update

.gitignore

@@ -0,0 +1,133 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+face_detection/face_detection.pb
+assets/audios/*.wav
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+.vercel

README.md

@@ -1,8 +1,8 @@
 ---
-title: Free-View Expressive Talking Head Video Editing
-emoji: 🔥
-colorFrom: indigo
-colorTo: pink
+title: FETE
+emoji: 👀
+colorFrom: blue
+colorTo: gray
 sdk: gradio
 sdk_version: 3.41.0
 app_file: app.py

app.py

@@ -0,0 +1,83 @@
+import os
+import glob
+from natsort import natsorted
+import gradio as gr
+
+from inference_util import init_model, infenrece
+from attributtes_utils import input_pose, input_emotion, input_blink
+
+model = init_model()
+
+
+def process(input_vid, audio_path, pose_select, emotion_select, blink_select):
+    pose = input_pose(pose_select)
+    emotion = input_emotion(emotion_select)
+    blink = input_blink(blink_select)
+
+    print("input_vid: ", input_vid)
+    result = infenrece(model, os.path.join("./assets/videos/", input_vid), os.path.join("./assets/audios/", audio_path), pose, emotion, blink)
+    print("result: ", result)
+
+    print("finished !")
+
+    return result  # , gr.Group.update(visible=True)
+
+
+available_videos = natsorted(glob.glob("./assets/videos/*.mp4"))
+available_videos = [os.path.basename(x) for x in available_videos]
+
+# prepare audio
+for video in available_videos:
+    audio = video.replace(".mp4", ".wav")
+    if not os.path.exists(os.path.join("./assets/audios/", audio)):
+        os.system(f"ffmpeg -i ./assets/videos/{video} -vn -acodec pcm_s16le -ar 16000 -ac 1 ./assets/audios/{audio}")
+available_audios = natsorted(glob.glob("./assets/audios/*.wav"))
+available_audios = [os.path.basename(x) for x in available_audios]
+
+
+
+with gr.Blocks() as demo:
+    gr.HTML(
+            """
+            <h1 style="text-align: center; font-size: 40px; font-family: 'Times New Roman', Times, serif;">
+                Free-View Expressive Talking Head Video Editing
+            </h1>
+            <p style="text-align: center; font-size: 20px; font-family: 'Times New Roman', Times, serif;">
+                <a href="https://sky24h.github.io/websites/icassp2023_free-view_video-editing/" target="_blank">
+                    <b>Project Page</b>
+                <br>
+                </a>
+                    <a style="text-align: center; display:inline-block" href="https://huggingface.co/spaces/sky24h/FETE?duplicate=true">
+                    <img src="https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-sm.svg#center" alt="Duplicate Space">
+                    </a>
+            </p>
+            """)
+    with gr.Column(elem_id="col-container"):
+        with gr.Row():
+            with gr.Column():
+                # select and preview video from a list of examples
+                video_preview = gr.Video(label="Video Preview", elem_id="video-preview", height=360, value="./assets/videos/sample1.mp4")
+                video_input = gr.Dropdown(available_videos, label="Input Video", value="sample1.mp4")
+                audio_preview = gr.Audio(label="Audio Preview", elem_id="audio-preview", height=360, value="./assets/audios/sample2.wav")
+                audio_input = gr.Dropdown(available_audios, label="Input Audio", value="sample2.wav")
+                pose_select = gr.Radio(["front", "left_right_shaking"], label="Pose", value="front")
+                emotion_select = gr.Radio(["neutral", "happy", "angry", "surprised"], label="Emotion", value="neutral")
+                blink_select = gr.Radio(["yes", "no"], label="Blink", value="yes")
+                # with gr.Row():
+            with gr.Column():
+                video_out = gr.Video(label="Video Output", elem_id="video-output", height=360)
+                # titile: Free-View Expressive Talking Head Video Editing
+
+                submit_btn = gr.Button("Generate video")
+
+        inputs = [video_input, audio_input, pose_select, emotion_select, blink_select]
+        outputs = [video_out]
+
+        video_preview_output = [video_preview]
+        audio_preview_output = [audio_preview]
+
+    video_input.select(lambda x: "./assets/videos/" + x, video_input, video_preview_output)
+    audio_input.select(lambda x: "./assets/audios/" + x, audio_input, audio_preview_output)
+    submit_btn.click(process, inputs, outputs)
+
+demo.queue(max_size=12).launch()

assets/audios/audio files.txt

@@ -0,0 +1 @@
+

assets/coords/coord files.txt

@@ -0,0 +1 @@
+

assets/coords/sample1.npz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5323c1d6ed9dbda978859aed00bd6b4ec2ca122dbc556821edd4add44181c360
+size 647

assets/coords/sample2.npz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aaf2424b58f2c32b3b55d172a4437bd73687ca2346e4928575c481fa024cf41d
+size 1150

assets/coords/sample3.npz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fb9c284ec586ffe5cb9af9e715907810ce6f07694ef4245c29bb06646a04f3ed
+size 839

assets/coords/sample4.npz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fa3cd8b488cbd368c22b8d10f072d8a55cc73c3c0e8cea6e11420f2743cc00c4
+size 901

assets/coords/sample5.npz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1ee15a8dd3b47bc036a4502ccd60a0a5c29262a5581593e8f97c11e18b389e67
+size 974

attributtes_utils.py

@@ -0,0 +1,47 @@
+import os
+import sys
+
+
+def input_pose(pose_select="front"):
+    step = 1
+    if pose_select == "front":
+        pose = [[0.0, 0.0, 0.0] for i in range(0, 10, step)]#-20 to 20
+    elif pose_select == "left_right_shaking":
+        pose = [[-i, 0.0, 0.0] for i in range(0, 20, step)]#0 to -20
+        pose += [[i - 20.0, 0.0, 0.0] for i in range(0, 40, step)]  # -20 to 20
+        pose += [[20.0 - i, 0.0, 0.0] for i in range(0, 20, step)]  # 20 to 0
+        pose = pose + pose
+        pose = pose + pose
+        pose = pose + pose
+        # pose = pose + pose[::-1]
+    else:
+        raise ValueError("pose_select Error")
+
+    return pose
+
+
+EMOTIONS = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']
+def input_emotion(emotion_select="neutral"):
+    sacle_factor = 2
+    if emotion_select == "neutral":
+        emotion = [[0.0,0.0,0.0,0.0,0.0,0.0,1.0] for _ in range(2)]#((i%50))*0.04
+    elif emotion_select == "happy":
+        emotion = [[0.0,0.0,0.0,1.0,0.0,0.0,0.0] for _ in range(2)]#((i%50))*0.04
+    elif emotion_select == "angry":
+        emotion = [[1.0,0.0,0.0,0.0,0.0,0.0,0.0] for _ in range(2)]
+    elif emotion_select == "surprised":
+        emotion = [[0.0,0.0,0.0,0.0,0.0,1.0,0.0] for _ in range(2)]
+    else:
+        raise ValueError("emotion_select Error")
+
+    return emotion * sacle_factor 
+
+
+def input_blink(blink_select="yes"):
+    if blink_select == "yes":
+        blink = [[1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [1.0], [0.8], [0.6], [0.0], [0.0], [1.0]]
+        blink = blink + blink
+    else:
+        blink = [[1.0] for _ in range(2)]
+    return blink
+

audio.py

@@ -0,0 +1,160 @@
+import librosa
+import librosa.filters
+import numpy as np
+
+# import tensorflow as tf
+from scipy import signal
+from scipy.io import wavfile
+
+hp_num_mels = 80
+hp_rescale = True
+hp_rescaling_max = 0.9
+hp_use_lws = False
+hp_n_fft = 800
+hp_hop_size = 200
+hp_win_size = 800
+hp_sample_rate = 16000
+hp_frame_shift_ms = None
+hp_signal_normalization = True
+hp_allow_clipping_in_normalization = True
+hp_symmetric_mels = True
+hp_max_abs_value = 4.0
+hp_preemphasize = True
+hp_preemphasis = 0.97
+hp_min_level_db = -100
+hp_ref_level_db = 20
+hp_fmin = 55
+hp_fmax = 7600
+
+
+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 _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))

checkpoints/obama-fp16.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a11a77110f573c5d379f7ce0a14b2c85be12c41723bef601c612f06d0d1f7d55
+size 96452948

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. 

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

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

face_detection/detection/__init__.py

@@ -0,0 +1 @@
+from .core import FaceDetector

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

face_detection/detection/sfd/__init__.py

@@ -0,0 +1 @@
+from .sfd_detector import SFDDetector as FaceDetector

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

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])

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]

face_detection/detection/sfd/s3fd.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:619a31681264d3f7f7fc7a16a42cbbe8b23f31a256f75a366e5a1bcd59b33543
+size 89843225

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

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

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

fete_model.py

@@ -0,0 +1,296 @@
+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, *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()
+
+    def forward(self, x):
+        out = self.conv_block(x)
+        return self.act(out)
+
+
+class Conv2d_res(nn.Module):
+    # TensorRT does not support 'if' statement, thus we create independent Conv2d_res for residual block
+    def __init__(self, cin, cout, kernel_size, stride, padding, *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()
+
+    def forward(self, x):
+        out = self.conv_block(x)
+        out += 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)
+
+
+class FETE_model(nn.Module):
+    def __init__(self):
+        super(FETE_model, self).__init__()
+
+        self.face_encoder_blocks = nn.ModuleList(
+            [
+                nn.Sequential(Conv2d(6, 16, kernel_size=7, stride=2, padding=3)),  # 256,256 -> 128,128
+                nn.Sequential(
+                    Conv2d(16, 32, kernel_size=3, stride=2, padding=1),  # 64,64
+                    Conv2d_res(32, 32, kernel_size=3, stride=1, padding=1),
+                    Conv2d_res(32, 32, kernel_size=3, stride=1, padding=1),
+                ),
+                nn.Sequential(
+                    Conv2d(32, 64, kernel_size=3, stride=2, padding=1),  # 32,32
+                    Conv2d_res(64, 64, kernel_size=3, stride=1, padding=1),
+                    Conv2d_res(64, 64, kernel_size=3, stride=1, padding=1),
+                    Conv2d_res(64, 64, kernel_size=3, stride=1, padding=1),
+                ),
+                nn.Sequential(
+                    Conv2d(64, 128, kernel_size=3, stride=2, padding=1),  # 16,16
+                    Conv2d_res(128, 128, kernel_size=3, stride=1, padding=1),
+                    Conv2d_res(128, 128, kernel_size=3, stride=1, padding=1),
+                ),
+                nn.Sequential(
+                    Conv2d(128, 256, kernel_size=3, stride=2, padding=1),  # 8,8
+                    Conv2d_res(256, 256, kernel_size=3, stride=1, padding=1),
+                    Conv2d_res(256, 256, kernel_size=3, stride=1, padding=1),
+                ),
+                nn.Sequential(
+                    Conv2d(256, 512, kernel_size=3, stride=2, padding=1),  # 4,4
+                    Conv2d_res(512, 512, kernel_size=3, stride=1, padding=1),
+                ),
+                nn.Sequential(
+                    Conv2d(512, 512, kernel_size=3, stride=2, 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_res(32, 32, kernel_size=3, stride=1, padding=1),
+            Conv2d_res(32, 32, kernel_size=3, stride=1, padding=1),
+            Conv2d(32, 64, kernel_size=3, stride=(3, 1), padding=1),
+            Conv2d_res(64, 64, kernel_size=3, stride=1, padding=1),
+            Conv2d_res(64, 64, kernel_size=3, stride=1, padding=1),
+            Conv2d(64, 128, kernel_size=3, stride=3, padding=1),
+            Conv2d_res(128, 128, kernel_size=3, stride=1, padding=1),
+            Conv2d_res(128, 128, kernel_size=3, stride=1, padding=1),
+            Conv2d(128, 256, kernel_size=3, stride=(3, 2), padding=1),
+            Conv2d_res(256, 256, kernel_size=3, stride=1, padding=1),
+            Conv2d(256, 512, kernel_size=3, stride=1, padding=0),
+            Conv2d(512, 512, kernel_size=1, stride=1, padding=0),
+        )
+
+        self.pose_encoder = nn.Sequential(
+            Conv2d(1, 32, kernel_size=3, stride=1, padding=1),
+            Conv2d_res(32, 32, kernel_size=3, stride=1, padding=1),
+            Conv2d(32, 64, kernel_size=3, stride=(1, 2), padding=1),
+            Conv2d_res(64, 64, kernel_size=3, stride=1, padding=1),
+            Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
+            Conv2d_res(128, 128, kernel_size=3, stride=1, padding=1),
+            Conv2d(128, 256, kernel_size=3, stride=(1, 2), padding=1),
+            Conv2d_res(256, 256, kernel_size=3, stride=1, padding=1),
+            Conv2d(256, 512, kernel_size=3, stride=2, padding=0),
+            Conv2d(512, 512, kernel_size=1, stride=1, padding=0),
+        )
+
+        self.emotion_encoder = nn.Sequential(
+            Conv2d(1, 32, kernel_size=7, stride=1, padding=1),
+            Conv2d_res(32, 32, kernel_size=3, stride=1, padding=1),
+            Conv2d(32, 64, kernel_size=3, stride=(1, 2), padding=1),
+            Conv2d_res(64, 64, kernel_size=3, stride=1, padding=1),
+            Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
+            Conv2d_res(128, 128, kernel_size=3, stride=1, padding=1),
+            Conv2d(128, 256, kernel_size=3, stride=(1, 2), padding=1),
+            Conv2d_res(256, 256, kernel_size=3, stride=1, padding=1),
+            Conv2d(256, 512, kernel_size=3, stride=2, padding=0),
+            Conv2d(512, 512, kernel_size=1, stride=1, padding=0),
+        )
+
+        self.blink_encoder = nn.Sequential(
+            Conv2d(1, 32, kernel_size=3, stride=1, padding=1),
+            Conv2d_res(32, 32, kernel_size=3, stride=1, padding=1),
+            Conv2d(32, 64, kernel_size=3, stride=(1, 2), padding=1),
+            Conv2d_res(64, 64, kernel_size=3, stride=1, padding=1),
+            Conv2d(64, 128, kernel_size=3, stride=(1, 2), padding=1),
+            Conv2d_res(128, 128, kernel_size=3, stride=1, padding=1),
+            Conv2d(128, 256, kernel_size=3, stride=(1, 2), padding=1),
+            Conv2d_res(256, 256, kernel_size=3, stride=1, padding=1),
+            Conv2d(256, 512, kernel_size=1, stride=(1, 2), padding=0),
+            Conv2d(512, 512, kernel_size=1, stride=1, padding=0),
+        )
+
+        self.face_decoder_blocks = nn.ModuleList(
+            [
+                nn.Sequential(
+                    Conv2d(2048, 512, kernel_size=1, stride=1, padding=0),
+                ),
+                nn.Sequential(
+                    Conv2dTranspose(1024, 512, kernel_size=4, stride=1, padding=0),  # 4,4
+                    Conv2d_res(512, 512, kernel_size=3, stride=1, padding=1),
+                ),
+                nn.Sequential(
+                    Conv2dTranspose(1024, 512, kernel_size=3, stride=2, padding=1, output_padding=1),
+                    Conv2d_res(512, 512, kernel_size=3, stride=1, padding=1),
+                    Conv2d_res(512, 512, kernel_size=3, stride=1, padding=1),  # 8,8
+                    Self_Attention(512, 512),
+                ),
+                nn.Sequential(
+                    Conv2dTranspose(768, 384, kernel_size=3, stride=2, padding=1, output_padding=1),
+                    Conv2d_res(384, 384, kernel_size=3, stride=1, padding=1),
+                    Conv2d_res(384, 384, kernel_size=3, stride=1, padding=1),  # 16, 16
+                    Self_Attention(384, 384),
+                ),
+                nn.Sequential(
+                    Conv2dTranspose(512, 256, kernel_size=3, stride=2, padding=1, output_padding=1),
+                    Conv2d_res(256, 256, kernel_size=3, stride=1, padding=1),
+                    Conv2d_res(256, 256, kernel_size=3, stride=1, padding=1),  # 32, 32
+                    Self_Attention(256, 256),
+                ),
+                nn.Sequential(
+                    Conv2dTranspose(320, 128, kernel_size=3, stride=2, padding=1, output_padding=1),
+                    Conv2d_res(128, 128, kernel_size=3, stride=1, padding=1),
+                    Conv2d_res(128, 128, kernel_size=3, stride=1, padding=1),
+                ),  # 64, 64
+                nn.Sequential(
+                    Conv2dTranspose(160, 64, kernel_size=3, stride=2, padding=1, output_padding=1),
+                    Conv2d_res(64, 64, kernel_size=3, stride=1, padding=1),
+                    Conv2d_res(64, 64, kernel_size=3, stride=1, padding=1),
+                ),
+            ]
+        )  # 128,128
+
+        # 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())
+
+        self.output_block = nn.Sequential(
+            Conv2dTranspose(80, 32, kernel_size=3, stride=2, padding=1, output_padding=1),
+            nn.Conv2d(32, 3, kernel_size=1, stride=1, padding=0),
+            nn.Sigmoid(),
+        )
+
+    def forward(
+        self,
+        face_sequences,
+        audio_sequences,
+        pose_sequences,
+        emotion_sequences,
+        blink_sequences,
+    ):
+        # audio_sequences = (B, T, 1, 80, 16)
+        B = audio_sequences.size(0)
+
+        # disabled for inference
+        # 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)
+        #     pose_sequences = torch.cat([pose_sequences[:, i] for i in range(pose_sequences.size(1))], dim=0)
+        #     emotion_sequences = torch.cat([emotion_sequences[:, i] for i in range(emotion_sequences.size(1))], dim=0)
+        #     blink_sequences = torch.cat([blink_sequences[:, i] for i in range(blink_sequences.size(1))], dim=0)
+        #     face_sequences = torch.cat([face_sequences[:, :, i] for i in range(face_sequences.size(2))], dim=0)
+        # print(audio_sequences.size(), face_sequences.size(), pose_sequences.size(), emotion_sequences.size())
+
+        audio_embedding   = self.audio_encoder(audio_sequences)  # B,                                                     512,  1, 1
+        pose_embedding    = self.pose_encoder(pose_sequences)  # B,                                                       512,  1, 1
+        emotion_embedding = self.emotion_encoder(emotion_sequences)  # B,                                                 512,  1, 1
+        blink_embedding   = self.blink_encoder(blink_sequences)  # B,                                                     512,  1, 1
+        inputs_embedding  = torch.cat((audio_embedding, pose_embedding, emotion_embedding, blink_embedding), dim=1)  # B, 1536, 1, 1
+        # print(audio_embedding.size(), pose_embedding.size(), emotion_embedding.size(), inputs_embedding.size())
+
+        feats = []
+        x = face_sequences
+        for f in self.face_encoder_blocks:
+            x = f(x)
+            # print(x.shape)
+            feats.append(x)
+
+        x = inputs_embedding
+        for f in self.face_decoder_blocks:
+            x = f(x)
+            # print(x.shape)
+
+            # 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 Self_Attention(nn.Module):
+    """
+    Source-Reference Attention Layer
+    """
+
+    def __init__(self, in_planes_s, in_planes_r):
+        """
+        Parameters
+        ----------
+            in_planes_s: int
+                Number of input source feature vector channels.
+            in_planes_r: int
+                Number of input reference feature vector channels.
+        """
+        super(Self_Attention, self).__init__()
+        self.query_conv = nn.Conv2d(in_channels=in_planes_s, out_channels=in_planes_s // 8, kernel_size=1)
+        self.key_conv   = nn.Conv2d(in_channels=in_planes_r, out_channels=in_planes_r // 8, kernel_size=1)
+        self.value_conv = nn.Conv2d(in_channels=in_planes_r, out_channels=in_planes_r, kernel_size=1)
+        self.gamma      = nn.Parameter(torch.zeros(1))
+        self.softmax    = nn.Softmax(dim=-1)
+
+    def forward(self, source):
+        source = source.float() if isinstance(source, torch.cuda.HalfTensor) else source
+        reference = source
+        """
+        Parameters
+        ----------
+            source : torch.Tensor
+                Source feature maps (B x Cs x Ts x Hs x Ws)
+            reference : torch.Tensor
+                Reference feature maps (B x Cr x Tr x Hr x Wr )
+         Returns :
+            torch.Tensor
+                Source-reference attention value added to the input source features
+            torch.Tensor
+                Attention map (B x Ns x Nt) (Ns=Ts*Hs*Ws, Nr=Tr*Hr*Wr)
+        """
+        s_batchsize, sC, sH, sW = source.size()
+        r_batchsize, rC, rH, rW = reference.size()
+
+        proj_query = self.query_conv(source).view(s_batchsize, -1, sH * sW).permute(0, 2, 1)
+        proj_key   = self.key_conv(reference).view(r_batchsize, -1, rW * rH)
+        energy     = torch.bmm(proj_query, proj_key)
+        attention  = self.softmax(energy)
+        proj_value = self.value_conv(reference).view(r_batchsize, -1, rH * rW)
+        out        = torch.bmm(proj_value, attention.permute(0, 2, 1))
+        out        = out.view(s_batchsize, sC, sH, sW)
+        out        = self.gamma * out + source
+        return out.half() if isinstance(source, torch.cuda.FloatTensor) else out

inference_util.py

@@ -0,0 +1,338 @@
+import os
+
+# set CUDA_MODULE_LOADING=LAZY to speed up the serverless function
+os.environ["CUDA_MODULE_LOADING"] = "LAZY"
+# set SAFETENSORS_FAST_GPU=1 to speed up the serverless function
+os.environ["SAFETENSORS_FAST_GPU"] = "1"
+import cv2
+import torch
+import time
+import imageio
+import numpy as np
+from tqdm import tqdm
+import moviepy.editor as mp
+import torch
+
+from audio import load_wav, melspectrogram
+from fete_model import FETE_model
+from preprocess_videos import face_detect, load_from_npz
+
+fps = 25
+mel_idx_multiplier = 80.0 / fps
+
+mel_step_size = 16
+batch_size = 64 if torch.cuda.is_available() else 4
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print("Using {} for inference.".format(device))
+use_fp16 = True if torch.cuda.is_available() else False
+print("Using FP16 for inference.") if use_fp16 else None
+torch.backends.cudnn.benchmark = True if device == "cuda" else False
+
+
+def init_model():
+    checkpoint_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "checkpoints/obama-fp16.safetensors")
+    model = FETE_model()
+    if checkpoint_path.endswith(".pth") or checkpoint_path.endswith(".ckpt"):
+        if device == "cuda":
+            checkpoint = torch.load(checkpoint_path)
+        else:
+            checkpoint = torch.load(checkpoint_path, map_location=lambda storage, loc: storage)
+        s = checkpoint["state_dict"]
+    else:
+        from safetensors import safe_open
+
+        s = {}
+        with safe_open(checkpoint_path, framework="pt", device=device) as f:
+            for key in f.keys():
+                s[key] = f.get_tensor(key)
+    new_s = {}
+    for k, v in s.items():
+        new_s[k.replace("module.", "")] = v
+    model.load_state_dict(new_s)
+
+    model = model.to(device)
+    model.eval()
+    print("Model loaded")
+    if use_fp16:
+        for name, module in model.named_modules():
+            if ".query_conv" in name or ".key_conv" in name or ".value_conv" in name:
+                # keep attention layers in full precision to avoid error
+                module.to(torch.float)
+            else:
+                module.to(torch.half)
+        print("Model converted to half precision to accelerate inference")
+    return model
+
+
+def make_mask(image_size=256, border_size=32):
+    mask_bar = np.linspace(1, 0, border_size).reshape(1, -1).repeat(image_size, axis=0)
+    mask = np.zeros((image_size, image_size), dtype=np.float32)
+    mask[-border_size:, :] += mask_bar.T[::-1]
+    mask[:, :border_size] = mask_bar
+    mask[:, -border_size:] = mask_bar[:, ::-1]
+    mask[-border_size:, :][mask[-border_size:, :] < 0.6] = 0.6
+    mask = np.stack([mask] * 3, axis=-1).astype(np.float32)
+    return mask
+
+
+face_mask = make_mask()
+
+
+def blend_images(foreground, background):
+    # Blend the foreground and background images using the mask
+    temp_mask = cv2.resize(face_mask, (foreground.shape[1], foreground.shape[0]))
+    blended = cv2.multiply(foreground.astype(np.float32), temp_mask)
+    blended += cv2.multiply(background.astype(np.float32), 1 - temp_mask)
+    blended = np.clip(blended, 0, 255).astype(np.uint8)
+    return blended
+
+
+def smooth_coord(last_coord, current_coord, factor=0.4):
+    change = np.array(current_coord) - np.array(last_coord)
+    change = change * factor
+    return (np.array(last_coord) + np.array(change)).astype(int).tolist()
+
+
+def add_black(imgs):
+    for i in range(len(imgs)):
+        # print('x', imgs[i].shape)
+        imgs[i] = cv2.vconcat(
+            [np.zeros((100, imgs[i].shape[1], 3), dtype=np.uint8), imgs[i], np.zeros((20, imgs[i].shape[1], 3), dtype=np.uint8)]
+        )
+        # imgs[i] = cv2.hconcat([np.zeros((imgs[i].shape[0], 100, 3), dtype=np.uint8), imgs[i], np.zeros((imgs[i].shape[0], 100, 3), dtype=np.uint8)])[:480+150,740-100:-740+100,:]
+
+        # print('xx', imgs[i].shape)
+    return imgs
+
+
+def remove_black(img):
+    return img[100:-20]
+
+
+def resize_length(input_attributes, length):
+    input_attributes = np.array(input_attributes)
+    resized_attributes = [input_attributes[int(i_ * (input_attributes.shape[0] / length))] for i_ in range(length)]
+    return np.array(resized_attributes).T
+
+
+def output_chunks(input_attributes):
+    output_chunks = []
+    len_ = len(input_attributes[0])
+
+    i = 0
+    # print(mel.shape, pose.shape)
+    # (80, 801) (3, 801)
+    while 1:
+        start_idx = int(i * mel_idx_multiplier)
+        if start_idx + mel_step_size > len_:
+            output_chunks.append(input_attributes[:, len_ - mel_step_size :])
+            break
+        output_chunks.append(input_attributes[:, start_idx : start_idx + mel_step_size])
+        i += 1
+    return output_chunks
+
+
+def prepare_data(face_path, audio_path, pose, emotion, blink, img_size=256, pads=[0, 0, 0, 0]):
+    if os.path.isfile(face_path) and face_path.split(".")[1] in ["jpg", "png", "jpeg"]:
+        static = True
+        full_frames = [cv2.imread(face_path)]
+    else:
+        static = False
+        video_stream = cv2.VideoCapture(face_path)
+
+        # print('Reading video frames...')
+        full_frames = []
+        while 1:
+            still_reading, frame = video_stream.read()
+            if not still_reading:
+                video_stream.release()
+                break
+            full_frames.append(frame)
+    print("Number of frames available for inference: " + str(len(full_frames)))
+
+    wav = load_wav(audio_path, 16000)
+    mel = melspectrogram(wav)
+    # take half
+    len_ = mel.shape[1]  #  //2
+    mel = mel[:, :len_]
+    # print('>>>', mel.shape)
+
+    pose = resize_length(pose, len_)
+    emotion = resize_length(emotion, len_)
+    blink = resize_length(blink, len_)
+
+    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 = output_chunks(mel)
+    pose_chunks = output_chunks(pose)
+    emotion_chunks = output_chunks(emotion)
+    blink_chunks = output_chunks(blink)
+
+    gen = datagen(face_path, full_frames, mel_chunks, pose_chunks, emotion_chunks, blink_chunks, static=static, img_size=img_size, pads=pads)
+    steps = int(np.ceil(float(len(mel_chunks)) / batch_size))
+
+    return gen, steps
+
+
+def preprocess_batch(batch):
+    return torch.FloatTensor(np.reshape(batch, [len(batch), 1, batch[0].shape[0], batch[0].shape[1]])).to(device)
+
+
+def datagen(face_path, frames, mels, poses, emotions, blinks, static=False, img_size=256, pads=[0, 0, 0, 0]):
+    img_batch, mel_batch, pose_batch, emotion_batch, blink_batch, frame_batch, coords_batch = [], [], [], [], [], [], []
+    scale_factor = img_size // 128
+
+    # print("Length of mel chunks: {}".format(len(mel_chunks)))
+    frames = frames[: len(mels)]
+    frames = add_black(frames)
+    try:
+        video_name = os.path.basename(face_path).split(".")[0]
+        coords = load_from_npz(video_name)
+        face_det_results = [[image[y1:y2, x1:x2], (y1, y2, x1, x2)] for image, (x1, y1, x2, y2) in zip(frames, coords)]
+
+    except Exception as e:
+        print("No existing coords found, running face detection...", "Error: ", e)
+        if not static:
+            coords = face_detect(frames, pads)
+            face_det_results = [[image[y1:y2, x1:x2], (y1, y2, x1, x2)] for image, (x1, y1, x2, y2) in zip(frames, coords)]
+        else:
+            coords = face_detect([frames[0]], pads)
+            face_det_results = [[image[y1:y2, x1:x2], (y1, y2, x1, x2)] for image, (x1, y1, x2, y2) in zip(frames, coords)]
+
+    face_det_results = face_det_results[: len(mels)]
+
+    while len(frames) < len(mels):
+        face_det_results = face_det_results + face_det_results[::-1]
+        frames = frames + frames[::-1]
+    else:
+        face_det_results = face_det_results[: len(mels)]
+        frames = frames[: len(mels)]
+
+    for i in range(len(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(mels[i])
+        pose_batch.append(poses[i])
+        emotion_batch.append(emotions[i])
+        blink_batch.append(blinks[i])
+        frame_batch.append(frame_to_save)
+        coords_batch.append(coords)
+
+        # print(m.shape, poses[i].shape)
+        # (80, 16) (3, 16)
+        if len(img_batch) >= batch_size:
+            img_masked = np.asarray(img_batch).copy()
+
+            img_masked[:, 16 * scale_factor : -16 * scale_factor, 16 * scale_factor : -16 * scale_factor] = 0.0
+
+            img_batch = np.concatenate((img_masked, img_batch), axis=3) / 255.0
+            img_batch = torch.FloatTensor(np.transpose(img_batch, (0, 3, 1, 2))).to(device)
+
+            mel_batch = preprocess_batch(mel_batch)
+            pose_batch = preprocess_batch(pose_batch)
+            emotion_batch = preprocess_batch(emotion_batch)
+            blink_batch = preprocess_batch(blink_batch)
+
+            if use_fp16:
+                yield (
+                    img_batch.half(),
+                    mel_batch.half(),
+                    pose_batch.half(),
+                    emotion_batch.half(),
+                    blink_batch.half(),
+                ), frame_batch, coords_batch
+            else:
+                yield (img_batch, mel_batch, pose_batch, emotion_batch, blink_batch), frame_batch, coords_batch
+            img_batch, mel_batch, pose_batch, emotion_batch, blink_batch, frame_batch, coords_batch = [], [], [], [], [], [], []
+
+    if len(img_batch) > 0:
+        img_masked = np.asarray(img_batch).copy()
+
+        img_masked[:, 16 * scale_factor : -16 * scale_factor, 16 * scale_factor : -16 * scale_factor] = 0.0
+
+        img_batch = np.concatenate((img_masked, img_batch), axis=3) / 255.0
+        img_batch = torch.FloatTensor(np.transpose(img_batch, (0, 3, 1, 2))).to(device)
+
+        mel_batch = preprocess_batch(mel_batch)
+        pose_batch = preprocess_batch(pose_batch)
+        emotion_batch = preprocess_batch(emotion_batch)
+        blink_batch = preprocess_batch(blink_batch)
+
+        if use_fp16:
+            yield (img_batch.half(), mel_batch.half(), pose_batch.half(), emotion_batch.half(), blink_batch.half()), frame_batch, coords_batch
+        else:
+            yield (img_batch, mel_batch, pose_batch, emotion_batch, blink_batch), frame_batch, coords_batch
+
+
+def infenrece(model, face_path, audio_path, pose, emotion, blink, preview=False):
+    timestamp = time.strftime("%Y-%m-%d-%H-%M-%S", time.gmtime(time.time()))
+    gen, steps = prepare_data(face_path, audio_path, pose, emotion, blink)
+    steps = 1 if preview else steps
+    # duration = librosa.get_duration(filename=audio_path)
+
+    if preview:
+        outfile = "/tmp/{}.jpg".format(timestamp)
+    else:
+        outfile = "/tmp/{}.mp4".format(timestamp)
+        tmp_video = "/tmp/temp_{}.mp4".format(timestamp)
+        writer = imageio.get_writer(tmp_video, fps=fps, codec="libx264", quality=10, pixelformat="yuv420p", macro_block_size=1) if not preview else None
+    # print('Generating frames...', outfile, steps)
+    for inputs, frames, coords in tqdm(gen, total=steps):
+        with torch.no_grad():
+            pred = model(*inputs)
+
+        pred = pred.cpu().numpy().transpose(0, 2, 3, 1) * 255.0
+
+        for p, f, c in zip(pred, frames, coords):
+            y1, y2, x1, x2 = c
+            y1, y2, x1, x2 = int(y1), int(y2), int(x1), int(x2)
+            y = round(y2 - y1)
+            x = round(x2 - x1)
+            # print(x, y, p.shape)
+            p = cv2.resize(p.astype(np.uint8), (x, y))
+
+            try:
+                f[y1 : y1 + y, x1 : x1 + x] = blend_images(f[y1 : y1 + y, x1 : x1 + x], p)
+            except Exception as e:
+                print(e)
+                f[y1 : y1 + y, x1 : x1 + x] = p
+            # out.write(f[100:-20])
+            f = remove_black(f)
+            if preview:
+                cv2.imwrite(outfile, f, [int(cv2.IMWRITE_JPEG_QUALITY), 95])
+                return outfile
+            writer.append_data(cv2.cvtColor(f, cv2.COLOR_BGR2RGB))
+    writer.close()
+    video_clip = mp.VideoFileClip(tmp_video)
+    audio_clip = mp.AudioFileClip(audio_path)
+    video_clip = video_clip.set_audio(audio_clip)
+    video_clip.write_videofile(outfile, codec="libx264")
+
+    print("Saved to {}".format(outfile) if os.path.exists(outfile) else "Failed to save {}".format(outfile))
+    try:
+        os.remove(tmp_video)
+        del video_clip
+        del audio_clip
+        del gen
+    except:
+        pass
+    return outfile
+
+
+if __name__ == "__main__":
+    model = init_model()
+
+    from attributtes_utils import input_pose, input_emotion, input_blink
+
+    pose = input_pose()
+    emotion = input_emotion()
+    blink = input_blink()
+    audio_path = "./assets/sample.wav"
+    face_path = "./assets/sample.mp4"
+
+    infenrece(model, face_path, audio_path, pose, emotion, blink)

preprocess_videos.py

@@ -0,0 +1,120 @@
+import face_detection
+import numpy as np
+import cv2
+from tqdm import tqdm
+import torch
+import glob
+import os
+from natsort import natsorted
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+def get_squre_coords(coords, image, size=None, last_size=None):
+    y1, y2, x1, x2 = coords
+    w, h = x2 - x1, y2 - y1
+    center = (x1 + w // 2, y1 + h // 2)
+    if size is None:
+        size = (w + h) // 2
+    if last_size is not None:
+        size = (w + h) // 2
+        size = (size - last_size) // 5 + last_size
+    x1, y1 = center[0] - size // 2, center[1] - size // 2
+    x2, y2 = x1 + size, y1 + size
+    return size, [y1, y2, x1, x2]
+
+
+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 face_detect(images, pads):
+    detector = face_detection.FaceAlignment(face_detection.LandmarksType._2D, flip_input=False, device=device)
+
+    batch_size = 32 if device == "cuda" else 4
+    print("face detect batch size:", batch_size)
+    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:
+            cv2.imwrite(".temp/faulty_frame.jpg", image)  # check this frame where the face was not detected.
+            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)
+        # y_gap, x_gap = ((y2 - y1) * 2) // 3, ((x2 - x1) * 2) // 3
+        y_gap, x_gap = (y2 - y1)//2, (x2 - x1)//2
+        coords_ = [y1 - y_gap, y2 + y_gap, x1 - x_gap, x2 + x_gap]
+
+        # smooth the coords
+        _, coords = get_squre_coords(coords_, image, None)
+
+        y1, y2, x1, x2 = coords
+        y1 = max(0, y1)
+        y2 = min(image.shape[0], y2)
+        x1 = max(0, x1)
+        x2 = min(image.shape[1], x2)
+
+        results.append([x1, y1, x2, y2])
+
+    print("Number of frames cropped: {}".format(len(results)))
+    print("First coords: {}".format(results[0]))
+    boxes = np.array(results)
+    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 boxes
+
+def add_black(imgs):
+    for i in range(len(imgs)):
+        imgs[i] = cv2.vconcat([np.zeros((100, imgs[i].shape[1], 3), dtype=np.uint8), imgs[i], np.zeros((20, imgs[i].shape[1], 3), dtype=np.uint8)])
+
+    return imgs
+
+def preprocess(video_dir="./assets/videos", save_dir="./assets/coords"):
+    all_videos = natsorted(glob.glob(os.path.join(video_dir, "*.mp4")))
+    for video_path in all_videos:
+        video_stream = cv2.VideoCapture(video_path)
+
+        # print('Reading video frames...')
+        full_frames = []
+        while 1:
+            still_reading, frame = video_stream.read()
+            if not still_reading:
+                video_stream.release()
+                break
+            full_frames.append(frame)
+        print("Number of frames available for inference: " + str(len(full_frames)))
+        full_frames = add_black(full_frames)
+        # print('Face detection running...')
+        coords = face_detect(full_frames, pads=(0, 0, 0, 0))
+        np.savez_compressed(os.path.join(save_dir, os.path.basename(video_path).split(".")[0]), coords=coords)
+
+
+def load_from_npz(video_name, save_dir="./assets/coords"):
+    npz = np.load(os.path.join(save_dir, video_name + ".npz"))
+    return npz["coords"]
+
+if __name__ == "__main__":
+    preprocess()

requirements.txt

@@ -0,0 +1,10 @@
+opencv-python==4.7.0.72
+imageio[ffmpeg]==2.31.1
+librosa==0.8.1
+moviepy==1.0.3
+numpy==1.23.5
+safetensors==0.3.2
+torchvision==0.15.2
+gradio==3.41.0
+natsort
+tqdm