An empty "result" folder gets created

README.md

@@ -1,31 +1,105 @@
 ## PyTorch Implementation of [AnimeGANv2](https://github.com/TachibanaYoshino/AnimeGANv2)
 
 
-**Updates**
+**This is a fork version that can evaluate Face Portrait v2 locally, both images & videos**
 
-* `2021-10-17` Add weights for [FacePortraitV2](#additional-model-weights)
-* `2021-11-07` Thanks to [ak92501](https://twitter.com/ak92501), a web demo is integrated to [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). 
+Follow this YouTube [tutorial](https://youtu.be/4WJEG1XwZxg) or if you have any questions feel free to join my [discord](https://discord.gg/sE8R7e45MV) and ask there.
 
-    See demo: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/akhaliq/AnimeGANv2)
+## Setup Environment
+We are going to use Anaconda3, download [Anaconda3](https://www.anaconda.com/products/individual) if you don't have it.  
 
-* `2021-11-07` Thanks to [xhlulu](https://github.com/xhlulu), the `torch.hub` model is now available. See [Torch Hub Usage](#torch-hub-usage).
-* `2021-11-07` Add FacePortraitV2 style demo to a telegram bot. See [@face2stickerbot](https://t.me/face2stickerbot) by [sxela](https://github.com/sxela)
-
-
-## Basic Usage
+- Create conda environment:
+```
+conda create -n AnimeGANv2 python=3.7
+conda activate AnimeGANv2
+```
+- Setup conda environment for nvidia non-30 series GPU:
+```
+conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch
+```
+- Setup conda environment for nvidia 30 series GPU:
+```
+conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch
+```
+- Setup the rest of the conda environment:
+
+clone this repo, and `cd` into the base folder. Then enter the following commands:
+```
+pip install -r requirements.txt
+conda install -c conda-forge ffmpeg
+```
+
+- *To reuse the created conda environment after you close the prompt, you just need to*:
+```
+conda activate AnimeGANv2
+```
+
+## Setup Files
 
-**Weight Conversion from the Original Repo (Requires TensorFlow 1.x)**
+Download the following files:
+- [shape_predictor_68_face_landmarks](http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2)
+- [face_paint_512_v2_0.pt](https://drive.google.com/uc?id=18H3iK09_d54qEDoWIc82SyWB2xun4gjU)
+
+File Structure:
 ```
-git clone https://github.com/TachibanaYoshino/AnimeGANv2
-python convert_weights.py
+📂animegan2-pytorch/ # this is root
+├── 📜shape_predictor_68_face_landmarks.dat (unzip it after download)
+├── 📜face_paint_512_v2_0.pt
+├── 📜requirements.txt
+├── 📂samples/
+│   │...
+│...
 ```
 
-**Inference**
+
+## Inference & Evaluate
+
+### Inference on an image
+
+Put the image/s you want to evaluate into the sample/inputs folder.
+
+Run the following command:
 ```
-python test.py --input_dir [image_folder_path] --device [cpu/cuda]
+python face_test.py
 ```
+And you will find the results under `sample/results`
+
+### Inference on a video
+
+We are going to use ffmpeg to extract videos frame by frame, evaluate them inividually, and combine them back together.
+
+Choose a video, for example mine will be `elon.mp4`, and put it in the `samples` folder. Also create a folder called `temp`, it'll be where we store all the extracted images from the video.
+
+Extract the frames in this format:
+```
+ffmpeg -i samples/YOUR_VIDEO -vf fps=YOUR_VIDEO_FPS samples/temp/YOUR_VIDEO_NAME%06d.png
+```
+For my example, it'll be:
+```
+ffmpeg -i samples/elon.mp4 -vf fps=30 samples/temp/elon%06d.png
+```
+
+Now we going to run the images through the AI:
+```
+python face_test.py --input_dir samples/temp
+```
+
+After this is done, you can combine the result images back together.
+
+Putting frames back together with this format:
+```
+ffmpeg -i samples/results/YOUR_VIDEO_NAME%06d.png -vf fps=YOUR_VIDEO_FPS -pix_fmt yuv420p samples/YOUR_VIDEO_NAME_result.mp4
+```
+
+For my example, it'll be:
+```
+ffmpeg -i samples/results/elon%06d.png -vf fps=30 -pix_fmt yuv420p samples/elon_result.mp4
+```
+
+And you can find your video under the samples folder. And that's it!
 
 
+## Official Results:
 **Results from converted [[Paprika]](https://drive.google.com/file/d/1K_xN32uoQKI8XmNYNLTX5gDn1UnQVe5I/view?usp=sharing) style model**
 
 (input image, original tensorflow result, pytorch result from left to right)

face_test.py

@@ -0,0 +1,242 @@
+import os
+import dlib
+import collections
+from typing import Union, List
+import numpy as np
+from PIL import Image
+import matplotlib.pyplot as plt
+import argparse
+import cv2
+
+
+
+
+import torch
+
+
+from PIL import Image
+from torchvision.transforms.functional import to_tensor, to_pil_image
+from model import Generator
+
+def face2paint(
+    img: Image.Image,
+    size: int,
+    side_by_side: bool = True,
+) -> Image.Image:
+    device = "cuda"
+    model_fname = "face_paint_512_v2_0.pt"
+    model = Generator().eval().to(device)
+    model.load_state_dict(torch.load(model_fname))
+
+
+    w, h = img.size
+    s = min(w, h)
+    img = img.crop(((w - s) // 2, (h - s) // 2, (w + s) // 2, (h + s) // 2))
+    img = img.resize((size, size), Image.LANCZOS)
+
+    input = to_tensor(img).unsqueeze(0) * 2 - 1
+    output = model(input.to(device)).cpu()[0]
+
+    if side_by_side:
+        output = torch.cat([input[0], output], dim=2)
+
+    output = (output * 0.5 + 0.5).clip(0, 1)
+
+    return to_pil_image(output)
+
+
+
+
+
+
+
+def get_dlib_face_detector(predictor_path: str = "shape_predictor_68_face_landmarks.dat"):
+
+    # if not os.path.isfile(predictor_path):
+    #     model_file = "shape_predictor_68_face_landmarks.dat.bz2"
+    #     os.system(f"wget http://dlib.net/files/{model_file}")
+    #     os.system(f"bzip2 -dk {model_file}")
+
+    detector = dlib.get_frontal_face_detector()
+    shape_predictor = dlib.shape_predictor(predictor_path)
+
+    def detect_face_landmarks(img: Union[Image.Image, np.ndarray]):
+        if isinstance(img, Image.Image):
+            img = np.array(img)
+        faces = []
+        dets = detector(img)
+        for d in dets:
+            shape = shape_predictor(img, d)
+            faces.append(np.array([[v.x, v.y] for v in shape.parts()]))
+        return faces
+
+    return detect_face_landmarks
+
+
+
+# https://github.com/NVlabs/ffhq-dataset/blob/master/download_ffhq.py
+
+import PIL.Image
+import PIL.ImageFile
+import numpy as np
+import scipy.ndimage
+
+
+def align_and_crop_face(
+    img: Image.Image,
+    landmarks: np.ndarray,
+    expand: float = 1.0,
+    output_size: int = 1024, 
+    transform_size: int = 4096,
+    enable_padding: bool = True,
+):
+    # Parse landmarks.
+    # pylint: disable=unused-variable
+    lm = landmarks
+    lm_chin          = lm[0  : 17]  # left-right
+    lm_eyebrow_left  = lm[17 : 22]  # left-right
+    lm_eyebrow_right = lm[22 : 27]  # left-right
+    lm_nose          = lm[27 : 31]  # top-down
+    lm_nostrils      = lm[31 : 36]  # top-down
+    lm_eye_left      = lm[36 : 42]  # left-clockwise
+    lm_eye_right     = lm[42 : 48]  # left-clockwise
+    lm_mouth_outer   = lm[48 : 60]  # left-clockwise
+    lm_mouth_inner   = lm[60 : 68]  # left-clockwise
+
+    # Calculate auxiliary vectors.
+    eye_left     = np.mean(lm_eye_left, axis=0)
+    eye_right    = np.mean(lm_eye_right, axis=0)
+    eye_avg      = (eye_left + eye_right) * 0.5
+    eye_to_eye   = eye_right - eye_left
+    mouth_left   = lm_mouth_outer[0]
+    mouth_right  = lm_mouth_outer[6]
+    mouth_avg    = (mouth_left + mouth_right) * 0.5
+    eye_to_mouth = mouth_avg - eye_avg
+
+    # Choose oriented crop rectangle.
+    x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]
+    x /= np.hypot(*x)
+    x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)
+    x *= expand
+    y = np.flipud(x) * [-1, 1]
+    c = eye_avg + eye_to_mouth * 0.1
+    quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
+    qsize = np.hypot(*x) * 2
+
+    # Shrink.
+    shrink = int(np.floor(qsize / output_size * 0.5))
+    if shrink > 1:
+        rsize = (int(np.rint(float(img.size[0]) / shrink)), int(np.rint(float(img.size[1]) / shrink)))
+        img = img.resize(rsize, PIL.Image.ANTIALIAS)
+        quad /= shrink
+        qsize /= shrink
+
+    # Crop.
+    border = max(int(np.rint(qsize * 0.1)), 3)
+    crop = (int(np.floor(min(quad[:,0]))), int(np.floor(min(quad[:,1]))), int(np.ceil(max(quad[:,0]))), int(np.ceil(max(quad[:,1]))))
+    crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]), min(crop[3] + border, img.size[1]))
+    if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]:
+        img = img.crop(crop)
+        quad -= crop[0:2]
+
+    # Pad.
+    pad = (int(np.floor(min(quad[:,0]))), int(np.floor(min(quad[:,1]))), int(np.ceil(max(quad[:,0]))), int(np.ceil(max(quad[:,1]))))
+    pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - img.size[0] + border, 0), max(pad[3] - img.size[1] + border, 0))
+    if enable_padding and max(pad) > border - 4:
+        pad = np.maximum(pad, int(np.rint(qsize * 0.3)))
+        img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')
+        h, w, _ = img.shape
+        y, x, _ = np.ogrid[:h, :w, :1]
+        mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w-1-x) / pad[2]), 1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h-1-y) / pad[3]))
+        blur = qsize * 0.02
+        img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
+        img += (np.median(img, axis=(0,1)) - img) * np.clip(mask, 0.0, 1.0)
+        img = PIL.Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB')
+        quad += pad[:2]
+
+    # Transform.
+    img = img.transform((transform_size, transform_size), PIL.Image.QUAD, (quad + 0.5).flatten(), PIL.Image.BILINEAR)
+    if output_size < transform_size:
+        img = img.resize((output_size, output_size), PIL.Image.ANTIALIAS)
+
+    return img
+
+
+def load_image(image_path, x32=False):
+    img = cv2.imread(image_path).astype(np.float32)
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    h, w = img.shape[:2]
+
+    if x32: # resize image to multiple of 32s
+        def to_32s(x):
+            return 256 if x < 256 else x - x%32
+        img = cv2.resize(img, (to_32s(w), to_32s(h)))
+
+    img = torch.from_numpy(img)
+    img = img/127.5 - 1.0
+    return img
+
+
+def test(args):
+    model_fname = "face_paint_512_v2_0.pt"
+    torch.set_grad_enabled(False)
+    device = "cuda"
+    model = Generator().eval().to(device)
+    model.load_state_dict(torch.load(model_fname))
+    face_detector = get_dlib_face_detector()
+
+
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    for image_name in sorted(os.listdir(args.input_dir)):
+        if os.path.splitext(image_name)[-1].lower() not in [".jpg", ".png", ".bmp", ".tiff"]:
+            continue
+
+        # image = load_image(os.path.join(args.input_dir, image_name), args.x32)
+        image = Image.open(os.path.join(args.input_dir, image_name)).convert("RGB")
+
+        landmarks = face_detector(image)
+        for landmark in landmarks:
+            face = align_and_crop_face(image, landmark, expand=1.3)
+
+
+            # cv2.imwrite(os.path.join(args.output_dir, image_name), , cv2.COLOR_BGR2RGB)
+            face2paint(face, 512).save("samples/results/"+image_name)
+            print(f"image saved: {image_name}")
+
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        '--checkpoint',
+        type=str,
+        default='./face_paint_512_v2_0.pt',
+    )
+    parser.add_argument(
+        '--input_dir', 
+        type=str, 
+        default='./samples/inputs',
+    )
+    parser.add_argument(
+        '--output_dir', 
+        type=str, 
+        default='./samples/results',
+    )
+    parser.add_argument(
+        '--device',
+        type=str,
+        default='cuda:0',
+    )
+    parser.add_argument(
+        '--upsample_align',
+        type=bool,
+        default=False,
+    )
+    parser.add_argument(
+        '--x32',
+        action="store_true",
+    )
+    args = parser.parse_args()
+
+    test(args)

requirements.txt

@@ -0,0 +1,5 @@
+cmake
+matplotlib
+dlib
+scipy
+opencv-python