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from glob import glob
from enum import Enum
import math
import subprocess
import cv2
import numpy as np
from tqdm import tqdm
import torch
import torch.nn.functional as F
from Wav2Lip import audio
import openvino as ov
device = "cpu"
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
def get_smoothened_boxes(boxes, T):
for i in range(len(boxes)):
if i + T > len(boxes):
window = boxes[len(boxes) - T :]
else:
window = boxes[i : i + T]
boxes[i] = np.mean(window, axis=0)
return boxes
def detect(net, img, device):
img = img - np.array([104, 117, 123])
img = img.transpose(2, 0, 1)
img = img.reshape((1,) + img.shape)
img = torch.from_numpy(img).float().to(device)
BB, CC, HH, WW = img.size()
results = net({"x": img})
olist = [torch.Tensor(results[i]) for i in range(12)]
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)
imgs = torch.from_numpy(imgs).float().to(device)
BB, CC, HH, WW = imgs.size()
results = net({"x": imgs.numpy()})
olist = [torch.Tensor(results[i]) for i in range(12)]
bboxlist = []
for i in range(len(olist) // 2):
olist[i * 2] = F.softmax(olist[i * 2], dim=1)
# olist[i * 2] = (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])
class OVFaceDetector(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
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
class OVSFDDetector(OVFaceDetector):
def __init__(self, device, path_to_detector="models/face_detection.xml", verbose=False):
super(OVSFDDetector, self).__init__(device, verbose)
core = ov.Core()
self.face_detector = core.compile_model(path_to_detector, self.device)
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="cpu")
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="cpu")
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
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
class OVFaceAlignment:
def __init__(
self, landmarks_type, network_size=NetworkSize.LARGE, device="CPU", flip_input=False, verbose=False, path_to_detector="models/face_detection.xml"
):
self.device = device
self.flip_input = flip_input
self.landmarks_type = landmarks_type
self.verbose = verbose
network_size = int(network_size)
self.face_detector = OVSFDDetector(device=device, path_to_detector=path_to_detector, 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
def face_detect_ov(images, device, face_det_batch_size, pads, nosmooth, path_to_detector):
detector = OVFaceAlignment(LandmarksType._2D, flip_input=False, device=device, path_to_detector=path_to_detector)
batch_size = face_det_batch_size
print("face_detect_ov images[0].shape: ", images[0].shape)
while 1:
predictions = []
try:
for i in tqdm(range(0, len(images), batch_size)):
predictions.extend(detector.get_detections_for_batch(np.array(images[i : i + batch_size])))
except RuntimeError:
if batch_size == 1:
raise RuntimeError("Image too big to run face detection on GPU. Please use the --resize_factor argument")
batch_size //= 2
print("Recovering from OOM error; New batch size: {}".format(batch_size))
continue
break
results = []
pady1, pady2, padx1, padx2 = pads
for rect, image in zip(predictions, images):
if rect is None:
# check this frame where the face was not detected.
cv2.imwrite("temp/faulty_frame.jpg", image)
raise ValueError("Face not detected! Ensure the video contains a face in all the frames.")
y1 = max(0, rect[1] - pady1)
y2 = min(image.shape[0], rect[3] + pady2)
x1 = max(0, rect[0] - padx1)
x2 = min(image.shape[1], rect[2] + padx2)
results.append([x1, y1, x2, y2])
boxes = np.array(results)
if not nosmooth:
boxes = get_smoothened_boxes(boxes, T=5)
results = [[image[y1:y2, x1:x2], (y1, y2, x1, x2)] for image, (x1, y1, x2, y2) in zip(images, boxes)]
del detector
return results
def datagen(frames, mels, box, static, face_det_batch_size, pads, nosmooth, img_size, wav2lip_batch_size, path_to_detector):
img_batch, mel_batch, frame_batch, coords_batch = [], [], [], []
if box[0] == -1:
if not static:
# BGR2RGB for CNN face detection
face_det_results = face_detect_ov(frames, "CPU", face_det_batch_size, pads, nosmooth, path_to_detector)
else:
face_det_results = face_detect_ov([frames[0]], "CPU", face_det_batch_size, pads, nosmooth, path_to_detector)
else:
print("Using the specified bounding box instead of face detection...")
y1, y2, x1, x2 = box
face_det_results = [[f[y1:y2, x1:x2], (y1, y2, x1, x2)] for f in frames]
for i, m in enumerate(mels):
idx = 0 if static else i % len(frames)
frame_to_save = frames[idx].copy()
face, coords = face_det_results[idx].copy()
face = cv2.resize(face, (img_size, img_size))
img_batch.append(face)
mel_batch.append(m)
frame_batch.append(frame_to_save)
coords_batch.append(coords)
if len(img_batch) >= wav2lip_batch_size:
img_batch, mel_batch = np.asarray(img_batch), np.asarray(mel_batch)
img_masked = img_batch.copy()
img_masked[:, img_size // 2 :] = 0
img_batch = np.concatenate((img_masked, img_batch), axis=3) / 255.0
mel_batch = np.reshape(mel_batch, [len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1])
yield img_batch, mel_batch, frame_batch, coords_batch
img_batch, mel_batch, frame_batch, coords_batch = [], [], [], []
if len(img_batch) > 0:
img_batch, mel_batch = np.asarray(img_batch), np.asarray(mel_batch)
img_masked = img_batch.copy()
img_masked[:, img_size // 2 :] = 0
img_batch = np.concatenate((img_masked, img_batch), axis=3) / 255.0
mel_batch = np.reshape(mel_batch, [len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1])
yield img_batch, mel_batch, frame_batch, coords_batch
def ov_inference(
face_path,
audio_path,
face_detection_path="models/face_detection.xml",
wav2lip_path="models/wav2lip.xml",
inference_device="CPU",
wav2lip_batch_size=128,
outfile="results/result_voice.mp4",
resize_factor=1,
rotate=False,
crop=[0, -1, 0, -1],
mel_step_size=16,
box=[-1, -1, -1, -1],
static=False,
img_size=96,
face_det_batch_size=16,
pads=[0, 10, 0, 0],
nosmooth=False,
):
print("Reading video frames...")
video_stream = cv2.VideoCapture(face_path)
fps = video_stream.get(cv2.CAP_PROP_FPS)
full_frames = []
while 1:
still_reading, frame = video_stream.read()
if not still_reading:
video_stream.release()
break
if resize_factor > 1:
frame = cv2.resize(frame, (frame.shape[1] // resize_factor, frame.shape[0] // resize_factor))
if rotate:
frame = cv2.rotate(frame, cv2.cv2.ROTATE_90_CLOCKWISE)
y1, y2, x1, x2 = crop
if x2 == -1:
x2 = frame.shape[1]
if y2 == -1:
y2 = frame.shape[0]
frame = frame[y1:y2, x1:x2]
full_frames.append(frame)
print("Number of frames available for inference: " + str(len(full_frames)))
core = ov.Core()
if not audio_path.endswith(".wav"):
print("Extracting raw audio...")
command = "ffmpeg -y -i {} -strict -2 {}".format(audio_path, "temp/temp.wav")
subprocess.call(command, shell=True)
audio_path = "temp/temp.wav"
wav = audio.load_wav(audio_path, 16000)
mel = audio.melspectrogram(wav)
print(mel.shape)
if np.isnan(mel.reshape(-1)).sum() > 0:
raise ValueError("Mel contains nan! Using a TTS voice? Add a small epsilon noise to the wav file and try again")
mel_chunks = []
mel_idx_multiplier = 80.0 / fps
i = 0
while 1:
start_idx = int(i * mel_idx_multiplier)
if start_idx + mel_step_size > len(mel[0]):
mel_chunks.append(mel[:, len(mel[0]) - mel_step_size :])
break
mel_chunks.append(mel[:, start_idx : start_idx + mel_step_size])
i += 1
print("Length of mel chunks: {}".format(len(mel_chunks)))
full_frames = full_frames[: len(mel_chunks)]
batch_size = wav2lip_batch_size
gen = datagen(full_frames.copy(), mel_chunks, box, static, face_det_batch_size, pads, nosmooth, img_size, wav2lip_batch_size, face_detection_path)
for i, (img_batch, mel_batch, frames, coords) in enumerate(tqdm(gen, total=int(np.ceil(float(len(mel_chunks)) / batch_size)))):
if i == 0:
img_batch = torch.FloatTensor(np.transpose(img_batch, (0, 3, 1, 2))).to(device)
mel_batch = torch.FloatTensor(np.transpose(mel_batch, (0, 3, 1, 2))).to(device)
compiled_wav2lip_model = core.compile_model(wav2lip_path, inference_device)
print("Model loaded")
frame_h, frame_w = full_frames[0].shape[:-1]
out = cv2.VideoWriter("C:/programacionEjercicios/miwav2lipv6/src/Wav2Lip/temp/result.avi", cv2.VideoWriter_fourcc(*"DIVX"), fps, (frame_w, frame_h))
pred_ov = compiled_wav2lip_model({"audio_sequences": mel_batch.numpy(), "face_sequences": img_batch.numpy()})[0]
else:
img_batch = np.transpose(img_batch, (0, 3, 1, 2))
mel_batch = np.transpose(mel_batch, (0, 3, 1, 2))
pred_ov = compiled_wav2lip_model({"audio_sequences": mel_batch, "face_sequences": img_batch})[0]
pred_ov = compiled_wav2lip_model({"audio_sequences": mel_batch, "face_sequences": img_batch})[0]
pred_ov = pred_ov.transpose(0, 2, 3, 1) * 255.0
for p, f, c in zip(pred_ov, frames, coords):
y1, y2, x1, x2 = c
p = cv2.resize(p.astype(np.uint8), (x2 - x1, y2 - y1))
f[y1:y2, x1:x2] = p
out.write(f)
out.release()
command = "ffmpeg -y -i {} -i {} -strict -2 -q:v 1 {}".format(audio_path, "C:/programacionEjercicios/miwav2lipv6/src/Wav2Lip/temp/result.avi", outfile)
subprocess.call(command, shell=True)
return outfile
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