orgin

annotator/__init__.py

@@ -16,6 +16,7 @@ from .pidinet import PidInet
 from .shuffle import Image2MaskShuffleDetector
 from .zoe import ZoeDetector
 from .oneformer import OneformerDetector
+from .denthPose import DenthPoseProcessor
 
 __all__ = [
     UniformerDetector,
@@ -34,7 +35,8 @@ __all__ = [
     PidInet,
     Image2MaskShuffleDetector,
     ZoeDetector,
-    OneformerDetector
+    OneformerDetector,
+    DenthPoseProcessor
 ]
 #
 #

annotator/denthPose/Base-DensePose-RCNN-FPN.yaml

@@ -0,0 +1,48 @@
+VERSION: 2
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  BACKBONE:
+    NAME: "build_resnet_fpn_backbone"
+  RESNETS:
+    OUT_FEATURES: ["res2", "res3", "res4", "res5"]
+  FPN:
+    IN_FEATURES: ["res2", "res3", "res4", "res5"]
+  ANCHOR_GENERATOR:
+    SIZES: [[32], [64], [128], [256], [512]]  # One size for each in feature map
+    ASPECT_RATIOS: [[0.5, 1.0, 2.0]]  # Three aspect ratios (same for all in feature maps)
+  RPN:
+    IN_FEATURES: ["p2", "p3", "p4", "p5", "p6"]
+    PRE_NMS_TOPK_TRAIN: 2000  # Per FPN level
+    PRE_NMS_TOPK_TEST: 1000  # Per FPN level
+    # Detectron1 uses 2000 proposals per-batch,
+    # (See "modeling/rpn/rpn_outputs.py" for details of this legacy issue)
+    # which is approximately 1000 proposals per-image since the default batch size for FPN is 2.
+    POST_NMS_TOPK_TRAIN: 1000
+    POST_NMS_TOPK_TEST: 1000
+
+  DENSEPOSE_ON: True
+  ROI_HEADS:
+    NAME: "DensePoseROIHeads"
+    IN_FEATURES: ["p2", "p3", "p4", "p5"]
+    NUM_CLASSES: 1
+  ROI_BOX_HEAD:
+    NAME: "FastRCNNConvFCHead"
+    NUM_FC: 2
+    POOLER_RESOLUTION: 7
+    POOLER_SAMPLING_RATIO: 2
+    POOLER_TYPE: "ROIAlign"
+  ROI_DENSEPOSE_HEAD:
+    NAME: "DensePoseV1ConvXHead"
+    POOLER_TYPE: "ROIAlign"
+    NUM_COARSE_SEGM_CHANNELS: 2
+DATASETS:
+  TRAIN: ("densepose_coco_2014_train", "densepose_coco_2014_valminusminival")
+  TEST: ("densepose_coco_2014_minival",)
+SOLVER:
+  IMS_PER_BATCH: 16
+  BASE_LR: 0.01
+  STEPS: (60000, 80000)
+  MAX_ITER: 90000
+  WARMUP_FACTOR: 0.1
+INPUT:
+  MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)

annotator/denthPose/__init__.py

@@ -0,0 +1,20 @@
+import os
+
+
+def install_package():
+    print(os.system("pip install git+https://github.com/facebookresearch/detectron2.git"))
+    print(os.system("pip install git+https://github.com/facebookresearch/detectron2@main#subdirectory=projects/DensePose"))
+
+
+install_package()
+
+print("finished")
+
+
+class DenthPoseProcessor:
+    def __init__(self):
+        from .depth_pose import DenthPoseProcess
+        self.processor = DenthPoseProcess()
+
+    def __call__(self, img):
+        return self.processor(img)

annotator/denthPose/densepose_rcnn_R_50_FPN_s1x.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b8a7382001b16e453bad95ca9dbc68ae8f2b839b304cf90eaf5c27fbdb4dae91
+size 255757821

annotator/denthPose/densepose_rcnn_R_50_FPN_s1x.yaml

@@ -0,0 +1,9 @@
+_BASE_: "Base-DensePose-RCNN-FPN.yaml"
+MODEL:
+  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
+  RESNETS:
+    DEPTH: 50
+  DEVICE: "cpu"
+SOLVER:
+  MAX_ITER: 130000
+  STEPS: (100000, 120000)

annotator/denthPose/depth_pose.py

@@ -0,0 +1,114 @@
+import os.path
+from os import path
+from typing import List, Dict, Any, ClassVar
+
+import torch
+from densepose import add_densepose_config
+from densepose.vis.base import CompoundVisualizer
+from densepose.vis.bounding_box import ScoredBoundingBoxVisualizer
+from densepose.vis.densepose_outputs_vertex import get_texture_atlases, DensePoseOutputsTextureVisualizer, \
+    DensePoseOutputsVertexVisualizer
+from densepose.vis.densepose_results import DensePoseResultsContourVisualizer, \
+    DensePoseResultsFineSegmentationVisualizer, DensePoseResultsUVisualizer, DensePoseResultsVVisualizer
+from densepose.vis.densepose_results_textures import get_texture_atlas, DensePoseResultsVisualizerWithTexture
+from densepose.vis.extractor import create_extractor, CompoundExtractor
+from detectron2.config import CfgNode, get_cfg
+from detectron2.engine.defaults import DefaultPredictor
+
+from annotator.base_annotator import BaseProcessor
+
+config_model = {
+    "densepose_rcnn_R_50_FPN_s1x": {
+        "yaml": 'densepose_rcnn_R_50_FPN_s1x.yaml',
+        "file": "densepose_rcnn_R_50_FPN_s1x.pkl"
+    }
+}
+
+default_conf = config_model["densepose_rcnn_R_50_FPN_s1x"]
+
+
+class DenthPoseProcess(BaseProcessor):
+    VISUALIZERS: ClassVar[Dict[str, object]] = {
+        "dp_contour": DensePoseResultsContourVisualizer,
+        "dp_segm": DensePoseResultsFineSegmentationVisualizer,
+        "dp_u": DensePoseResultsUVisualizer,
+        "dp_v": DensePoseResultsVVisualizer,
+        "dp_iuv_texture": DensePoseResultsVisualizerWithTexture,
+        "dp_cse_texture": DensePoseOutputsTextureVisualizer,
+        "dp_vertex": DensePoseOutputsVertexVisualizer,
+        "bbox": ScoredBoundingBoxVisualizer,
+    }
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        here = path.abspath(path.dirname(__file__))
+        self.cfg, self.model_conf = os.path.join(here, default_conf["yaml"]), os.path.join(here, default_conf["file"])
+        self.predictor = None
+
+    @classmethod
+    def setup_config(
+            cls: type, config_fpath: str, model_fpath: str, opts: List[str]
+    ):
+        cfg = get_cfg()
+        add_densepose_config(cfg)
+        cfg.merge_from_file(config_fpath)
+        if opts:
+            cfg.merge_from_list(opts)
+        cfg.MODEL.WEIGHTS = model_fpath
+        cfg.freeze()
+        return cfg
+
+    @classmethod
+    def create_context(cls: type, vis_specs, cfg: CfgNode, arg_texture_atlas=None, arg_texture_atlases_map=None) -> \
+    Dict[
+        str, Any]:
+        """
+            创建可视化
+        """
+        # vis_specs = visualizations.split(",")
+        visualizers = []
+        extractors = []
+        for vis_spec in vis_specs:
+            texture_atlas = get_texture_atlas(arg_texture_atlas)
+            texture_atlases_dict = get_texture_atlases(arg_texture_atlases_map)
+            vis = cls.VISUALIZERS[vis_spec](
+                cfg=cfg,
+                texture_atlas=texture_atlas,
+                texture_atlases_dict=texture_atlases_dict,
+            )
+            visualizers.append(vis)
+            extractor = create_extractor(vis)
+            extractors.append(extractor)
+        visualizer = CompoundVisualizer(visualizers)
+        extractor = CompoundExtractor(extractors)
+        context = {
+            "extractor": extractor,
+            "visualizer": visualizer,
+            "entry_idx": 0,
+        }
+        return context
+
+    def execute_on_outputs(self, image, context: Dict[str, Any], outputs):
+        visualizer = context["visualizer"]
+        extractor = context["extractor"]
+        # image = np.tile(image[:, :, np.newaxis], [1, 1, 3])
+        data = extractor(outputs)
+        # zero_image = np.zeros(image.shape, dtype=np.uint8)
+        image_vis = visualizer.visualize(image, data)
+        return image_vis
+
+    def __call__(self, img, visualizations=["dp_u", "dp_v", "bbox"], texture_atlas=None, texture_atlases_map=None):
+        opts = []
+        cfg = self.setup_config(config_fpath=self.cfg, model_fpath=self.model_conf, opts=opts)
+        if self.predictor is None:
+            self.predictor = DefaultPredictor(cfg)
+        context = self.create_context(visualizations, cfg, texture_atlas, texture_atlases_map)
+        with torch.no_grad():
+            outputs = self.predictor(img)["instances"]
+            return self.execute_on_outputs(img, context, outputs)
+
+# if __name__ == '__main__':
+#     image_path = "demo.jpeg"
+#     img = cv2.imread(image_path)
+#     process = DenthPoseProcessor()
+#     process(img, "dp_contour,bbox")

annotator/denthPose/readme.md

@@ -0,0 +1,10 @@
+### 安装 detectron2
+pip install 'git+https://github.com/facebookresearch/detectron2.git'
+
+### 安装 DensePose
+pip install git+https://github.com/facebookresearch/detectron2@main#subdirectory=projects/DensePose
+
+
+#
+python apply_net.py show densepose_rcnn_R_50_FPN_s1x.yaml model_final_162be9.pkl demo.jpg dp_contour,bbox --output image_densepose_contour.png
+

config/annotator.yaml

@@ -123,3 +123,6 @@ zoe:
 oneformer:
      process: OneformerDetector
      input: []
+denthPose:
+     process: DenthPoseProcessor
+     input: []