RAVE-main/evaluation_uncleaned/preprocesser_utils.py

import cv2
import yaml

import numpy as np
from annotator.lineart import LineartDetector
from annotator.zoe import ZoeDetector
from annotator.manga_line import MangaLineExtration
from annotator.lineart_anime import LineartAnimeDetector
from annotator.hed import apply_hed
from annotator.canny import apply_canny
from annotator.pidinet import apply_pidinet
from annotator.leres import apply_leres
from annotator.midas import apply_midas

import torch
import torch.nn.functional as F
import utils.image_process_utils as ipu

def yaml_load(path):
    with open(path, 'r') as stream:
        try:
            return yaml.safe_load(stream)
        except yaml.YAMLError as exc:
            print(exc)

def yaml_dump(path, data):
    with open(path, 'w') as outfile:
        yaml.dump(data, outfile, default_flow_style=False)

def pad64(x):
    return int(np.ceil(float(x) / 64.0) * 64 - x)

def HWC3(x):
    assert x.dtype == np.uint8
    if x.ndim == 2:
        x = x[:, :, None]
    assert x.ndim == 3
    H, W, C = x.shape
    assert C == 1 or C == 3 or C == 4
    if C == 3:
        return x
    if C == 1:
        return np.concatenate([x, x, x], axis=2)
    if C == 4:
        color = x[:, :, 0:3].astype(np.float32)
        alpha = x[:, :, 3:4].astype(np.float32) / 255.0
        y = color * alpha + 255.0 * (1.0 - alpha)
        y = y.clip(0, 255).astype(np.uint8)
        return y

def safer_memory(x):
    # Fix many MAC/AMD problems
    return np.ascontiguousarray(x.copy()).copy()


def resize_image_with_pad(input_image, resolution, skip_hwc3=False):
    if skip_hwc3:
        img = input_image
    else:
        img = HWC3(input_image)
    H_raw, W_raw, _ = img.shape
    k = float(resolution) / float(min(H_raw, W_raw))
    interpolation = cv2.INTER_CUBIC if k > 1 else cv2.INTER_AREA
    H_target = int(np.round(float(H_raw) * k))
    W_target = int(np.round(float(W_raw) * k))
    img = cv2.resize(img, (W_target, H_target), interpolation=interpolation)
    H_pad, W_pad = pad64(H_target), pad64(W_target)
    img_padded = np.pad(img, [[0, H_pad], [0, W_pad], [0, 0]], mode='edge')

    def remove_pad(x):
        return safer_memory(x[:H_target, :W_target])

    return safer_memory(img_padded), remove_pad



def lineart_standard(img, res=512, **kwargs):
    img, remove_pad = resize_image_with_pad(img, res)
    x = img.astype(np.float32)
    g = cv2.GaussianBlur(x, (0, 0), 6.0)
    intensity = np.min(g - x, axis=2).clip(0, 255)
    intensity /= max(16, np.median(intensity[intensity > 8]))
    intensity *= 127
    result = intensity.clip(0, 255).astype(np.uint8)
    return remove_pad(result), True


def lineart(img, res=512, **kwargs):
    img, remove_pad = resize_image_with_pad(img, res)    
    model_lineart = LineartDetector('sk_model.pth')

    # applied auto inversion
    result = 255 - model_lineart(img)
    return remove_pad(result), True


def lineart_coarse(img, res=512, **kwargs):
    img, remove_pad = resize_image_with_pad(img, res)
    model_lineart_coarse = LineartDetector('sk_model2.pth')

    # applied auto inversion
    result = 255 - model_lineart_coarse(img)
    return remove_pad(result), True

def lineart_anime(img, res=512, **kwargs):
    img, remove_pad = resize_image_with_pad(img, res)
    model_lineart_anime = LineartAnimeDetector()

    # applied auto inversion
    result = 255 - model_lineart_anime(img)
    return remove_pad(result), True


def lineart_anime_denoise(img, res=512, **kwargs):
    img, remove_pad = resize_image_with_pad(img, res)
    model_manga_line = MangaLineExtration()

    # applied auto inversion
    result = model_manga_line(img)
    return remove_pad(result), True


def canny(img, res=512, thr_a=100, thr_b=200, **kwargs):
    l, h = thr_a, thr_b
    img, remove_pad = resize_image_with_pad(img, res)        
    model_canny = apply_canny
    result = model_canny(img, l, h)
    return remove_pad(result), True



def hed(img, res=512, **kwargs):
    img, remove_pad = resize_image_with_pad(img, res)
    model_hed = apply_hed
    result = model_hed(img)
    return remove_pad(result), True


def hed_safe(img, res=512, **kwargs):
    img, remove_pad = resize_image_with_pad(img, res)
    model_hed = apply_hed
    result = model_hed(img, is_safe=True)
    return remove_pad(result), True

def midas(img, res=512, a=np.pi * 2.0, **kwargs):
    img, remove_pad = resize_image_with_pad(img, res)
    model_midas = apply_midas
    result, _ = model_midas(img, a)
    return remove_pad(result), True


def leres(img, res=512, thr_a=0, thr_b=0, boost=False, **kwargs):
    img, remove_pad = resize_image_with_pad(img, res)
    model_leres = apply_leres
    result = model_leres(img, thr_a, thr_b, boost=boost)
    return remove_pad(result), True

def lerespp(img, res=512, thr_a=0, thr_b=0, boost=True, **kwargs):
    img, remove_pad = resize_image_with_pad(img, res)
    model_leres = apply_leres
    result = model_leres(img, thr_a, thr_b, boost=boost)
    return remove_pad(result), True


def pidinet(img, res=512, **kwargs):
    img, remove_pad = resize_image_with_pad(img, res)
    model_pidinet = apply_pidinet
    result = model_pidinet(img)
    return remove_pad(result), True


def pidinet_ts(img, res=512, **kwargs):
    img, remove_pad = resize_image_with_pad(img, res)
    model_pidinet = apply_pidinet
    result = model_pidinet(img, apply_fliter=True)
    return remove_pad(result), True


def pidinet_safe(img, res=512, **kwargs):
    img, remove_pad = resize_image_with_pad(img, res)
    model_pidinet = apply_pidinet
    result = model_pidinet(img, is_safe=True)
    return remove_pad(result), True



def zoe_depth(img, res=512, **kwargs):
    img, remove_pad = resize_image_with_pad(img, res)
    model_zoe_depth = ZoeDetector()
    result = model_zoe_depth(img)
    return remove_pad(result), True


preprocessors_dict = {
    'lineart_realistic': lineart,
    'lineart_coarse': lineart_coarse,
    'lineart_standard': lineart_standard,
    'lineart_anime': lineart_anime,
    'lineart_anime_denoise': lineart_anime_denoise,
    'softedge_hed': hed,
    'softedge_hedsafe': hed_safe,
    'softedge_pidinet': pidinet,
    'softedge_pidsafe': pidinet_safe,
    'canny': canny,
    'depth_leres': leres,
    'depth_leres++': lerespp,
    'depth_midas': midas,
    'depth_zoe': zoe_depth,
}

def pixel_perfect_process(input_image, p_name):
    raw_H, raw_W, _ = input_image.shape
    preprocessor_resolution = raw_H
    detected_map, _ = preprocessors_dict[p_name](input_image, res=preprocessor_resolution)
    return detected_map

def calculate_flow(prev_frame, curr_frame):
    prev = ipu.pil_to_cv_gray(prev_frame)
    curr = ipu.pil_to_cv_gray(curr_frame)

    flow = cv2.calcOpticalFlowFarneback(prev, curr, None, 0.5, 3, 15, 3, 5, 1.2, 0)
    h, w = flow.shape[:2]
    flow = -flow
    flow[:,:,0] += np.arange(w)
    flow[:,:,1] += np.arange(h)[:,np.newaxis]
    return flow

def condition_smoothing(prev_condition, prev_flow, curr_condition, next_condition, next_flow, smoothing):        
    # prev_condition.shape # (H, W)
    warped_prev = cv2.remap(prev_condition, prev_flow, None, cv2.INTER_LINEAR)
    warped_next = cv2.remap(next_condition, next_flow, None, cv2.INTER_LINEAR)

    # curr_condition = smoothing * warped_prev + (1 - smoothing) * warped_next 
    curr_condition = 2*smoothing * warped_prev + smoothing * warped_next + (1 - (3 * smoothing)) * curr_condition
    return curr_condition # (H, W) numpy array
            

def warp(x, flo):
    """
    warp an image/tensor (im2) back to im1, according to the optical flow
    x: [B, C, H, W] (im2)
    flo: [B, 2, H, W] flow
    """
    B, C, H, W = x.size()
    # mesh grid
    xx = torch.arange(0, W).view(1, -1).repeat(H, 1)
    yy = torch.arange(0, H).view(-1, 1).repeat(1, W)
    xx = xx.view(1, 1, H, W).repeat(B, 1, 1, 1)
    yy = yy.view(1, 1, H, W).repeat(B, 1, 1, 1)
    grid = torch.cat((xx, yy), 1).float()

    grid = grid.to(x.device)
    vgrid = grid + flo
    # scale grid to [-1,1]
    vgrid[:, 0, :, :] = 2.0 * vgrid[:, 0, :, :].clone() / max(W - 1, 1) - 1.0
    vgrid[:, 1, :, :] = 2.0 * vgrid[:, 1, :, :].clone() / max(H - 1, 1) - 1.0

    vgrid = vgrid.permute(0, 2, 3, 1)
    output = F.grid_sample(x, vgrid, mode='nearest', align_corners=True, padding_mode='zeros')
    # mask = torch.ones(x.size()).to(x.device)
    # mask = F.grid_sample(mask, vgrid)

    # mask[mask < 0.999] = 0
    # mask[mask > 0] = 1

    return output