EVOLUTION-MANAGER

Edit File: visualize.py

from fastai.core import *
from fastai.vision import *
from matplotlib.axes import Axes
from matplotlib.figure import Figure
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from .filters import IFilter, MasterFilter, ColorizerFilter
from .generators import gen_inference_deep, gen_inference_wide
from tensorboardX import SummaryWriter
from scipy import misc
from PIL import Image
import ffmpeg
import youtube_dl
import gc
import requests
from io import BytesIO
import base64
from IPython import display as ipythondisplay
from IPython.display import HTML
from IPython.display import Image as ipythonimage
import cv2

# adapted from https://www.pyimagesearch.com/2016/04/25/watermarking-images-with-opencv-and-python/
def get_watermarked(pil_image: Image) -> Image:
    try:
        image = cv2.cvtColor(np.array(pil_image), cv2.COLOR_RGB2BGR)
        (h, w) = image.shape[:2]
        image = np.dstack([image, np.ones((h, w), dtype="uint8") * 255])
        pct = 0.05
        full_watermark = cv2.imread(
            './resource_images/watermark.png', cv2.IMREAD_UNCHANGED
        )
        (fwH, fwW) = full_watermark.shape[:2]
        wH = int(pct * h)
        wW = int((pct * h / fwH) * fwW)
        watermark = cv2.resize(full_watermark, (wH, wW), interpolation=cv2.INTER_AREA)
        overlay = np.zeros((h, w, 4), dtype="uint8")
        (wH, wW) = watermark.shape[:2]
        overlay[h - wH - 10 : h - 10, 10 : 10 + wW] = watermark
        # blend the two images together using transparent overlays
        output = image.copy()
        cv2.addWeighted(overlay, 0.5, output, 1.0, 0, output)
        rgb_image = cv2.cvtColor(output, cv2.COLOR_BGR2RGB)
        final_image = Image.fromarray(rgb_image)
        return final_image
    except:
        # Don't want this to crash everything, so let's just not watermark the image for now.
        return pil_image

class ModelImageVisualizer:
    def __init__(self, filter: IFilter, results_dir: str = None):
        self.filter = filter
        self.results_dir = None if results_dir is None else Path(results_dir)
        self.results_dir.mkdir(parents=True, exist_ok=True)

def _clean_mem(self):
        torch.cuda.empty_cache()
        # gc.collect()

def _open_pil_image(self, path: Path) -> Image:
        return PIL.Image.open(path).convert('RGB')

def _get_image_from_url(self, url: str) -> Image:
        response = requests.get(url, timeout=30, headers={'Accept': '*/*;q=0.8'})
        img = PIL.Image.open(BytesIO(response.content)).convert('RGB')
        return img

def plot_transformed_image_from_url(
        self,
        url: str,
        path: str = 'test_images/image.png',
        results_dir:Path = None,
        figsize: (int, int) = (20, 20),
        render_factor: int = None,
        
        display_render_factor: bool = False,
        compare: bool = False,
        post_process: bool = True,
        watermarked: bool = True,
    ) -> Path:
        img = self._get_image_from_url(url)
        img.save(path)
        return self.plot_transformed_image(
            path=path,
            results_dir=results_dir,
            figsize=figsize,
            render_factor=render_factor,
            display_render_factor=display_render_factor,
            compare=compare,
            post_process = post_process,
            watermarked=watermarked,
        )

def plot_transformed_image(
        self,
        path: str,
        results_dir:Path = None,
        figsize: (int, int) = (20, 20),
        render_factor: int = None,
        display_render_factor: bool = False,
        compare: bool = False,
        post_process: bool = True,
        watermarked: bool = True,
    ) -> Path:
        path = Path(path)
        if results_dir is None:
            results_dir = Path(self.results_dir)
        result = self.get_transformed_image(
            path, render_factor, post_process=post_process,watermarked=watermarked
        )
        orig = self._open_pil_image(path)
        if compare:
            self._plot_comparison(
                figsize, render_factor, display_render_factor, orig, result
            )
        else:
            self._plot_solo(figsize, render_factor, display_render_factor, result)

orig.close()
        result_path = self._save_result_image(path, result, results_dir=results_dir)
        result.close()
        return result_path

def _plot_comparison(
        self,
        figsize: (int, int),
        render_factor: int,
        display_render_factor: bool,
        orig: Image,
        result: Image,
    ):
        fig, axes = plt.subplots(1, 2, figsize=figsize)
        self._plot_image(
            orig,
            axes=axes[0],
            figsize=figsize,
            render_factor=render_factor,
            display_render_factor=False,
        )
        self._plot_image(
            result,
            axes=axes[1],
            figsize=figsize,
            render_factor=render_factor,
            display_render_factor=display_render_factor,
        )

def _plot_solo(
        self,
        figsize: (int, int),
        render_factor: int,
        display_render_factor: bool,
        result: Image,
    ):
        fig, axes = plt.subplots(1, 1, figsize=figsize)
        self._plot_image(
            result,
            axes=axes,
            figsize=figsize,
            render_factor=render_factor,
            display_render_factor=display_render_factor,
        )

def _save_result_image(self, source_path: Path, image: Image, results_dir = None) -> Path:
        if results_dir is None:
            results_dir = Path(self.results_dir)
        result_path = results_dir / source_path.name
        image.save(result_path)
        return result_path

def get_transformed_image(
        self, path: Path, render_factor: int = None, post_process: bool = True,
        watermarked: bool = True,
    ) -> Image:
        self._clean_mem()
        orig_image = self._open_pil_image(path)
        filtered_image = self.filter.filter(
            orig_image, orig_image, render_factor=render_factor,post_process=post_process
        )

if watermarked:
            return get_watermarked(filtered_image)

return filtered_image

def _plot_image(
        self,
        image: Image,
        render_factor: int,
        axes: Axes = None,
        figsize=(20, 20),
        display_render_factor = False,
    ):
        if axes is None:
            _, axes = plt.subplots(figsize=figsize)
        axes.imshow(np.asarray(image) / 255)
        axes.axis('off')
        if render_factor is not None and display_render_factor:
            plt.text(
                10,
                10,
                'render_factor: ' + str(render_factor),
                color='white',
                backgroundcolor='black',
            )

def _get_num_rows_columns(self, num_images: int, max_columns: int) -> (int, int):
        columns = min(num_images, max_columns)
        rows = num_images // columns
        rows = rows if rows * columns == num_images else rows + 1
        return rows, columns

class VideoColorizer:
    def __init__(self, vis: ModelImageVisualizer):
        self.vis = vis
        workfolder = Path('./video')
        self.source_folder = workfolder / "source"
        self.bwframes_root = workfolder / "bwframes"
        self.audio_root = workfolder / "audio"
        self.colorframes_root = workfolder / "colorframes"
        self.result_folder = workfolder / "result"

def _purge_images(self, dir):
        for f in os.listdir(dir):
            if re.search('.*?\.jpg', f):
                os.remove(os.path.join(dir, f))

def _get_fps(self, source_path: Path) -> str:
        probe = ffmpeg.probe(str(source_path))
        stream_data = next(
            (stream for stream in probe['streams'] if stream['codec_type'] == 'video'),
            None,
        )
        return stream_data['avg_frame_rate']

def _download_video_from_url(self, source_url, source_path: Path):
        if source_path.exists():
            source_path.unlink()

ydl_opts = {
            'format': 'bestvideo[ext=mp4]+bestaudio[ext=m4a]/mp4',
            'outtmpl': str(source_path),
            'retries': 30,
            'fragment-retries': 30
        }
        with youtube_dl.YoutubeDL(ydl_opts) as ydl:
            ydl.download([source_url])

def _extract_raw_frames(self, source_path: Path):
        bwframes_folder = self.bwframes_root / (source_path.stem)
        bwframe_path_template = str(bwframes_folder / '%5d.jpg')
        bwframes_folder.mkdir(parents=True, exist_ok=True)
        self._purge_images(bwframes_folder)
        ffmpeg.input(str(source_path)).output(
            str(bwframe_path_template), format='image2', vcodec='mjpeg', qscale=0
        ).run(capture_stdout=True)

def _colorize_raw_frames(
        self, source_path: Path, render_factor: int = None, post_process: bool = True,
        watermarked: bool = True,
    ):
        colorframes_folder = self.colorframes_root / (source_path.stem)
        colorframes_folder.mkdir(parents=True, exist_ok=True)
        self._purge_images(colorframes_folder)
        bwframes_folder = self.bwframes_root / (source_path.stem)

for img in progress_bar(os.listdir(str(bwframes_folder))):
            img_path = bwframes_folder / img

if os.path.isfile(str(img_path)):
                color_image = self.vis.get_transformed_image(
                    str(img_path), render_factor=render_factor, post_process=post_process,watermarked=watermarked
                )
                color_image.save(str(colorframes_folder / img))

def _build_video(self, source_path: Path) -> Path:
        colorized_path = self.result_folder / (
            source_path.name.replace('.mp4', '_no_audio.mp4')
        )
        colorframes_folder = self.colorframes_root / (source_path.stem)
        colorframes_path_template = str(colorframes_folder / '%5d.jpg')
        colorized_path.parent.mkdir(parents=True, exist_ok=True)
        if colorized_path.exists():
            colorized_path.unlink()
        fps = self._get_fps(source_path)

ffmpeg.input(
            str(colorframes_path_template),
            format='image2',
            vcodec='mjpeg',
            framerate=fps,
        ).output(str(colorized_path), crf=17, vcodec='libx264').run(capture_stdout=True)

result_path = self.result_folder / source_path.name
        if result_path.exists():
            result_path.unlink()
        # making copy of non-audio version in case adding back audio doesn't apply or fails.
        shutil.copyfile(str(colorized_path), str(result_path))

# adding back sound here
        audio_file = Path(str(source_path).replace('.mp4', '.aac'))
        if audio_file.exists():
            audio_file.unlink()

os.system(
            'ffmpeg -y -i "'
            + str(source_path)
            + '" -vn -acodec copy "'
            + str(audio_file)
            + '"'
        )

if audio_file.exists:
            os.system(
                'ffmpeg -y -i "'
                + str(colorized_path)
                + '" -i "'
                + str(audio_file)
                + '" -shortest -c:v copy -c:a aac -b:a 256k "'
                + str(result_path)
                + '"'
            )
        print('Video created here: ' + str(result_path))
        return result_path

def colorize_from_url(
        self,
        source_url,
        file_name: str,
        render_factor: int = None,
        post_process: bool = True,
        watermarked: bool = True,

) -> Path:
        source_path = self.source_folder / file_name
        self._download_video_from_url(source_url, source_path)
        return self._colorize_from_path(
            source_path, render_factor=render_factor, post_process=post_process,watermarked=watermarked
        )

def colorize_from_file_name(
        self, file_name: str, render_factor: int = None,  watermarked: bool = True, post_process: bool = True,
    ) -> Path:
        source_path = self.source_folder / file_name
        return self._colorize_from_path(
            source_path, render_factor=render_factor,  post_process=post_process,watermarked=watermarked
        )

def _colorize_from_path(
        self, source_path: Path, render_factor: int = None,  watermarked: bool = True, post_process: bool = True
    ) -> Path:
        if not source_path.exists():
            raise Exception(
                'Video at path specfied, ' + str(source_path) + ' could not be found.'
            )
        self._extract_raw_frames(source_path)
        self._colorize_raw_frames(
            source_path, render_factor=render_factor,post_process=post_process,watermarked=watermarked
        )
        return self._build_video(source_path)

def get_video_colorizer(render_factor: int = 21) -> VideoColorizer:
    return get_stable_video_colorizer(render_factor=render_factor)

def get_artistic_video_colorizer(
    root_folder: Path = Path('./'),
    weights_name: str = 'ColorizeArtistic_gen',
    results_dir='result_images',
    render_factor: int = 35
) -> VideoColorizer:
    learn = gen_inference_deep(root_folder=root_folder, weights_name=weights_name)
    filtr = MasterFilter([ColorizerFilter(learn=learn)], render_factor=render_factor)
    vis = ModelImageVisualizer(filtr, results_dir=results_dir)
    return VideoColorizer(vis)

def get_stable_video_colorizer(
    root_folder: Path = Path('./'),
    weights_name: str = 'ColorizeVideo_gen',
    results_dir='result_images',
    render_factor: int = 21
) -> VideoColorizer:
    learn = gen_inference_wide(root_folder=root_folder, weights_name=weights_name)
    filtr = MasterFilter([ColorizerFilter(learn=learn)], render_factor=render_factor)
    vis = ModelImageVisualizer(filtr, results_dir=results_dir)
    return VideoColorizer(vis)

def get_image_colorizer(
    root_folder: Path = Path('./'), render_factor: int = 35, artistic: bool = True
) -> ModelImageVisualizer:
    if artistic:
        return get_artistic_image_colorizer(root_folder=root_folder, render_factor=render_factor)
    else:
        return get_stable_image_colorizer(root_folder=root_folder, render_factor=render_factor)

def get_stable_image_colorizer(
    root_folder: Path = Path('./'),
    weights_name: str = 'ColorizeStable_gen',
    results_dir='result_images',
    render_factor: int = 35
) -> ModelImageVisualizer:
    learn = gen_inference_wide(root_folder=root_folder, weights_name=weights_name)
    filtr = MasterFilter([ColorizerFilter(learn=learn)], render_factor=render_factor)
    vis = ModelImageVisualizer(filtr, results_dir=results_dir)
    return vis

def get_artistic_image_colorizer(
    root_folder: Path = Path('./'),
    weights_name: str = 'ColorizeArtistic_gen',
    results_dir='result_images',
    render_factor: int = 35
) -> ModelImageVisualizer:
    learn = gen_inference_deep(root_folder=root_folder, weights_name=weights_name)
    filtr = MasterFilter([ColorizerFilter(learn=learn)], render_factor=render_factor)
    vis = ModelImageVisualizer(filtr, results_dir=results_dir)
    return vis

def show_image_in_notebook(image_path: Path):
    ipythondisplay.display(ipythonimage(str(image_path)))

def show_video_in_notebook(video_path: Path):
    video = io.open(video_path, 'r+b').read()
    encoded = base64.b64encode(video)
    ipythondisplay.display(
        HTML(
            data='''<video alt="test" autoplay 
                loop controls style="height: 400px;">
                <source src="data:video/mp4;base64,{0}" type="video/mp4" />
             </video>'''.format(
                encoded.decode('ascii')
            )
        )
    )