helpers/processor.py

import io
import cv2
import imageio
import numpy as np
import torch
from typing import Dict, List
from fvcore.common.config import CfgNode
from detectron2.config import get_cfg
from detectron2.engine.defaults import DefaultPredictor
from detectron2.structures.instances import Instances
from densepose import add_densepose_config
from densepose.vis.base import CompoundVisualizer
from densepose.vis.densepose_outputs_vertex import get_texture_atlases
from densepose.vis.densepose_results_textures import DensePoseResultsVisualizerWithTexture as dp_iuv_texture
from densepose.vis.extractor import CompoundExtractor, create_extractor, DensePoseResultExtractor

class TextureProcessor:
  def __init__(self, config: str, weights: str) -> None:
    self.config = self.get_config(config, weights)
    self.predictor = DefaultPredictor(self.config)
    self.extractor = DensePoseResultExtractor()
  
  def process_texture(self, image: np.ndarray) -> np.ndarray:
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    output = self.execute(image)
    if 'pred_densepose' in output:
        texture = self.create_iuv(output, image)
        atlas_texture_bytes = io.BytesIO()
        imageio.imwrite(atlas_texture_bytes, texture, format='PNG')
        texture_atlas_array = np.frombuffer(atlas_texture_bytes.getvalue(), dtype=np.uint8)
        texture_atlas = cv2.imdecode(texture_atlas_array, cv2.IMREAD_COLOR)
        texture_atlas = cv2.cvtColor(texture_atlas, cv2.COLOR_BGR2RGB)
        return texture_atlas
    else:
      raise Exception('Clothes not found')

  def extract(self, person_img, model_img):
    texture_atlas = self.process_texture(model_img)
    return self.overlay_texture(texture_atlas, person_img)

  def overlay_texture(self, texture_atlas: np.ndarray, original_image: np.ndarray) -> np.ndarray:
    texture_atlas[:, :, :3] = texture_atlas[:, :, 2::-1]
    texture_atlases_dict = get_texture_atlases(None)
    vis = dp_iuv_texture(
        cfg=self.config,
        texture_atlas=texture_atlas,
        texture_atlases_dict=texture_atlases_dict
    )

    extractor = create_extractor(vis)

    visualizer = CompoundVisualizer([vis])
    extractor = CompoundExtractor([extractor])

    with torch.no_grad():
      outputs = self.predictor(original_image)['instances']
    
    image = cv2.cvtColor(original_image, cv2.COLOR_BGR2GRAY)
    image = np.tile(image[:, :, np.newaxis], [1, 1, 3])
    data = extractor(outputs)
    image_vis = visualizer.visualize(image, data)

    return image_vis

  def parse_iuv(self, result: Dict) -> np.ndarray:
    i = result['pred_densepose'][0].labels.cpu().numpy().astype(float)
    uv = (result['pred_densepose'][0].uv.cpu().numpy() * 255.0).astype(float)
    iuv = np.stack((uv[1, :, :], uv[0, :, :], i))
    iuv = np.transpose(iuv, (1, 2, 0))
    return iuv

  def parse_bbox(self, result: Dict) -> np.ndarray:
    return result['pred_boxes_XYXY'][0].cpu().numpy()

  def interpolate_tex(self, tex: np.ndarray) -> np.ndarray:
    valid_mask = np.array((tex.sum(0) != 0) * 1, dtype='uint8')
    radius_increase = 10
    kernel = np.ones((radius_increase, radius_increase), np.uint8)
    dilated_mask = cv2.dilate(valid_mask, kernel, iterations=1)
    
    invalid_region = 1 - valid_mask
    actual_part_max = tex.max()
    actual_part_min = tex.min()
    actual_part_uint = np.array((tex - actual_part_min) / (actual_part_max - actual_part_min) * 255, dtype='uint8')
    
    actual_part_uint = cv2.inpaint(actual_part_uint.transpose((1, 2, 0)), invalid_region, 1, cv2.INPAINT_TELEA).transpose((2, 0, 1))
    
    actual_part = (actual_part_uint / 255.0) * (actual_part_max - actual_part_min) + actual_part_min
    actual_part = actual_part * dilated_mask
    
    return actual_part

  def concat_textures(self, array: List[np.ndarray]) -> np.ndarray:
    texture_rows = [np.concatenate(array[i:i+6], axis=1) for i in range(0, 24, 6)]
    texture = np.concatenate(texture_rows, axis=0)
    return texture

  def get_texture(
        self, 
        im: np.ndarray, 
        iuv: np.ndarray, 
        bbox: List[int], 
        tex_part_size: int = 200) -> np.ndarray:
    
    im = im.transpose(2, 1, 0) / 255
    image_w, image_h = im.shape[1], im.shape[2]
    bbox[2] = bbox[2] - bbox[0]
    bbox[3] = bbox[3] - bbox[1]
    x, y, w, h = [int(v) for v in bbox]
    bg = np.zeros((image_h, image_w, 3))
    bg[y:y + h, x:x + w, :] = iuv
    iuv = bg
    iuv = iuv.transpose((2, 1, 0))
    i, u, v = iuv[2], iuv[1], iuv[0]

    n_parts = 22
    texture = np.zeros((n_parts, 3, tex_part_size, tex_part_size))

    for part_id in range(1, n_parts + 1):
        generated = np.zeros((3, tex_part_size, tex_part_size))

        x, y = u[i == part_id], v[i == part_id]

        tex_u_coo = (x * (tex_part_size - 1) / 255).astype(int)
        tex_v_coo = (y * (tex_part_size - 1) / 255).astype(int)

        tex_u_coo = np.clip(tex_u_coo, 0, tex_part_size - 1)
        tex_v_coo = np.clip(tex_v_coo, 0, tex_part_size - 1)

        for channel in range(3):
            generated[channel][tex_v_coo, tex_u_coo] = im[channel][i == part_id]

        if np.sum(generated) > 0:
            generated = self.interpolate_tex(generated)

        texture[part_id - 1] = generated[:, ::-1, :]

    tex_concat = np.zeros((24, tex_part_size, tex_part_size, 3))
    for i in range(texture.shape[0]):
        tex_concat[i] = texture[i].transpose(2, 1, 0)
    tex = self.concat_textures(tex_concat)

    return tex

  def create_iuv(self, results: Dict, image: np.ndarray) -> np.ndarray:
    iuv = self.parse_iuv(results)
    bbox = self.parse_bbox(results)
    uv_texture = self.get_texture(image, iuv, bbox)
    uv_texture = uv_texture.transpose([1, 0, 2])
    return uv_texture

  def get_config(self, config_fpath: str, model_fpath: str) -> CfgNode:
    cfg = get_cfg()
    add_densepose_config(cfg)
    cfg.merge_from_file(config_fpath)
    cfg.MODEL.WEIGHTS = model_fpath
    cfg.MODEL.DEVICE = 'cpu'
    cfg.freeze()
    return cfg

  def execute(self, image: np.ndarray) -> Dict:
    with torch.no_grad():
        outputs = self.predictor(image)['instances']
        return self.execute_on_outputs(outputs)

  def execute_on_outputs(self, outputs: Instances) -> Dict:
    result = {}
    if outputs.has('scores'):
        result['scores'] = outputs.get('scores').cpu()
    if outputs.has('pred_boxes'):
        result['pred_boxes_XYXY'] = outputs.get('pred_boxes').tensor.cpu()
        if outputs.has('pred_densepose'):
            result['pred_densepose'] = self.extractor(outputs)[0]
    return result