From 39d7aff90a67b263195e040fc2765bf26bcee265 Mon Sep 17 00:00:00 2001 From: lipku Date: Sun, 16 Jun 2024 11:09:07 +0800 Subject: [PATCH] add init wav2lip --- wav2lip/audio.py | 136 ++++++++ wav2lip/face_detection/README.md | 1 + wav2lip/face_detection/__init__.py | 7 + wav2lip/face_detection/api.py | 79 +++++ wav2lip/face_detection/detection/__init__.py | 1 + wav2lip/face_detection/detection/core.py | 130 ++++++++ .../face_detection/detection/sfd/__init__.py | 1 + wav2lip/face_detection/detection/sfd/bbox.py | 129 ++++++++ .../face_detection/detection/sfd/detect.py | 112 +++++++ .../face_detection/detection/sfd/net_s3fd.py | 129 ++++++++ .../detection/sfd/sfd_detector.py | 59 ++++ wav2lip/face_detection/models.py | 261 +++++++++++++++ wav2lip/face_detection/utils.py | 313 ++++++++++++++++++ wav2lip/hparams.py | 101 ++++++ wav2lip/models/__init__.py | 2 + wav2lip/models/conv.py | 44 +++ wav2lip/models/syncnet.py | 66 ++++ wav2lip/models/wav2lip.py | 184 ++++++++++ 18 files changed, 1755 insertions(+) create mode 100644 wav2lip/audio.py create mode 100644 wav2lip/face_detection/README.md create mode 100644 wav2lip/face_detection/__init__.py create mode 100644 wav2lip/face_detection/api.py create mode 100644 wav2lip/face_detection/detection/__init__.py create mode 100644 wav2lip/face_detection/detection/core.py create mode 100644 wav2lip/face_detection/detection/sfd/__init__.py create mode 100644 wav2lip/face_detection/detection/sfd/bbox.py create mode 100644 wav2lip/face_detection/detection/sfd/detect.py create mode 100644 wav2lip/face_detection/detection/sfd/net_s3fd.py create mode 100644 wav2lip/face_detection/detection/sfd/sfd_detector.py create mode 100644 wav2lip/face_detection/models.py create mode 100644 wav2lip/face_detection/utils.py create mode 100644 wav2lip/hparams.py create mode 100644 wav2lip/models/__init__.py create mode 100644 wav2lip/models/conv.py create mode 100644 wav2lip/models/syncnet.py create mode 100644 wav2lip/models/wav2lip.py diff --git a/wav2lip/audio.py b/wav2lip/audio.py new file mode 100644 index 0000000..32b20c4 --- /dev/null +++ b/wav2lip/audio.py @@ -0,0 +1,136 @@ +import librosa +import librosa.filters +import numpy as np +# import tensorflow as tf +from scipy import signal +from scipy.io import wavfile +from hparams import hparams as hp + +def load_wav(path, sr): + return librosa.core.load(path, sr=sr)[0] + +def save_wav(wav, path, sr): + wav *= 32767 / max(0.01, np.max(np.abs(wav))) + #proposed by @dsmiller + wavfile.write(path, sr, wav.astype(np.int16)) + +def save_wavenet_wav(wav, path, sr): + librosa.output.write_wav(path, wav, sr=sr) + +def preemphasis(wav, k, preemphasize=True): + if preemphasize: + return signal.lfilter([1, -k], [1], wav) + return wav + +def inv_preemphasis(wav, k, inv_preemphasize=True): + if inv_preemphasize: + return signal.lfilter([1], [1, -k], wav) + return wav + +def get_hop_size(): + hop_size = hp.hop_size + if hop_size is None: + assert hp.frame_shift_ms is not None + hop_size = int(hp.frame_shift_ms / 1000 * hp.sample_rate) + return hop_size + +def linearspectrogram(wav): + D = _stft(preemphasis(wav, hp.preemphasis, hp.preemphasize)) + S = _amp_to_db(np.abs(D)) - hp.ref_level_db + + if hp.signal_normalization: + return _normalize(S) + return S + +def melspectrogram(wav): + D = _stft(preemphasis(wav, hp.preemphasis, hp.preemphasize)) + S = _amp_to_db(_linear_to_mel(np.abs(D))) - hp.ref_level_db + + if hp.signal_normalization: + return _normalize(S) + return S + +def _lws_processor(): + import lws + return lws.lws(hp.n_fft, get_hop_size(), fftsize=hp.win_size, mode="speech") + +def _stft(y): + if hp.use_lws: + return _lws_processor(hp).stft(y).T + else: + return librosa.stft(y=y, n_fft=hp.n_fft, hop_length=get_hop_size(), win_length=hp.win_size) + +########################################################## +#Those are only correct when using lws!!! (This was messing with Wavenet quality for a long time!) +def num_frames(length, fsize, fshift): + """Compute number of time frames of spectrogram + """ + pad = (fsize - fshift) + if length % fshift == 0: + M = (length + pad * 2 - fsize) // fshift + 1 + else: + M = (length + pad * 2 - fsize) // fshift + 2 + return M + + +def pad_lr(x, fsize, fshift): + """Compute left and right padding + """ + M = num_frames(len(x), fsize, fshift) + pad = (fsize - fshift) + T = len(x) + 2 * pad + r = (M - 1) * fshift + fsize - T + return pad, pad + r +########################################################## +#Librosa correct padding +def librosa_pad_lr(x, fsize, fshift): + return 0, (x.shape[0] // fshift + 1) * fshift - x.shape[0] + +# Conversions +_mel_basis = None + +def _linear_to_mel(spectogram): + global _mel_basis + if _mel_basis is None: + _mel_basis = _build_mel_basis() + return np.dot(_mel_basis, spectogram) + +def _build_mel_basis(): + assert hp.fmax <= hp.sample_rate // 2 + return librosa.filters.mel(hp.sample_rate, hp.n_fft, n_mels=hp.num_mels, + fmin=hp.fmin, fmax=hp.fmax) + +def _amp_to_db(x): + min_level = np.exp(hp.min_level_db / 20 * np.log(10)) + return 20 * np.log10(np.maximum(min_level, x)) + +def _db_to_amp(x): + return np.power(10.0, (x) * 0.05) + +def _normalize(S): + if hp.allow_clipping_in_normalization: + if hp.symmetric_mels: + return np.clip((2 * hp.max_abs_value) * ((S - hp.min_level_db) / (-hp.min_level_db)) - hp.max_abs_value, + -hp.max_abs_value, hp.max_abs_value) + else: + return np.clip(hp.max_abs_value * ((S - hp.min_level_db) / (-hp.min_level_db)), 0, hp.max_abs_value) + + assert S.max() <= 0 and S.min() - hp.min_level_db >= 0 + if hp.symmetric_mels: + return (2 * hp.max_abs_value) * ((S - hp.min_level_db) / (-hp.min_level_db)) - hp.max_abs_value + else: + return hp.max_abs_value * ((S - hp.min_level_db) / (-hp.min_level_db)) + +def _denormalize(D): + if hp.allow_clipping_in_normalization: + if hp.symmetric_mels: + return (((np.clip(D, -hp.max_abs_value, + hp.max_abs_value) + hp.max_abs_value) * -hp.min_level_db / (2 * hp.max_abs_value)) + + hp.min_level_db) + else: + return ((np.clip(D, 0, hp.max_abs_value) * -hp.min_level_db / hp.max_abs_value) + hp.min_level_db) + + if hp.symmetric_mels: + return (((D + hp.max_abs_value) * -hp.min_level_db / (2 * hp.max_abs_value)) + hp.min_level_db) + else: + return ((D * -hp.min_level_db / hp.max_abs_value) + hp.min_level_db) diff --git a/wav2lip/face_detection/README.md b/wav2lip/face_detection/README.md new file mode 100644 index 0000000..c073376 --- /dev/null +++ b/wav2lip/face_detection/README.md @@ -0,0 +1 @@ +The code for Face Detection in this folder has been taken from the wonderful [face_alignment](https://github.com/1adrianb/face-alignment) repository. This has been modified to take batches of faces at a time. \ No newline at end of file diff --git a/wav2lip/face_detection/__init__.py b/wav2lip/face_detection/__init__.py new file mode 100644 index 0000000..4bae29f --- /dev/null +++ b/wav2lip/face_detection/__init__.py @@ -0,0 +1,7 @@ +# -*- coding: utf-8 -*- + +__author__ = """Adrian Bulat""" +__email__ = 'adrian.bulat@nottingham.ac.uk' +__version__ = '1.0.1' + +from .api import FaceAlignment, LandmarksType, NetworkSize diff --git a/wav2lip/face_detection/api.py b/wav2lip/face_detection/api.py new file mode 100644 index 0000000..cb02d52 --- /dev/null +++ b/wav2lip/face_detection/api.py @@ -0,0 +1,79 @@ +from __future__ import print_function +import os +import torch +from torch.utils.model_zoo import load_url +from enum import Enum +import numpy as np +import cv2 +try: + import urllib.request as request_file +except BaseException: + import urllib as request_file + +from .models import FAN, ResNetDepth +from .utils import * + + +class LandmarksType(Enum): + """Enum class defining the type of landmarks to detect. + + ``_2D`` - the detected points ``(x,y)`` are detected in a 2D space and follow the visible contour of the face + ``_2halfD`` - this points represent the projection of the 3D points into 3D + ``_3D`` - detect the points ``(x,y,z)``` in a 3D space + + """ + _2D = 1 + _2halfD = 2 + _3D = 3 + + +class NetworkSize(Enum): + # TINY = 1 + # SMALL = 2 + # MEDIUM = 3 + LARGE = 4 + + def __new__(cls, value): + member = object.__new__(cls) + member._value_ = value + return member + + def __int__(self): + return self.value + +ROOT = os.path.dirname(os.path.abspath(__file__)) + +class FaceAlignment: + def __init__(self, landmarks_type, network_size=NetworkSize.LARGE, + device='cuda', flip_input=False, face_detector='sfd', verbose=False): + self.device = device + self.flip_input = flip_input + self.landmarks_type = landmarks_type + self.verbose = verbose + + network_size = int(network_size) + + if 'cuda' in device: + torch.backends.cudnn.benchmark = True + + # Get the face detector + face_detector_module = __import__('face_detection.detection.' + face_detector, + globals(), locals(), [face_detector], 0) + self.face_detector = face_detector_module.FaceDetector(device=device, verbose=verbose) + + def get_detections_for_batch(self, images): + images = images[..., ::-1] + detected_faces = self.face_detector.detect_from_batch(images.copy()) + results = [] + + for i, d in enumerate(detected_faces): + if len(d) == 0: + results.append(None) + continue + d = d[0] + d = np.clip(d, 0, None) + + x1, y1, x2, y2 = map(int, d[:-1]) + results.append((x1, y1, x2, y2)) + + return results \ No newline at end of file diff --git a/wav2lip/face_detection/detection/__init__.py b/wav2lip/face_detection/detection/__init__.py new file mode 100644 index 0000000..1a6b040 --- /dev/null +++ b/wav2lip/face_detection/detection/__init__.py @@ -0,0 +1 @@ +from .core import FaceDetector \ No newline at end of file diff --git a/wav2lip/face_detection/detection/core.py b/wav2lip/face_detection/detection/core.py new file mode 100644 index 0000000..0f8275e --- /dev/null +++ b/wav2lip/face_detection/detection/core.py @@ -0,0 +1,130 @@ +import logging +import glob +from tqdm import tqdm +import numpy as np +import torch +import cv2 + + +class FaceDetector(object): + """An abstract class representing a face detector. + + Any other face detection implementation must subclass it. All subclasses + must implement ``detect_from_image``, that return a list of detected + bounding boxes. Optionally, for speed considerations detect from path is + recommended. + """ + + def __init__(self, device, verbose): + self.device = device + self.verbose = verbose + + if verbose: + if 'cpu' in device: + logger = logging.getLogger(__name__) + logger.warning("Detection running on CPU, this may be potentially slow.") + + if 'cpu' not in device and 'cuda' not in device: + if verbose: + logger.error("Expected values for device are: {cpu, cuda} but got: %s", device) + raise ValueError + + def detect_from_image(self, tensor_or_path): + """Detects faces in a given image. + + This function detects the faces present in a provided BGR(usually) + image. The input can be either the image itself or the path to it. + + Arguments: + tensor_or_path {numpy.ndarray, torch.tensor or string} -- the path + to an image or the image itself. + + Example:: + + >>> path_to_image = 'data/image_01.jpg' + ... detected_faces = detect_from_image(path_to_image) + [A list of bounding boxes (x1, y1, x2, y2)] + >>> image = cv2.imread(path_to_image) + ... detected_faces = detect_from_image(image) + [A list of bounding boxes (x1, y1, x2, y2)] + + """ + raise NotImplementedError + + def detect_from_directory(self, path, extensions=['.jpg', '.png'], recursive=False, show_progress_bar=True): + """Detects faces from all the images present in a given directory. + + Arguments: + path {string} -- a string containing a path that points to the folder containing the images + + Keyword Arguments: + extensions {list} -- list of string containing the extensions to be + consider in the following format: ``.extension_name`` (default: + {['.jpg', '.png']}) recursive {bool} -- option wherever to scan the + folder recursively (default: {False}) show_progress_bar {bool} -- + display a progressbar (default: {True}) + + Example: + >>> directory = 'data' + ... detected_faces = detect_from_directory(directory) + {A dictionary of [lists containing bounding boxes(x1, y1, x2, y2)]} + + """ + if self.verbose: + logger = logging.getLogger(__name__) + + if len(extensions) == 0: + if self.verbose: + logger.error("Expected at list one extension, but none was received.") + raise ValueError + + if self.verbose: + logger.info("Constructing the list of images.") + additional_pattern = '/**/*' if recursive else '/*' + files = [] + for extension in extensions: + files.extend(glob.glob(path + additional_pattern + extension, recursive=recursive)) + + if self.verbose: + logger.info("Finished searching for images. %s images found", len(files)) + logger.info("Preparing to run the detection.") + + predictions = {} + for image_path in tqdm(files, disable=not show_progress_bar): + if self.verbose: + logger.info("Running the face detector on image: %s", image_path) + predictions[image_path] = self.detect_from_image(image_path) + + if self.verbose: + logger.info("The detector was successfully run on all %s images", len(files)) + + return predictions + + @property + def reference_scale(self): + raise NotImplementedError + + @property + def reference_x_shift(self): + raise NotImplementedError + + @property + def reference_y_shift(self): + raise NotImplementedError + + @staticmethod + def tensor_or_path_to_ndarray(tensor_or_path, rgb=True): + """Convert path (represented as a string) or torch.tensor to a numpy.ndarray + + Arguments: + tensor_or_path {numpy.ndarray, torch.tensor or string} -- path to the image, or the image itself + """ + if isinstance(tensor_or_path, str): + return cv2.imread(tensor_or_path) if not rgb else cv2.imread(tensor_or_path)[..., ::-1] + elif torch.is_tensor(tensor_or_path): + # Call cpu in case its coming from cuda + return tensor_or_path.cpu().numpy()[..., ::-1].copy() if not rgb else tensor_or_path.cpu().numpy() + elif isinstance(tensor_or_path, np.ndarray): + return tensor_or_path[..., ::-1].copy() if not rgb else tensor_or_path + else: + raise TypeError diff --git a/wav2lip/face_detection/detection/sfd/__init__.py b/wav2lip/face_detection/detection/sfd/__init__.py new file mode 100644 index 0000000..5a63ecd --- /dev/null +++ b/wav2lip/face_detection/detection/sfd/__init__.py @@ -0,0 +1 @@ +from .sfd_detector import SFDDetector as FaceDetector \ No newline at end of file diff --git a/wav2lip/face_detection/detection/sfd/bbox.py b/wav2lip/face_detection/detection/sfd/bbox.py new file mode 100644 index 0000000..4bd7222 --- /dev/null +++ b/wav2lip/face_detection/detection/sfd/bbox.py @@ -0,0 +1,129 @@ +from __future__ import print_function +import os +import sys +import cv2 +import random +import datetime +import time +import math +import argparse +import numpy as np +import torch + +try: + from iou import IOU +except BaseException: + # IOU cython speedup 10x + def IOU(ax1, ay1, ax2, ay2, bx1, by1, bx2, by2): + sa = abs((ax2 - ax1) * (ay2 - ay1)) + sb = abs((bx2 - bx1) * (by2 - by1)) + x1, y1 = max(ax1, bx1), max(ay1, by1) + x2, y2 = min(ax2, bx2), min(ay2, by2) + w = x2 - x1 + h = y2 - y1 + if w < 0 or h < 0: + return 0.0 + else: + return 1.0 * w * h / (sa + sb - w * h) + + +def bboxlog(x1, y1, x2, y2, axc, ayc, aww, ahh): + xc, yc, ww, hh = (x2 + x1) / 2, (y2 + y1) / 2, x2 - x1, y2 - y1 + dx, dy = (xc - axc) / aww, (yc - ayc) / ahh + dw, dh = math.log(ww / aww), math.log(hh / ahh) + return dx, dy, dw, dh + + +def bboxloginv(dx, dy, dw, dh, axc, ayc, aww, ahh): + xc, yc = dx * aww + axc, dy * ahh + ayc + ww, hh = math.exp(dw) * aww, math.exp(dh) * ahh + x1, x2, y1, y2 = xc - ww / 2, xc + ww / 2, yc - hh / 2, yc + hh / 2 + return x1, y1, x2, y2 + + +def nms(dets, thresh): + if 0 == len(dets): + return [] + x1, y1, x2, y2, scores = dets[:, 0], dets[:, 1], dets[:, 2], dets[:, 3], dets[:, 4] + areas = (x2 - x1 + 1) * (y2 - y1 + 1) + order = scores.argsort()[::-1] + + keep = [] + while order.size > 0: + i = order[0] + keep.append(i) + xx1, yy1 = np.maximum(x1[i], x1[order[1:]]), np.maximum(y1[i], y1[order[1:]]) + xx2, yy2 = np.minimum(x2[i], x2[order[1:]]), np.minimum(y2[i], y2[order[1:]]) + + w, h = np.maximum(0.0, xx2 - xx1 + 1), np.maximum(0.0, yy2 - yy1 + 1) + ovr = w * h / (areas[i] + areas[order[1:]] - w * h) + + inds = np.where(ovr <= thresh)[0] + order = order[inds + 1] + + return keep + + +def encode(matched, priors, variances): + """Encode the variances from the priorbox layers into the ground truth boxes + we have matched (based on jaccard overlap) with the prior boxes. + Args: + matched: (tensor) Coords of ground truth for each prior in point-form + Shape: [num_priors, 4]. + priors: (tensor) Prior boxes in center-offset form + Shape: [num_priors,4]. + variances: (list[float]) Variances of priorboxes + Return: + encoded boxes (tensor), Shape: [num_priors, 4] + """ + + # dist b/t match center and prior's center + g_cxcy = (matched[:, :2] + matched[:, 2:]) / 2 - priors[:, :2] + # encode variance + g_cxcy /= (variances[0] * priors[:, 2:]) + # match wh / prior wh + g_wh = (matched[:, 2:] - matched[:, :2]) / priors[:, 2:] + g_wh = torch.log(g_wh) / variances[1] + # return target for smooth_l1_loss + return torch.cat([g_cxcy, g_wh], 1) # [num_priors,4] + + +def decode(loc, priors, variances): + """Decode locations from predictions using priors to undo + the encoding we did for offset regression at train time. + Args: + loc (tensor): location predictions for loc layers, + Shape: [num_priors,4] + priors (tensor): Prior boxes in center-offset form. + Shape: [num_priors,4]. + variances: (list[float]) Variances of priorboxes + Return: + decoded bounding box predictions + """ + + boxes = torch.cat(( + priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:], + priors[:, 2:] * torch.exp(loc[:, 2:] * variances[1])), 1) + boxes[:, :2] -= boxes[:, 2:] / 2 + boxes[:, 2:] += boxes[:, :2] + return boxes + +def batch_decode(loc, priors, variances): + """Decode locations from predictions using priors to undo + the encoding we did for offset regression at train time. + Args: + loc (tensor): location predictions for loc layers, + Shape: [num_priors,4] + priors (tensor): Prior boxes in center-offset form. + Shape: [num_priors,4]. + variances: (list[float]) Variances of priorboxes + Return: + decoded bounding box predictions + """ + + boxes = torch.cat(( + priors[:, :, :2] + loc[:, :, :2] * variances[0] * priors[:, :, 2:], + priors[:, :, 2:] * torch.exp(loc[:, :, 2:] * variances[1])), 2) + boxes[:, :, :2] -= boxes[:, :, 2:] / 2 + boxes[:, :, 2:] += boxes[:, :, :2] + return boxes diff --git a/wav2lip/face_detection/detection/sfd/detect.py b/wav2lip/face_detection/detection/sfd/detect.py new file mode 100644 index 0000000..efef627 --- /dev/null +++ b/wav2lip/face_detection/detection/sfd/detect.py @@ -0,0 +1,112 @@ +import torch +import torch.nn.functional as F + +import os +import sys +import cv2 +import random +import datetime +import math +import argparse +import numpy as np + +import scipy.io as sio +import zipfile +from .net_s3fd import s3fd +from .bbox import * + + +def detect(net, img, device): + img = img - np.array([104, 117, 123]) + img = img.transpose(2, 0, 1) + img = img.reshape((1,) + img.shape) + + if 'cuda' in device: + torch.backends.cudnn.benchmark = True + + img = torch.from_numpy(img).float().to(device) + BB, CC, HH, WW = img.size() + with torch.no_grad(): + olist = net(img) + + bboxlist = [] + for i in range(len(olist) // 2): + olist[i * 2] = F.softmax(olist[i * 2], dim=1) + olist = [oelem.data.cpu() for oelem in olist] + for i in range(len(olist) // 2): + ocls, oreg = olist[i * 2], olist[i * 2 + 1] + FB, FC, FH, FW = ocls.size() # feature map size + stride = 2**(i + 2) # 4,8,16,32,64,128 + anchor = stride * 4 + poss = zip(*np.where(ocls[:, 1, :, :] > 0.05)) + for Iindex, hindex, windex in poss: + axc, ayc = stride / 2 + windex * stride, stride / 2 + hindex * stride + score = ocls[0, 1, hindex, windex] + loc = oreg[0, :, hindex, windex].contiguous().view(1, 4) + priors = torch.Tensor([[axc / 1.0, ayc / 1.0, stride * 4 / 1.0, stride * 4 / 1.0]]) + variances = [0.1, 0.2] + box = decode(loc, priors, variances) + x1, y1, x2, y2 = box[0] * 1.0 + # cv2.rectangle(imgshow,(int(x1),int(y1)),(int(x2),int(y2)),(0,0,255),1) + bboxlist.append([x1, y1, x2, y2, score]) + bboxlist = np.array(bboxlist) + if 0 == len(bboxlist): + bboxlist = np.zeros((1, 5)) + + return bboxlist + +def batch_detect(net, imgs, device): + imgs = imgs - np.array([104, 117, 123]) + imgs = imgs.transpose(0, 3, 1, 2) + + if 'cuda' in device: + torch.backends.cudnn.benchmark = True + + imgs = torch.from_numpy(imgs).float().to(device) + BB, CC, HH, WW = imgs.size() + with torch.no_grad(): + olist = net(imgs) + + bboxlist = [] + for i in range(len(olist) // 2): + olist[i * 2] = F.softmax(olist[i * 2], dim=1) + olist = [oelem.data.cpu() for oelem in olist] + for i in range(len(olist) // 2): + ocls, oreg = olist[i * 2], olist[i * 2 + 1] + FB, FC, FH, FW = ocls.size() # feature map size + stride = 2**(i + 2) # 4,8,16,32,64,128 + anchor = stride * 4 + poss = zip(*np.where(ocls[:, 1, :, :] > 0.05)) + for Iindex, hindex, windex in poss: + axc, ayc = stride / 2 + windex * stride, stride / 2 + hindex * stride + score = ocls[:, 1, hindex, windex] + loc = oreg[:, :, hindex, windex].contiguous().view(BB, 1, 4) + priors = torch.Tensor([[axc / 1.0, ayc / 1.0, stride * 4 / 1.0, stride * 4 / 1.0]]).view(1, 1, 4) + variances = [0.1, 0.2] + box = batch_decode(loc, priors, variances) + box = box[:, 0] * 1.0 + # cv2.rectangle(imgshow,(int(x1),int(y1)),(int(x2),int(y2)),(0,0,255),1) + bboxlist.append(torch.cat([box, score.unsqueeze(1)], 1).cpu().numpy()) + bboxlist = np.array(bboxlist) + if 0 == len(bboxlist): + bboxlist = np.zeros((1, BB, 5)) + + return bboxlist + +def flip_detect(net, img, device): + img = cv2.flip(img, 1) + b = detect(net, img, device) + + bboxlist = np.zeros(b.shape) + bboxlist[:, 0] = img.shape[1] - b[:, 2] + bboxlist[:, 1] = b[:, 1] + bboxlist[:, 2] = img.shape[1] - b[:, 0] + bboxlist[:, 3] = b[:, 3] + bboxlist[:, 4] = b[:, 4] + return bboxlist + + +def pts_to_bb(pts): + min_x, min_y = np.min(pts, axis=0) + max_x, max_y = np.max(pts, axis=0) + return np.array([min_x, min_y, max_x, max_y]) diff --git a/wav2lip/face_detection/detection/sfd/net_s3fd.py b/wav2lip/face_detection/detection/sfd/net_s3fd.py new file mode 100644 index 0000000..fc64313 --- /dev/null +++ b/wav2lip/face_detection/detection/sfd/net_s3fd.py @@ -0,0 +1,129 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class L2Norm(nn.Module): + def __init__(self, n_channels, scale=1.0): + super(L2Norm, self).__init__() + self.n_channels = n_channels + self.scale = scale + self.eps = 1e-10 + self.weight = nn.Parameter(torch.Tensor(self.n_channels)) + self.weight.data *= 0.0 + self.weight.data += self.scale + + def forward(self, x): + norm = x.pow(2).sum(dim=1, keepdim=True).sqrt() + self.eps + x = x / norm * self.weight.view(1, -1, 1, 1) + return x + + +class s3fd(nn.Module): + def __init__(self): + super(s3fd, self).__init__() + self.conv1_1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1) + self.conv1_2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1) + + self.conv2_1 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1) + self.conv2_2 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1) + + self.conv3_1 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1) + self.conv3_2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1) + self.conv3_3 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1) + + self.conv4_1 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1) + self.conv4_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1) + self.conv4_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1) + + self.conv5_1 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1) + self.conv5_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1) + self.conv5_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1) + + self.fc6 = nn.Conv2d(512, 1024, kernel_size=3, stride=1, padding=3) + self.fc7 = nn.Conv2d(1024, 1024, kernel_size=1, stride=1, padding=0) + + self.conv6_1 = nn.Conv2d(1024, 256, kernel_size=1, stride=1, padding=0) + self.conv6_2 = nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1) + + self.conv7_1 = nn.Conv2d(512, 128, kernel_size=1, stride=1, padding=0) + self.conv7_2 = nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1) + + self.conv3_3_norm = L2Norm(256, scale=10) + self.conv4_3_norm = L2Norm(512, scale=8) + self.conv5_3_norm = L2Norm(512, scale=5) + + self.conv3_3_norm_mbox_conf = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1) + self.conv3_3_norm_mbox_loc = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1) + self.conv4_3_norm_mbox_conf = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1) + self.conv4_3_norm_mbox_loc = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1) + self.conv5_3_norm_mbox_conf = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1) + self.conv5_3_norm_mbox_loc = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1) + + self.fc7_mbox_conf = nn.Conv2d(1024, 2, kernel_size=3, stride=1, padding=1) + self.fc7_mbox_loc = nn.Conv2d(1024, 4, kernel_size=3, stride=1, padding=1) + self.conv6_2_mbox_conf = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1) + self.conv6_2_mbox_loc = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1) + self.conv7_2_mbox_conf = nn.Conv2d(256, 2, kernel_size=3, stride=1, padding=1) + self.conv7_2_mbox_loc = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1) + + def forward(self, x): + h = F.relu(self.conv1_1(x)) + h = F.relu(self.conv1_2(h)) + h = F.max_pool2d(h, 2, 2) + + h = F.relu(self.conv2_1(h)) + h = F.relu(self.conv2_2(h)) + h = F.max_pool2d(h, 2, 2) + + h = F.relu(self.conv3_1(h)) + h = F.relu(self.conv3_2(h)) + h = F.relu(self.conv3_3(h)) + f3_3 = h + h = F.max_pool2d(h, 2, 2) + + h = F.relu(self.conv4_1(h)) + h = F.relu(self.conv4_2(h)) + h = F.relu(self.conv4_3(h)) + f4_3 = h + h = F.max_pool2d(h, 2, 2) + + h = F.relu(self.conv5_1(h)) + h = F.relu(self.conv5_2(h)) + h = F.relu(self.conv5_3(h)) + f5_3 = h + h = F.max_pool2d(h, 2, 2) + + h = F.relu(self.fc6(h)) + h = F.relu(self.fc7(h)) + ffc7 = h + h = F.relu(self.conv6_1(h)) + h = F.relu(self.conv6_2(h)) + f6_2 = h + h = F.relu(self.conv7_1(h)) + h = F.relu(self.conv7_2(h)) + f7_2 = h + + f3_3 = self.conv3_3_norm(f3_3) + f4_3 = self.conv4_3_norm(f4_3) + f5_3 = self.conv5_3_norm(f5_3) + + cls1 = self.conv3_3_norm_mbox_conf(f3_3) + reg1 = self.conv3_3_norm_mbox_loc(f3_3) + cls2 = self.conv4_3_norm_mbox_conf(f4_3) + reg2 = self.conv4_3_norm_mbox_loc(f4_3) + cls3 = self.conv5_3_norm_mbox_conf(f5_3) + reg3 = self.conv5_3_norm_mbox_loc(f5_3) + cls4 = self.fc7_mbox_conf(ffc7) + reg4 = self.fc7_mbox_loc(ffc7) + cls5 = self.conv6_2_mbox_conf(f6_2) + reg5 = self.conv6_2_mbox_loc(f6_2) + cls6 = self.conv7_2_mbox_conf(f7_2) + reg6 = self.conv7_2_mbox_loc(f7_2) + + # max-out background label + chunk = torch.chunk(cls1, 4, 1) + bmax = torch.max(torch.max(chunk[0], chunk[1]), chunk[2]) + cls1 = torch.cat([bmax, chunk[3]], dim=1) + + return [cls1, reg1, cls2, reg2, cls3, reg3, cls4, reg4, cls5, reg5, cls6, reg6] diff --git a/wav2lip/face_detection/detection/sfd/sfd_detector.py b/wav2lip/face_detection/detection/sfd/sfd_detector.py new file mode 100644 index 0000000..8fbce15 --- /dev/null +++ b/wav2lip/face_detection/detection/sfd/sfd_detector.py @@ -0,0 +1,59 @@ +import os +import cv2 +from torch.utils.model_zoo import load_url + +from ..core import FaceDetector + +from .net_s3fd import s3fd +from .bbox import * +from .detect import * + +models_urls = { + 's3fd': 'https://www.adrianbulat.com/downloads/python-fan/s3fd-619a316812.pth', +} + + +class SFDDetector(FaceDetector): + def __init__(self, device, path_to_detector=os.path.join(os.path.dirname(os.path.abspath(__file__)), 's3fd.pth'), verbose=False): + super(SFDDetector, self).__init__(device, verbose) + + # Initialise the face detector + if not os.path.isfile(path_to_detector): + model_weights = load_url(models_urls['s3fd']) + else: + model_weights = torch.load(path_to_detector) + + self.face_detector = s3fd() + self.face_detector.load_state_dict(model_weights) + self.face_detector.to(device) + self.face_detector.eval() + + def detect_from_image(self, tensor_or_path): + image = self.tensor_or_path_to_ndarray(tensor_or_path) + + bboxlist = detect(self.face_detector, image, device=self.device) + keep = nms(bboxlist, 0.3) + bboxlist = bboxlist[keep, :] + bboxlist = [x for x in bboxlist if x[-1] > 0.5] + + return bboxlist + + def detect_from_batch(self, images): + bboxlists = batch_detect(self.face_detector, images, device=self.device) + keeps = [nms(bboxlists[:, i, :], 0.3) for i in range(bboxlists.shape[1])] + bboxlists = [bboxlists[keep, i, :] for i, keep in enumerate(keeps)] + bboxlists = [[x for x in bboxlist if x[-1] > 0.5] for bboxlist in bboxlists] + + return bboxlists + + @property + def reference_scale(self): + return 195 + + @property + def reference_x_shift(self): + return 0 + + @property + def reference_y_shift(self): + return 0 diff --git a/wav2lip/face_detection/models.py b/wav2lip/face_detection/models.py new file mode 100644 index 0000000..ee2dde3 --- /dev/null +++ b/wav2lip/face_detection/models.py @@ -0,0 +1,261 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import math + + +def conv3x3(in_planes, out_planes, strd=1, padding=1, bias=False): + "3x3 convolution with padding" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, + stride=strd, padding=padding, bias=bias) + + +class ConvBlock(nn.Module): + def __init__(self, in_planes, out_planes): + super(ConvBlock, self).__init__() + self.bn1 = nn.BatchNorm2d(in_planes) + self.conv1 = conv3x3(in_planes, int(out_planes / 2)) + self.bn2 = nn.BatchNorm2d(int(out_planes / 2)) + self.conv2 = conv3x3(int(out_planes / 2), int(out_planes / 4)) + self.bn3 = nn.BatchNorm2d(int(out_planes / 4)) + self.conv3 = conv3x3(int(out_planes / 4), int(out_planes / 4)) + + if in_planes != out_planes: + self.downsample = nn.Sequential( + nn.BatchNorm2d(in_planes), + nn.ReLU(True), + nn.Conv2d(in_planes, out_planes, + kernel_size=1, stride=1, bias=False), + ) + else: + self.downsample = None + + def forward(self, x): + residual = x + + out1 = self.bn1(x) + out1 = F.relu(out1, True) + out1 = self.conv1(out1) + + out2 = self.bn2(out1) + out2 = F.relu(out2, True) + out2 = self.conv2(out2) + + out3 = self.bn3(out2) + out3 = F.relu(out3, True) + out3 = self.conv3(out3) + + out3 = torch.cat((out1, out2, out3), 1) + + if self.downsample is not None: + residual = self.downsample(residual) + + out3 += residual + + return out3 + + +class Bottleneck(nn.Module): + + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(Bottleneck, self).__init__() + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, + padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) + self.bn3 = nn.BatchNorm2d(planes * 4) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class HourGlass(nn.Module): + def __init__(self, num_modules, depth, num_features): + super(HourGlass, self).__init__() + self.num_modules = num_modules + self.depth = depth + self.features = num_features + + self._generate_network(self.depth) + + def _generate_network(self, level): + self.add_module('b1_' + str(level), ConvBlock(self.features, self.features)) + + self.add_module('b2_' + str(level), ConvBlock(self.features, self.features)) + + if level > 1: + self._generate_network(level - 1) + else: + self.add_module('b2_plus_' + str(level), ConvBlock(self.features, self.features)) + + self.add_module('b3_' + str(level), ConvBlock(self.features, self.features)) + + def _forward(self, level, inp): + # Upper branch + up1 = inp + up1 = self._modules['b1_' + str(level)](up1) + + # Lower branch + low1 = F.avg_pool2d(inp, 2, stride=2) + low1 = self._modules['b2_' + str(level)](low1) + + if level > 1: + low2 = self._forward(level - 1, low1) + else: + low2 = low1 + low2 = self._modules['b2_plus_' + str(level)](low2) + + low3 = low2 + low3 = self._modules['b3_' + str(level)](low3) + + up2 = F.interpolate(low3, scale_factor=2, mode='nearest') + + return up1 + up2 + + def forward(self, x): + return self._forward(self.depth, x) + + +class FAN(nn.Module): + + def __init__(self, num_modules=1): + super(FAN, self).__init__() + self.num_modules = num_modules + + # Base part + self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3) + self.bn1 = nn.BatchNorm2d(64) + self.conv2 = ConvBlock(64, 128) + self.conv3 = ConvBlock(128, 128) + self.conv4 = ConvBlock(128, 256) + + # Stacking part + for hg_module in range(self.num_modules): + self.add_module('m' + str(hg_module), HourGlass(1, 4, 256)) + self.add_module('top_m_' + str(hg_module), ConvBlock(256, 256)) + self.add_module('conv_last' + str(hg_module), + nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0)) + self.add_module('bn_end' + str(hg_module), nn.BatchNorm2d(256)) + self.add_module('l' + str(hg_module), nn.Conv2d(256, + 68, kernel_size=1, stride=1, padding=0)) + + if hg_module < self.num_modules - 1: + self.add_module( + 'bl' + str(hg_module), nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0)) + self.add_module('al' + str(hg_module), nn.Conv2d(68, + 256, kernel_size=1, stride=1, padding=0)) + + def forward(self, x): + x = F.relu(self.bn1(self.conv1(x)), True) + x = F.avg_pool2d(self.conv2(x), 2, stride=2) + x = self.conv3(x) + x = self.conv4(x) + + previous = x + + outputs = [] + for i in range(self.num_modules): + hg = self._modules['m' + str(i)](previous) + + ll = hg + ll = self._modules['top_m_' + str(i)](ll) + + ll = F.relu(self._modules['bn_end' + str(i)] + (self._modules['conv_last' + str(i)](ll)), True) + + # Predict heatmaps + tmp_out = self._modules['l' + str(i)](ll) + outputs.append(tmp_out) + + if i < self.num_modules - 1: + ll = self._modules['bl' + str(i)](ll) + tmp_out_ = self._modules['al' + str(i)](tmp_out) + previous = previous + ll + tmp_out_ + + return outputs + + +class ResNetDepth(nn.Module): + + def __init__(self, block=Bottleneck, layers=[3, 8, 36, 3], num_classes=68): + self.inplanes = 64 + super(ResNetDepth, self).__init__() + self.conv1 = nn.Conv2d(3 + 68, 64, kernel_size=7, stride=2, padding=3, + bias=False) + self.bn1 = nn.BatchNorm2d(64) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2) + self.avgpool = nn.AvgPool2d(7) + self.fc = nn.Linear(512 * block.expansion, num_classes) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2. / n)) + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + + def _make_layer(self, block, planes, blocks, stride=1): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2d(self.inplanes, planes * block.expansion, + kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(planes * block.expansion), + ) + + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample)) + self.inplanes = planes * block.expansion + for i in range(1, blocks): + layers.append(block(self.inplanes, planes)) + + return nn.Sequential(*layers) + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + + x = self.avgpool(x) + x = x.view(x.size(0), -1) + x = self.fc(x) + + return x diff --git a/wav2lip/face_detection/utils.py b/wav2lip/face_detection/utils.py new file mode 100644 index 0000000..3dc4cf3 --- /dev/null +++ b/wav2lip/face_detection/utils.py @@ -0,0 +1,313 @@ +from __future__ import print_function +import os +import sys +import time +import torch +import math +import numpy as np +import cv2 + + +def _gaussian( + size=3, sigma=0.25, amplitude=1, normalize=False, width=None, + height=None, sigma_horz=None, sigma_vert=None, mean_horz=0.5, + mean_vert=0.5): + # handle some defaults + if width is None: + width = size + if height is None: + height = size + if sigma_horz is None: + sigma_horz = sigma + if sigma_vert is None: + sigma_vert = sigma + center_x = mean_horz * width + 0.5 + center_y = mean_vert * height + 0.5 + gauss = np.empty((height, width), dtype=np.float32) + # generate kernel + for i in range(height): + for j in range(width): + gauss[i][j] = amplitude * math.exp(-(math.pow((j + 1 - center_x) / ( + sigma_horz * width), 2) / 2.0 + math.pow((i + 1 - center_y) / (sigma_vert * height), 2) / 2.0)) + if normalize: + gauss = gauss / np.sum(gauss) + return gauss + + +def draw_gaussian(image, point, sigma): + # Check if the gaussian is inside + ul = [math.floor(point[0] - 3 * sigma), math.floor(point[1] - 3 * sigma)] + br = [math.floor(point[0] + 3 * sigma), math.floor(point[1] + 3 * sigma)] + if (ul[0] > image.shape[1] or ul[1] > image.shape[0] or br[0] < 1 or br[1] < 1): + return image + size = 6 * sigma + 1 + g = _gaussian(size) + g_x = [int(max(1, -ul[0])), int(min(br[0], image.shape[1])) - int(max(1, ul[0])) + int(max(1, -ul[0]))] + g_y = [int(max(1, -ul[1])), int(min(br[1], image.shape[0])) - int(max(1, ul[1])) + int(max(1, -ul[1]))] + img_x = [int(max(1, ul[0])), int(min(br[0], image.shape[1]))] + img_y = [int(max(1, ul[1])), int(min(br[1], image.shape[0]))] + assert (g_x[0] > 0 and g_y[1] > 0) + image[img_y[0] - 1:img_y[1], img_x[0] - 1:img_x[1] + ] = image[img_y[0] - 1:img_y[1], img_x[0] - 1:img_x[1]] + g[g_y[0] - 1:g_y[1], g_x[0] - 1:g_x[1]] + image[image > 1] = 1 + return image + + +def transform(point, center, scale, resolution, invert=False): + """Generate and affine transformation matrix. + + Given a set of points, a center, a scale and a targer resolution, the + function generates and affine transformation matrix. If invert is ``True`` + it will produce the inverse transformation. + + Arguments: + point {torch.tensor} -- the input 2D point + center {torch.tensor or numpy.array} -- the center around which to perform the transformations + scale {float} -- the scale of the face/object + resolution {float} -- the output resolution + + Keyword Arguments: + invert {bool} -- define wherever the function should produce the direct or the + inverse transformation matrix (default: {False}) + """ + _pt = torch.ones(3) + _pt[0] = point[0] + _pt[1] = point[1] + + h = 200.0 * scale + t = torch.eye(3) + t[0, 0] = resolution / h + t[1, 1] = resolution / h + t[0, 2] = resolution * (-center[0] / h + 0.5) + t[1, 2] = resolution * (-center[1] / h + 0.5) + + if invert: + t = torch.inverse(t) + + new_point = (torch.matmul(t, _pt))[0:2] + + return new_point.int() + + +def crop(image, center, scale, resolution=256.0): + """Center crops an image or set of heatmaps + + Arguments: + image {numpy.array} -- an rgb image + center {numpy.array} -- the center of the object, usually the same as of the bounding box + scale {float} -- scale of the face + + Keyword Arguments: + resolution {float} -- the size of the output cropped image (default: {256.0}) + + Returns: + [type] -- [description] + """ # Crop around the center point + """ Crops the image around the center. Input is expected to be an np.ndarray """ + ul = transform([1, 1], center, scale, resolution, True) + br = transform([resolution, resolution], center, scale, resolution, True) + # pad = math.ceil(torch.norm((ul - br).float()) / 2.0 - (br[0] - ul[0]) / 2.0) + if image.ndim > 2: + newDim = np.array([br[1] - ul[1], br[0] - ul[0], + image.shape[2]], dtype=np.int32) + newImg = np.zeros(newDim, dtype=np.uint8) + else: + newDim = np.array([br[1] - ul[1], br[0] - ul[0]], dtype=np.int) + newImg = np.zeros(newDim, dtype=np.uint8) + ht = image.shape[0] + wd = image.shape[1] + newX = np.array( + [max(1, -ul[0] + 1), min(br[0], wd) - ul[0]], dtype=np.int32) + newY = np.array( + [max(1, -ul[1] + 1), min(br[1], ht) - ul[1]], dtype=np.int32) + oldX = np.array([max(1, ul[0] + 1), min(br[0], wd)], dtype=np.int32) + oldY = np.array([max(1, ul[1] + 1), min(br[1], ht)], dtype=np.int32) + newImg[newY[0] - 1:newY[1], newX[0] - 1:newX[1] + ] = image[oldY[0] - 1:oldY[1], oldX[0] - 1:oldX[1], :] + newImg = cv2.resize(newImg, dsize=(int(resolution), int(resolution)), + interpolation=cv2.INTER_LINEAR) + return newImg + + +def get_preds_fromhm(hm, center=None, scale=None): + """Obtain (x,y) coordinates given a set of N heatmaps. If the center + and the scale is provided the function will return the points also in + the original coordinate frame. + + Arguments: + hm {torch.tensor} -- the predicted heatmaps, of shape [B, N, W, H] + + Keyword Arguments: + center {torch.tensor} -- the center of the bounding box (default: {None}) + scale {float} -- face scale (default: {None}) + """ + max, idx = torch.max( + hm.view(hm.size(0), hm.size(1), hm.size(2) * hm.size(3)), 2) + idx += 1 + preds = idx.view(idx.size(0), idx.size(1), 1).repeat(1, 1, 2).float() + preds[..., 0].apply_(lambda x: (x - 1) % hm.size(3) + 1) + preds[..., 1].add_(-1).div_(hm.size(2)).floor_().add_(1) + + for i in range(preds.size(0)): + for j in range(preds.size(1)): + hm_ = hm[i, j, :] + pX, pY = int(preds[i, j, 0]) - 1, int(preds[i, j, 1]) - 1 + if pX > 0 and pX < 63 and pY > 0 and pY < 63: + diff = torch.FloatTensor( + [hm_[pY, pX + 1] - hm_[pY, pX - 1], + hm_[pY + 1, pX] - hm_[pY - 1, pX]]) + preds[i, j].add_(diff.sign_().mul_(.25)) + + preds.add_(-.5) + + preds_orig = torch.zeros(preds.size()) + if center is not None and scale is not None: + for i in range(hm.size(0)): + for j in range(hm.size(1)): + preds_orig[i, j] = transform( + preds[i, j], center, scale, hm.size(2), True) + + return preds, preds_orig + +def get_preds_fromhm_batch(hm, centers=None, scales=None): + """Obtain (x,y) coordinates given a set of N heatmaps. If the centers + and the scales is provided the function will return the points also in + the original coordinate frame. + + Arguments: + hm {torch.tensor} -- the predicted heatmaps, of shape [B, N, W, H] + + Keyword Arguments: + centers {torch.tensor} -- the centers of the bounding box (default: {None}) + scales {float} -- face scales (default: {None}) + """ + max, idx = torch.max( + hm.view(hm.size(0), hm.size(1), hm.size(2) * hm.size(3)), 2) + idx += 1 + preds = idx.view(idx.size(0), idx.size(1), 1).repeat(1, 1, 2).float() + preds[..., 0].apply_(lambda x: (x - 1) % hm.size(3) + 1) + preds[..., 1].add_(-1).div_(hm.size(2)).floor_().add_(1) + + for i in range(preds.size(0)): + for j in range(preds.size(1)): + hm_ = hm[i, j, :] + pX, pY = int(preds[i, j, 0]) - 1, int(preds[i, j, 1]) - 1 + if pX > 0 and pX < 63 and pY > 0 and pY < 63: + diff = torch.FloatTensor( + [hm_[pY, pX + 1] - hm_[pY, pX - 1], + hm_[pY + 1, pX] - hm_[pY - 1, pX]]) + preds[i, j].add_(diff.sign_().mul_(.25)) + + preds.add_(-.5) + + preds_orig = torch.zeros(preds.size()) + if centers is not None and scales is not None: + for i in range(hm.size(0)): + for j in range(hm.size(1)): + preds_orig[i, j] = transform( + preds[i, j], centers[i], scales[i], hm.size(2), True) + + return preds, preds_orig + +def shuffle_lr(parts, pairs=None): + """Shuffle the points left-right according to the axis of symmetry + of the object. + + Arguments: + parts {torch.tensor} -- a 3D or 4D object containing the + heatmaps. + + Keyword Arguments: + pairs {list of integers} -- [order of the flipped points] (default: {None}) + """ + if pairs is None: + pairs = [16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, + 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 27, 28, 29, 30, 35, + 34, 33, 32, 31, 45, 44, 43, 42, 47, 46, 39, 38, 37, 36, 41, + 40, 54, 53, 52, 51, 50, 49, 48, 59, 58, 57, 56, 55, 64, 63, + 62, 61, 60, 67, 66, 65] + if parts.ndimension() == 3: + parts = parts[pairs, ...] + else: + parts = parts[:, pairs, ...] + + return parts + + +def flip(tensor, is_label=False): + """Flip an image or a set of heatmaps left-right + + Arguments: + tensor {numpy.array or torch.tensor} -- [the input image or heatmaps] + + Keyword Arguments: + is_label {bool} -- [denote wherever the input is an image or a set of heatmaps ] (default: {False}) + """ + if not torch.is_tensor(tensor): + tensor = torch.from_numpy(tensor) + + if is_label: + tensor = shuffle_lr(tensor).flip(tensor.ndimension() - 1) + else: + tensor = tensor.flip(tensor.ndimension() - 1) + + return tensor + +# From pyzolib/paths.py (https://bitbucket.org/pyzo/pyzolib/src/tip/paths.py) + + +def appdata_dir(appname=None, roaming=False): + """ appdata_dir(appname=None, roaming=False) + + Get the path to the application directory, where applications are allowed + to write user specific files (e.g. configurations). For non-user specific + data, consider using common_appdata_dir(). + If appname is given, a subdir is appended (and created if necessary). + If roaming is True, will prefer a roaming directory (Windows Vista/7). + """ + + # Define default user directory + userDir = os.getenv('FACEALIGNMENT_USERDIR', None) + if userDir is None: + userDir = os.path.expanduser('~') + if not os.path.isdir(userDir): # pragma: no cover + userDir = '/var/tmp' # issue #54 + + # Get system app data dir + path = None + if sys.platform.startswith('win'): + path1, path2 = os.getenv('LOCALAPPDATA'), os.getenv('APPDATA') + path = (path2 or path1) if roaming else (path1 or path2) + elif sys.platform.startswith('darwin'): + path = os.path.join(userDir, 'Library', 'Application Support') + # On Linux and as fallback + if not (path and os.path.isdir(path)): + path = userDir + + # Maybe we should store things local to the executable (in case of a + # portable distro or a frozen application that wants to be portable) + prefix = sys.prefix + if getattr(sys, 'frozen', None): + prefix = os.path.abspath(os.path.dirname(sys.executable)) + for reldir in ('settings', '../settings'): + localpath = os.path.abspath(os.path.join(prefix, reldir)) + if os.path.isdir(localpath): # pragma: no cover + try: + open(os.path.join(localpath, 'test.write'), 'wb').close() + os.remove(os.path.join(localpath, 'test.write')) + except IOError: + pass # We cannot write in this directory + else: + path = localpath + break + + # Get path specific for this app + if appname: + if path == userDir: + appname = '.' + appname.lstrip('.') # Make it a hidden directory + path = os.path.join(path, appname) + if not os.path.isdir(path): # pragma: no cover + os.mkdir(path) + + # Done + return path diff --git a/wav2lip/hparams.py b/wav2lip/hparams.py new file mode 100644 index 0000000..1c01904 --- /dev/null +++ b/wav2lip/hparams.py @@ -0,0 +1,101 @@ +from glob import glob +import os + +def get_image_list(data_root, split): + filelist = [] + + with open('filelists/{}.txt'.format(split)) as f: + for line in f: + line = line.strip() + if ' ' in line: line = line.split()[0] + filelist.append(os.path.join(data_root, line)) + + return filelist + +class HParams: + def __init__(self, **kwargs): + self.data = {} + + for key, value in kwargs.items(): + self.data[key] = value + + def __getattr__(self, key): + if key not in self.data: + raise AttributeError("'HParams' object has no attribute %s" % key) + return self.data[key] + + def set_hparam(self, key, value): + self.data[key] = value + + +# Default hyperparameters +hparams = HParams( + num_mels=80, # Number of mel-spectrogram channels and local conditioning dimensionality + # network + rescale=True, # Whether to rescale audio prior to preprocessing + rescaling_max=0.9, # Rescaling value + + # Use LWS (https://github.com/Jonathan-LeRoux/lws) for STFT and phase reconstruction + # It"s preferred to set True to use with https://github.com/r9y9/wavenet_vocoder + # Does not work if n_ffit is not multiple of hop_size!! + use_lws=False, + + n_fft=800, # Extra window size is filled with 0 paddings to match this parameter + hop_size=200, # For 16000Hz, 200 = 12.5 ms (0.0125 * sample_rate) + win_size=800, # For 16000Hz, 800 = 50 ms (If None, win_size = n_fft) (0.05 * sample_rate) + sample_rate=16000, # 16000Hz (corresponding to librispeech) (sox --i ) + + frame_shift_ms=None, # Can replace hop_size parameter. (Recommended: 12.5) + + # Mel and Linear spectrograms normalization/scaling and clipping + signal_normalization=True, + # Whether to normalize mel spectrograms to some predefined range (following below parameters) + allow_clipping_in_normalization=True, # Only relevant if mel_normalization = True + symmetric_mels=True, + # Whether to scale the data to be symmetric around 0. (Also multiplies the output range by 2, + # faster and cleaner convergence) + max_abs_value=4., + # max absolute value of data. If symmetric, data will be [-max, max] else [0, max] (Must not + # be too big to avoid gradient explosion, + # not too small for fast convergence) + # Contribution by @begeekmyfriend + # Spectrogram Pre-Emphasis (Lfilter: Reduce spectrogram noise and helps model certitude + # levels. Also allows for better G&L phase reconstruction) + preemphasize=True, # whether to apply filter + preemphasis=0.97, # filter coefficient. + + # Limits + min_level_db=-100, + ref_level_db=20, + fmin=55, + # Set this to 55 if your speaker is male! if female, 95 should help taking off noise. (To + # test depending on dataset. Pitch info: male~[65, 260], female~[100, 525]) + fmax=7600, # To be increased/reduced depending on data. + + ###################### Our training parameters ################################# + img_size=96, + fps=25, + + batch_size=16, + initial_learning_rate=1e-4, + nepochs=200000000000000000, ### ctrl + c, stop whenever eval loss is consistently greater than train loss for ~10 epochs + num_workers=16, + checkpoint_interval=3000, + eval_interval=3000, + save_optimizer_state=True, + + syncnet_wt=0.0, # is initially zero, will be set automatically to 0.03 later. Leads to faster convergence. + syncnet_batch_size=64, + syncnet_lr=1e-4, + syncnet_eval_interval=10000, + syncnet_checkpoint_interval=10000, + + disc_wt=0.07, + disc_initial_learning_rate=1e-4, +) + + +def hparams_debug_string(): + values = hparams.values() + hp = [" %s: %s" % (name, values[name]) for name in sorted(values) if name != "sentences"] + return "Hyperparameters:\n" + "\n".join(hp) diff --git a/wav2lip/models/__init__.py b/wav2lip/models/__init__.py new file mode 100644 index 0000000..4374370 --- /dev/null +++ b/wav2lip/models/__init__.py @@ -0,0 +1,2 @@ +from .wav2lip import Wav2Lip, Wav2Lip_disc_qual +from .syncnet import SyncNet_color \ No newline at end of file diff --git a/wav2lip/models/conv.py b/wav2lip/models/conv.py new file mode 100644 index 0000000..ed83da0 --- /dev/null +++ b/wav2lip/models/conv.py @@ -0,0 +1,44 @@ +import torch +from torch import nn +from torch.nn import functional as F + +class Conv2d(nn.Module): + def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, *args, **kwargs): + super().__init__(*args, **kwargs) + self.conv_block = nn.Sequential( + nn.Conv2d(cin, cout, kernel_size, stride, padding), + nn.BatchNorm2d(cout) + ) + self.act = nn.ReLU() + self.residual = residual + + def forward(self, x): + out = self.conv_block(x) + if self.residual: + out += x + return self.act(out) + +class nonorm_Conv2d(nn.Module): + def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, *args, **kwargs): + super().__init__(*args, **kwargs) + self.conv_block = nn.Sequential( + nn.Conv2d(cin, cout, kernel_size, stride, padding), + ) + self.act = nn.LeakyReLU(0.01, inplace=True) + + def forward(self, x): + out = self.conv_block(x) + return self.act(out) + +class Conv2dTranspose(nn.Module): + def __init__(self, cin, cout, kernel_size, stride, padding, output_padding=0, *args, **kwargs): + super().__init__(*args, **kwargs) + self.conv_block = nn.Sequential( + nn.ConvTranspose2d(cin, cout, kernel_size, stride, padding, output_padding), + nn.BatchNorm2d(cout) + ) + self.act = nn.ReLU() + + def forward(self, x): + out = self.conv_block(x) + return self.act(out) diff --git a/wav2lip/models/syncnet.py b/wav2lip/models/syncnet.py new file mode 100644 index 0000000..e773cdc --- /dev/null +++ b/wav2lip/models/syncnet.py @@ -0,0 +1,66 @@ +import torch +from torch import nn +from torch.nn import functional as F + +from .conv import Conv2d + +class SyncNet_color(nn.Module): + def __init__(self): + super(SyncNet_color, self).__init__() + + self.face_encoder = nn.Sequential( + Conv2d(15, 32, kernel_size=(7, 7), stride=1, padding=3), + + Conv2d(32, 64, kernel_size=5, stride=(1, 2), padding=1), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(64, 128, kernel_size=3, stride=2, padding=1), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(128, 256, kernel_size=3, stride=2, padding=1), + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(256, 512, kernel_size=3, stride=2, padding=1), + Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(512, 512, kernel_size=3, stride=2, padding=1), + Conv2d(512, 512, kernel_size=3, stride=1, padding=0), + Conv2d(512, 512, kernel_size=1, stride=1, padding=0),) + + self.audio_encoder = nn.Sequential( + Conv2d(1, 32, kernel_size=3, stride=1, padding=1), + Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(32, 64, kernel_size=3, stride=(3, 1), padding=1), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(64, 128, kernel_size=3, stride=3, padding=1), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(128, 256, kernel_size=3, stride=(3, 2), padding=1), + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(256, 512, kernel_size=3, stride=1, padding=0), + Conv2d(512, 512, kernel_size=1, stride=1, padding=0),) + + def forward(self, audio_sequences, face_sequences): # audio_sequences := (B, dim, T) + face_embedding = self.face_encoder(face_sequences) + audio_embedding = self.audio_encoder(audio_sequences) + + audio_embedding = audio_embedding.view(audio_embedding.size(0), -1) + face_embedding = face_embedding.view(face_embedding.size(0), -1) + + audio_embedding = F.normalize(audio_embedding, p=2, dim=1) + face_embedding = F.normalize(face_embedding, p=2, dim=1) + + + return audio_embedding, face_embedding diff --git a/wav2lip/models/wav2lip.py b/wav2lip/models/wav2lip.py new file mode 100644 index 0000000..ae5d691 --- /dev/null +++ b/wav2lip/models/wav2lip.py @@ -0,0 +1,184 @@ +import torch +from torch import nn +from torch.nn import functional as F +import math + +from .conv import Conv2dTranspose, Conv2d, nonorm_Conv2d + +class Wav2Lip(nn.Module): + def __init__(self): + super(Wav2Lip, self).__init__() + + self.face_encoder_blocks = nn.ModuleList([ + nn.Sequential(Conv2d(6, 16, kernel_size=7, stride=1, padding=3)), # 96,96 + + nn.Sequential(Conv2d(16, 32, kernel_size=3, stride=2, padding=1), # 48,48 + Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True)), + + nn.Sequential(Conv2d(32, 64, kernel_size=3, stride=2, padding=1), # 24,24 + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True)), + + nn.Sequential(Conv2d(64, 128, kernel_size=3, stride=2, padding=1), # 12,12 + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True)), + + nn.Sequential(Conv2d(128, 256, kernel_size=3, stride=2, padding=1), # 6,6 + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True)), + + nn.Sequential(Conv2d(256, 512, kernel_size=3, stride=2, padding=1), # 3,3 + Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True),), + + nn.Sequential(Conv2d(512, 512, kernel_size=3, stride=1, padding=0), # 1, 1 + Conv2d(512, 512, kernel_size=1, stride=1, padding=0)),]) + + self.audio_encoder = nn.Sequential( + Conv2d(1, 32, kernel_size=3, stride=1, padding=1), + Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(32, 64, kernel_size=3, stride=(3, 1), padding=1), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(64, 128, kernel_size=3, stride=3, padding=1), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(128, 256, kernel_size=3, stride=(3, 2), padding=1), + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(256, 512, kernel_size=3, stride=1, padding=0), + Conv2d(512, 512, kernel_size=1, stride=1, padding=0),) + + self.face_decoder_blocks = nn.ModuleList([ + nn.Sequential(Conv2d(512, 512, kernel_size=1, stride=1, padding=0),), + + nn.Sequential(Conv2dTranspose(1024, 512, kernel_size=3, stride=1, padding=0), # 3,3 + Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True),), + + nn.Sequential(Conv2dTranspose(1024, 512, kernel_size=3, stride=2, padding=1, output_padding=1), + Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True),), # 6, 6 + + nn.Sequential(Conv2dTranspose(768, 384, kernel_size=3, stride=2, padding=1, output_padding=1), + Conv2d(384, 384, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(384, 384, kernel_size=3, stride=1, padding=1, residual=True),), # 12, 12 + + nn.Sequential(Conv2dTranspose(512, 256, kernel_size=3, stride=2, padding=1, output_padding=1), + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),), # 24, 24 + + nn.Sequential(Conv2dTranspose(320, 128, kernel_size=3, stride=2, padding=1, output_padding=1), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),), # 48, 48 + + nn.Sequential(Conv2dTranspose(160, 64, kernel_size=3, stride=2, padding=1, output_padding=1), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),),]) # 96,96 + + self.output_block = nn.Sequential(Conv2d(80, 32, kernel_size=3, stride=1, padding=1), + nn.Conv2d(32, 3, kernel_size=1, stride=1, padding=0), + nn.Sigmoid()) + + def forward(self, audio_sequences, face_sequences): + # audio_sequences = (B, T, 1, 80, 16) + B = audio_sequences.size(0) + + input_dim_size = len(face_sequences.size()) + if input_dim_size > 4: + audio_sequences = torch.cat([audio_sequences[:, i] for i in range(audio_sequences.size(1))], dim=0) + face_sequences = torch.cat([face_sequences[:, :, i] for i in range(face_sequences.size(2))], dim=0) + + audio_embedding = self.audio_encoder(audio_sequences) # B, 512, 1, 1 + + feats = [] + x = face_sequences + for f in self.face_encoder_blocks: + x = f(x) + feats.append(x) + + x = audio_embedding + for f in self.face_decoder_blocks: + x = f(x) + try: + x = torch.cat((x, feats[-1]), dim=1) + except Exception as e: + print(x.size()) + print(feats[-1].size()) + raise e + + feats.pop() + + x = self.output_block(x) + + if input_dim_size > 4: + x = torch.split(x, B, dim=0) # [(B, C, H, W)] + outputs = torch.stack(x, dim=2) # (B, C, T, H, W) + + else: + outputs = x + + return outputs + +class Wav2Lip_disc_qual(nn.Module): + def __init__(self): + super(Wav2Lip_disc_qual, self).__init__() + + self.face_encoder_blocks = nn.ModuleList([ + nn.Sequential(nonorm_Conv2d(3, 32, kernel_size=7, stride=1, padding=3)), # 48,96 + + nn.Sequential(nonorm_Conv2d(32, 64, kernel_size=5, stride=(1, 2), padding=2), # 48,48 + nonorm_Conv2d(64, 64, kernel_size=5, stride=1, padding=2)), + + nn.Sequential(nonorm_Conv2d(64, 128, kernel_size=5, stride=2, padding=2), # 24,24 + nonorm_Conv2d(128, 128, kernel_size=5, stride=1, padding=2)), + + nn.Sequential(nonorm_Conv2d(128, 256, kernel_size=5, stride=2, padding=2), # 12,12 + nonorm_Conv2d(256, 256, kernel_size=5, stride=1, padding=2)), + + nn.Sequential(nonorm_Conv2d(256, 512, kernel_size=3, stride=2, padding=1), # 6,6 + nonorm_Conv2d(512, 512, kernel_size=3, stride=1, padding=1)), + + nn.Sequential(nonorm_Conv2d(512, 512, kernel_size=3, stride=2, padding=1), # 3,3 + nonorm_Conv2d(512, 512, kernel_size=3, stride=1, padding=1),), + + nn.Sequential(nonorm_Conv2d(512, 512, kernel_size=3, stride=1, padding=0), # 1, 1 + nonorm_Conv2d(512, 512, kernel_size=1, stride=1, padding=0)),]) + + self.binary_pred = nn.Sequential(nn.Conv2d(512, 1, kernel_size=1, stride=1, padding=0), nn.Sigmoid()) + self.label_noise = .0 + + def get_lower_half(self, face_sequences): + return face_sequences[:, :, face_sequences.size(2)//2:] + + def to_2d(self, face_sequences): + B = face_sequences.size(0) + face_sequences = torch.cat([face_sequences[:, :, i] for i in range(face_sequences.size(2))], dim=0) + return face_sequences + + def perceptual_forward(self, false_face_sequences): + false_face_sequences = self.to_2d(false_face_sequences) + false_face_sequences = self.get_lower_half(false_face_sequences) + + false_feats = false_face_sequences + for f in self.face_encoder_blocks: + false_feats = f(false_feats) + + false_pred_loss = F.binary_cross_entropy(self.binary_pred(false_feats).view(len(false_feats), -1), + torch.ones((len(false_feats), 1)).cuda()) + + return false_pred_loss + + def forward(self, face_sequences): + face_sequences = self.to_2d(face_sequences) + face_sequences = self.get_lower_half(face_sequences) + + x = face_sequences + for f in self.face_encoder_blocks: + x = f(x) + + return self.binary_pred(x).view(len(x), -1)