| import torchvision |
| import cv2 |
| import numpy as np |
| import torch |
| import torch.nn as nn |
| from einops import rearrange |
| from PIL import Image |
|
|
| from custom_controlnet_aux.util import HWC3, resize_image_with_pad, common_input_validate, custom_hf_download, DENSEPOSE_MODEL_NAME |
| from .densepose import DensePoseMaskedColormapResultsVisualizer, _extract_i_from_iuvarr, densepose_chart_predictor_output_to_result_with_confidences |
|
|
| N_PART_LABELS = 24 |
|
|
| class DenseposeDetector: |
| def __init__(self, model): |
| self.dense_pose_estimation = model |
| self.device = "cpu" |
| self.result_visualizer = DensePoseMaskedColormapResultsVisualizer( |
| alpha=1, |
| data_extractor=_extract_i_from_iuvarr, |
| segm_extractor=_extract_i_from_iuvarr, |
| val_scale = 255.0 / N_PART_LABELS |
| ) |
|
|
| @classmethod |
| def from_pretrained(cls, pretrained_model_or_path=DENSEPOSE_MODEL_NAME, filename="densepose_r50_fpn_dl.torchscript"): |
| torchscript_model_path = custom_hf_download(pretrained_model_or_path, filename) |
| densepose = torch.jit.load(torchscript_model_path, map_location="cpu") |
| return cls(densepose) |
|
|
| def to(self, device): |
| self.dense_pose_estimation.to(device) |
| self.device = device |
| return self |
| |
| def __call__(self, input_image, detect_resolution=512, output_type="pil", upscale_method="INTER_CUBIC", cmap="viridis", **kwargs): |
| input_image, output_type = common_input_validate(input_image, output_type, **kwargs) |
| input_image, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method) |
| H, W = input_image.shape[:2] |
|
|
| hint_image_canvas = np.zeros([H, W], dtype=np.uint8) |
| hint_image_canvas = np.tile(hint_image_canvas[:, :, np.newaxis], [1, 1, 3]) |
|
|
| input_image = rearrange(torch.from_numpy(input_image).to(self.device), 'h w c -> c h w') |
|
|
| pred_boxes, corase_segm, fine_segm, u, v = self.dense_pose_estimation(input_image) |
|
|
| extractor = densepose_chart_predictor_output_to_result_with_confidences |
| densepose_results = [extractor(pred_boxes[i:i+1], corase_segm[i:i+1], fine_segm[i:i+1], u[i:i+1], v[i:i+1]) for i in range(len(pred_boxes))] |
|
|
| if cmap=="viridis": |
| self.result_visualizer.mask_visualizer.cmap = cv2.COLORMAP_VIRIDIS |
| hint_image = self.result_visualizer.visualize(hint_image_canvas, densepose_results) |
| hint_image = cv2.cvtColor(hint_image, cv2.COLOR_BGR2RGB) |
| hint_image[:, :, 0][hint_image[:, :, 0] == 0] = 68 |
| hint_image[:, :, 1][hint_image[:, :, 1] == 0] = 1 |
| hint_image[:, :, 2][hint_image[:, :, 2] == 0] = 84 |
| else: |
| self.result_visualizer.mask_visualizer.cmap = cv2.COLORMAP_PARULA |
| hint_image = self.result_visualizer.visualize(hint_image_canvas, densepose_results) |
| hint_image = cv2.cvtColor(hint_image, cv2.COLOR_BGR2RGB) |
|
|
| detected_map = remove_pad(HWC3(hint_image)) |
| if output_type == "pil": |
| detected_map = Image.fromarray(detected_map) |
| return detected_map |
|
|
