load_process)
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@staticmethod
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def make_queues(side, is_timelapse):
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""" Create the buffer token queues for Fixed Producer Dispatcher """
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q_name = "timelapse_{}".format(side) if is_timelapse else "train_{}".format(side)
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q_names = ["{}_{}".format(q_name, direction) for direction in ("in", "out")]
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logger.debug(q_names)
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queues = [queue_manager.get_queue(queue) for queue in q_names]
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return queues
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def load_batches(self, mem_gen, images, side, is_timelapse,
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do_shuffle=True, batchsize=0):
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""" Load the warped images and target images to queue """
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logger.debug("Loading batch: (image_count: %s, side: '%s', is_timelapse: %s, "
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"do_shuffle: %s)", len(images), side, is_timelapse, do_shuffle)
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self.validate_samples(images)
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# Intialize this for each subprocess
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self._nearest_landmarks = dict()
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def _img_iter(imgs):
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while True:
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if do_shuffle:
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shuffle(imgs)
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for img in imgs:
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yield img
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img_iter = _img_iter(images)
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epoch = 0
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for memory_wrapper in mem_gen:
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memory = memory_wrapper.get()
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logger.trace("Putting to batch queue: (side: '%s', is_timelapse: %s)",
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side, is_timelapse)
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for i, img_path in enumerate(img_iter):
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imgs = self.process_face(img_path, side, is_timelapse)
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for j, img in enumerate(imgs):
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memory[j][i][:] = img
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epoch += 1
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if i == batchsize - 1:
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break
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memory_wrapper.ready()
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logger.debug("Finished batching: (epoch: %s, side: '%s', is_timelapse: %s)",
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epoch, side, is_timelapse)
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def validate_samples(self, data):
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""" Check the total number of images against batchsize and return
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the total number of images """
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length = len(data)
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msg = ("Number of images is lower than batch-size (Note that too few "
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"images may lead to bad training). # images: {}, "
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"batch-size: {}".format(length, self.batchsize))
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assert length >= self.batchsize, msg
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@staticmethod
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def minibatch(side, is_timelapse, load_process):
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""" A generator function that yields epoch, batchsize of warped_img
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and batchsize of target_img from the load queue """
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logger.debug("Launching minibatch generator for queue (side: '%s', is_timelapse: %s)",
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side, is_timelapse)
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for batch_wrapper in load_process:
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with batch_wrapper as batch:
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logger.trace("Yielding batch: (size: %s, item shapes: %s, side: '%s', "
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"is_timelapse: %s)",
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len(batch), [item.shape for item in batch], side, is_timelapse)
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yield batch
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load_process.stop()
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logger.debug("Finished minibatch generator for queue: (side: '%s', is_timelapse: %s)",
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side, is_timelapse)
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load_process.join()
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def process_face(self, filename, side, is_timelapse):
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""" Load an image and perform transformation and warping """
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logger.trace("Process face: (filename: '%s', side: '%s', is_timelapse: %s)",
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filename, side, is_timelapse)
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try:
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image = cv2.imread(filename) # pylint: disable=no-member
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except TypeError:
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raise Exception("Error while reading image", filename)
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if self.mask_class or self.training_opts["warp_to_landmarks"]:
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src_pts = self.get_landmarks(filename, image, side)
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if self.mask_class:
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image = self.mask_class(src_pts, image, channels=4).mask
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image = self.processing.color_adjust(image)
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if not is_timelapse:
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image = self.processing.random_transform(image)
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if not self.training_opts["no_flip"]:
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image = self.processing.do_random_flip(image)
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sample = image.copy()[:, :, :3]
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if self.training_opts["warp_to_landmarks"]:
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dst_pts = self.get_closest_match(filename, side, src_pts)
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processed = self.processing.random_warp_landmarks(image, src_pts, dst_pts)
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else:
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processed = self.processing.random_warp(image)
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processed.insert(0, sample)
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logger.trace("Processed face: (filename: '%s', side: '%s', shapes: %s)",
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filename, side, [img.shape for img in processed])
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return processed
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def get_landmarks(self, filename, image, side):
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""" Return the landmarks for this face """
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logger.trace("Retrieving landmarks: (filename: '%s', side: '%s'", filename, side)
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lm_key = sha1(image).hexdigest()
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try:
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src_points = self.landmarks[side][lm_key]
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except KeyError:
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raise Exception("Landmarks not found for hash: '{}' file: '{}'".format(lm_key,
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filename))
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logger.trace("Returning: (src_points: %s)", src_points)
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return src_points
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def get_closest_match(self, filename, side, src_points):
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""" Return closest matched landmarks from opposite set """
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logger.trace("Retrieving closest matched landmarks: (filename: '%s', src_points: '%s'",
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filename, src_points)
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landmarks = self.landmarks["a"] if side == "b" else self.landmarks["b"]
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closest_hashes = self._nearest_landmarks.get(filename)
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if not closest_hashes:
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dst_points_items = list(landmarks.items())
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dst_points = list(x[1] for x in dst_points_items)
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closest = (np.mean(np.square(src_points - dst_points),
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axis=(1, 2))).argsort()[:10]
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closest_hashes = tuple(dst_points_items[i][0] for i in closest)
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self._nearest_landmarks[filename] = closest_hashes
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dst_points = landmarks[choice(closest_hashes)]
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logger.trace("Returning: (dst_points: %s)", dst_points)
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return dst_points
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class ImageManipulation():
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""" Manipulations to be performed on training images """
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def __init__(self, input_size, output_size, coverage_ratio):
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""" input_size: Size of the face input into the model
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output_size: Size of the face that comes out of the modell
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coverage_ratio: Coverage ratio of full image. Eg: 256 * 0.625 = 160
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"""
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logger.debug("Initializing %s: (input_size: %s, output_size: %s, coverage_ratio: %s)",
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self.__class__.__name__, input_size, output_size, coverage_ratio)
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# Transform args
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self.rotation_range = 10 # Range to randomly rotate the image by
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self.zoom_range = 0.05 # Range to randomly zoom the image by
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self.shift_range = 0.05 # Range to randomly translate the image by
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self.random_flip = 0.5 # Chance to flip the image horizontally
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# Transform and Warp args
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self.input_size = input_size
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self.output_size = output_size
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# Warp args
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self.coverage_ratio = coverage_ratio # Coverage ratio of full image. Eg: 256 * 0.625 = 160
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self.scale = 5 # Normal random variable scale
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logger.debug("Initialized %s", self.__class__.__name__)
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@staticmethod
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def color_adjust(img):
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""" Color adjust RGB image """
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logger.trace("Color adjusting image")
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return img.astype('float32') / 255.0
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@staticmethod
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def separate_mask(image):
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""" Return the image and the mask from a 4 channel image """
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mask = None
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if image.shape[2] == 4:
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logger.trace("Image contains mask")
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mask = np.expand_dims(image[:, :, -1], axis=2)
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image = image[:, :, :3]
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else:
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logger.trace("Image has no mask")
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return image, mask
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def get_coverage(self, image):
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""" Return coverage value for given image """
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coverage = int(image.shape[0] * self.coverage_ratio)
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logger.trace("Coverage: %s", coverage)
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return coverage
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def random_transform(self, image):
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""" Randomly transform an image """
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logger.trace("Randomly transforming image")
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height, width = image.shape[0:2]
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rotation = np.random.uniform(-self.rotation_range, self.rotation_range)
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scale = np.random.uniform(1 - self.zoom_range, 1 + self.zoom_range)
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tnx = np.random.uniform(-self.shift_range, self.shift_range) * width
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tny = np.random.uniform(-self.shift_range, self.shift_range) * height
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mat = cv2.getRotationMatrix2D( # pylint: disable=no-member
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(width // 2, height // 2), rotation, scale)
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mat[:, 2] += (tnx, tny)
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result = cv2.warpAffine( # pylint: disable=no-member
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image, mat, (width, height),
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borderMode=cv2.BORDER_REPLICATE) # pylint: disable=no-member
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logger.trace("Randomly transformed image")
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return result
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def do_random_flip(self, image):
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""" Perform flip on image if random number is within threshold """
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logger.trace("Randomly flipping image")
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if np.random.random() < self.random_flip:
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logger.trace("Flip within threshold. Flipping")
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retval = image[:, ::-1]
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else:
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logger.trace("Flip outside threshold. Not Flipping")
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retval = image
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logger.trace("Randomly flipped image")
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return retval
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def random_warp(self, image):
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""" get pair of random warped images from aligned face image """
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logger.trace("Randomly warping image")
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height, width = image.shape[0:2]
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coverage = self.get_coverage(image)
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assert height == width and height % 2 == 0
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range_ = np.linspace(height // 2 - coverage // 2,
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height // 2 + coverage // 2,
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5, dtype='float32')
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mapx = np.broadcast_to(range_, (5, 5)).copy()
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mapy = mapx.T
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# mapx, mapy = np.float32(np.meshgrid(range_,range_)) # instead of broadcast
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pad = int(1.25 * self.input_size)
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slices = slice(pad // 10, -pad // 10)
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dst_slice = slice(0, (self.output_size + 1), (self.output_size // 4))
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interp = np.empty((2, self.input_size, self.input_size), dtype='float32')
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####
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for i, map_ in enumerate([mapx, mapy]):
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map_ = map_ + np.random.normal(size=(5, 5), scale=self.scale)
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interp[i] = cv2.resize(map_, (pad, pad))[slices, slices] # pylint: disable=no-member
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warped_image = cv2.remap( # pylint: disable=no-member
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image, interp[0], interp[1], cv2.INTER_LINEAR) # pylint: disable=no-member
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logger.trace("Warped image shape: %s", warped_image.shape)
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src_points = np.stack([mapx.ravel(), mapy.ravel()], axis=-1)
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dst_points = np.mgrid[dst_slice, dst_slice]
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mat = umeyama(src_points, True, dst_points.T.reshape(-1, 2))[0:2]
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target_image = cv2.warpAffine( # pylint: disable=no-member
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image, mat, (self.output_size, self.output_size))
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logger.trace("Target image shape: %s", target_image.shape)
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warped_image, warped_mask = self.separate_mask(warped_image)
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target_image, target_mask = self.separate_mask(target_image)
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if target_mask is None:
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logger.trace("Randomly warped image")
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return [warped_image, target_image]
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logger.trace("Target mask shape: %s", target_mask.shape)
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logger.trace("Randomly warped image and mask")
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return [warped_image, target_image, target_mask]
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def random_warp_landmarks(self, image, src_points=None, dst_points=None):
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""" get warped image, target image and target mask
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From DFAKER plugin """
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logger.trace("Randomly warping landmarks")
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size = image.shape[0]
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coverage = self.get_coverage(image)
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p_mx = size - 1
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p_hf = (size // 2) - 1
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edge_anchors = [(0, 0), (0, p_mx), (p_mx, p_mx), (p_mx, 0),
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(p_hf, 0), (p_hf, p_mx), (p_mx, p_hf), (0, p_hf)]
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grid_x, grid_y = np.mgrid[0:p_mx:complex(size), 0:p_mx:complex(size)]
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source = src_points
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destination = (dst_points.copy().astype('float32') +
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np.random.normal(size=dst_points.shape, scale=2.0))
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destination = destination.astype('uint8')
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face_core = cv2.convexHull(np.concatenate( # pylint: disable=no-member
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[source[17:], destination[17:]], axis=0).astype(int))
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source = [(pty, ptx) for ptx, pty in source] + edge_anchors
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destination = [(pty, ptx) for ptx, pty in destination] + edge_anchors
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indicies_to_remove = set()
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for fpl in source, destination:
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for idx, (pty, ptx) in enumerate(fpl):
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if idx > 17:
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break
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elif cv2.pointPolygonTest(face_core, # pylint: disable=no-member
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(pty, ptx),
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False) >= 0:
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indicies_to_remove.add(idx)
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for idx in sorted(indicies_to_remove, reverse=True):
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source.pop(idx)
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destination.pop(idx)
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grid_z = griddata(destination, source, (grid_x, grid_y), method="linear")
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map_x = np.append([], [ar[:, 1] for ar in grid_z]).reshape(size, size)
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map_y = np.append([], [ar[:, 0] for ar in grid_z]).reshape(size, size)
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map_x_32 = map_x.astype('float32')
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map_y_32 = map_y.astype('float32')
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warped_image = cv2.remap(image, # pylint: disable=no-member
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map_x_32,
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map_y_32,
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cv2.INTER_LINEAR, # pylint: disable=no-member
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cv2.BORDER_TRANSPARENT) # pylint: disable=no-member
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target_image = image
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# TODO Make sure this replacement is correct
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slices = slice(size // 2 - coverage // 2, size // 2 + coverage // 2)
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# slices = slice(size // 32, size - size // 32) # 8px on a 256px image
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warped_image = cv2.resize( # pylint: disable=no-member
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warped_image[slices, slices, :], (self.input_size, self.input_size),
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cv2.INTER_AREA) # pylint: disable=no-member
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logger.trace("Warped image shape: %s", warped_image.shape)
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target_image = cv2.resize( # pylint: disable=no-member
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target_image[slices, slices, :], (self.output_size, self.output_size),
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cv2.INTER_AREA) # pylint: disable=no-member
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logger.trace("Target image shape: %s", target_image.shape)
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warped_image, warped_mask = self.separate_mask(warped_image)
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target_image, target_mask = self.separate_mask(target_image)
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if target_mask is None:
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logger.trace("Randomly warped image")
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|
return [warped_image, target_image]
|
|
|
|
logger.trace("Target mask shape: %s", target_mask.shape)
|
|
logger.trace("Randomly warped image and mask")
|
|
return [warped_image, target_image, target_mask]
|
|
|
|
|
|
def stack_images(images):
|
|
""" Stack images """
|
|
logger.debug("Stack images")
|
|
|
|
def get_transpose_axes(num):
|
|
if num % 2 == 0:
|
|
logger.debug("Even number of images to stack")
|
|
y_axes = list(range(1, num - 1, 2))
|
|
x_axes = list(range(0, num - 1, 2))
|
|
else:
|
|
logger.debug("Odd number of images to stack")
|
|
y_axes = list(range(0, num - 1, 2))
|
|
x_axes = list(range(1, num - 1, 2))
|
|
return y_axes, x_axes, [num - 1]
|
|
|
|
images_shape = np.array(images.shape)
|
|
new_axes = get_transpose_axes(len(images_shape))
|
|
new_shape = [np.prod(images_shape[x]) for x in new_axes]
|
|
logger.debug("Stacked images")
|
|
return np.transpose(
|
|
images,
|
|
axes=np.concatenate(new_axes)
|
|
).reshape(new_shape)
|