Renamed new wrapper script.
This commit is contained in:
Daniel
515
scripts/utils_2d_pose.py
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515
scripts/utils_2d_pose.py
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@ -0,0 +1,515 @@
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import math
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import os
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from abc import ABC, abstractmethod
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from typing import List
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import cv2
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import numpy as np
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import onnxruntime as ort
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from tqdm import tqdm
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# ==================================================================================================
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class BaseModel(ABC):
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def __init__(self, model_path: str, warmup: int):
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self.model_path = model_path
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self.runtime = ""
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if not os.path.exists(model_path):
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raise FileNotFoundError("File not found:", model_path)
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if model_path.endswith(".onnx"):
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self.init_onnxruntime(model_path)
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self.runtime = "ort"
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else:
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raise ValueError("Unsupported model format:", model_path)
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if warmup > 0:
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print("Running warmup for '{}' ...".format(self.__class__.__name__))
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self.warmup(warmup // 2)
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self.warmup(warmup // 2)
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def init_onnxruntime(self, model_path):
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usetrt = True
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usegpu = True
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self.opt = ort.SessionOptions()
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providers = ort.get_available_providers()
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# ort.set_default_logger_severity(1)
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self.providers = []
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if usetrt and "TensorrtExecutionProvider" in providers:
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self.providers.append(
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(
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"TensorrtExecutionProvider",
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{
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"trt_engine_cache_enable": True,
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"trt_engine_cache_path": "/RapidPoseTriangulation/data/trt_cache/",
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},
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)
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)
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elif usegpu and "CUDAExecutionProvider" in providers:
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self.providers.append("CUDAExecutionProvider")
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else:
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self.providers.append("CPUExecutionProvider")
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print("Using providers:", self.providers)
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self.session = ort.InferenceSession(
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model_path, providers=self.providers, sess_options=self.opt
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)
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self.input_names = [input.name for input in self.session.get_inputs()]
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self.input_shapes = [input.shape for input in self.session.get_inputs()]
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input_types = [input.type for input in self.session.get_inputs()]
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self.input_types = []
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for i in range(len(input_types)):
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input_type = input_types[i]
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if input_type == "tensor(float32)":
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itype = np.float32
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elif input_type == "tensor(float16)":
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itype = np.float16
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elif input_type == "tensor(int32)":
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itype = np.int32
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elif input_type == "tensor(uint8)":
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itype = np.uint8
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else:
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raise ValueError("Undefined input type:", input_type)
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self.input_types.append(itype)
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@abstractmethod
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def preprocess(self, **kwargs):
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pass
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@abstractmethod
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def postprocess(self, **kwargs):
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pass
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def warmup(self, epoch: int):
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np.random.seed(42)
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for _ in tqdm(range(epoch)):
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inputs = {}
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for i in range(len(self.input_names)):
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iname = self.input_names[i]
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if "image" in iname:
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ishape = list(self.input_shapes[i])
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if "batch_size" in ishape:
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max_batch_size = 10
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ishape[0] = np.random.choice(list(range(1, max_batch_size + 1)))
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tensor = np.random.random(ishape)
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tensor = tensor * 255
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else:
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raise ValueError("Undefined input type:", iname)
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tensor = tensor.astype(self.input_types[i])
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inputs[iname] = tensor
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self.call_model_ort(list(inputs.values()))
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def call_model_ort(self, tensor):
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inputs = {}
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for i in range(len(self.input_names)):
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iname = self.input_names[i]
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inputs[iname] = tensor[i]
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result = self.session.run(None, inputs)
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return result
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def __call__(self, **kwargs):
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tensor = self.preprocess(**kwargs)
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result = self.call_model_ort(tensor)
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output = self.postprocess(result=result, **kwargs)
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return output
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# ==================================================================================================
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class LetterBox:
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def __init__(self, target_size, fill_value=0):
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self.target_size = target_size
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self.fill_value = fill_value
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def calc_params(self, ishape):
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img_h, img_w = ishape[:2]
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target_h, target_w = self.target_size
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scale = min(target_w / img_w, target_h / img_h)
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new_w = round(img_w * scale)
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new_h = round(img_h * scale)
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pad_w = target_w - new_w
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pad_h = target_h - new_h
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pad_left = pad_w // 2
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pad_top = pad_h // 2
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pad_right = pad_w - pad_left
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pad_bottom = pad_h - pad_top
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paddings = (pad_left, pad_right, pad_top, pad_bottom)
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return paddings, scale, (new_w, new_h)
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def resize_image(self, image):
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paddings, _, new_size = self.calc_params(image.shape)
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# Resize the image
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new_w, new_h = new_size
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resized_img = cv2.resize(
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image,
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(new_w, new_h),
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interpolation=cv2.INTER_NEAREST,
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)
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# Optionally pad the image
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pad_left, pad_right, pad_top, pad_bottom = paddings
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if pad_left == 0 and pad_right == 0 and pad_top == 0 and pad_bottom == 0:
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final_img = resized_img
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else:
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final_img = cv2.copyMakeBorder(
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resized_img,
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pad_top,
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pad_bottom,
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pad_left,
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pad_right,
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borderType=cv2.BORDER_CONSTANT,
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value=[self.fill_value, self.fill_value, self.fill_value],
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)
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return final_img
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# ==================================================================================================
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class BoxCrop:
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def __init__(self, target_size, padding_scale=1.0, fill_value=0):
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self.target_size = target_size
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self.padding_scale = padding_scale
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self.fill_value = fill_value
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def calc_params(self, ishape, bbox):
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start_x, start_y, end_x, end_y = bbox[0], bbox[1], bbox[2], bbox[3]
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target_h, target_w = self.target_size
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# Calculate original bounding box center
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center_x = (start_x + end_x) / 2.0
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center_y = (start_y + end_y) / 2.0
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# Scale the bounding box by the padding_scale
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bbox_w = end_x - start_x
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bbox_h = end_y - start_y
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scaled_w = bbox_w * self.padding_scale
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scaled_h = bbox_h * self.padding_scale
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# Calculate the aspect ratios
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bbox_aspect = scaled_w / scaled_h
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target_aspect = target_w / target_h
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# Adjust the scaled bounding box to match the target aspect ratio
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if bbox_aspect > target_aspect:
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adjusted_h = scaled_w / target_aspect
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adjusted_w = scaled_w
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else:
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adjusted_w = scaled_h * target_aspect
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adjusted_h = scaled_h
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# Calculate scaled bounding box coordinates
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bbox_w = adjusted_w
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bbox_h = adjusted_h
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new_start_x = center_x - bbox_w / 2.0
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new_start_y = center_y - bbox_h / 2.0
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new_end_x = center_x + bbox_w / 2.0
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new_end_y = center_y + bbox_h / 2.0
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# Round the box coordinates
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start_x = int(math.floor(new_start_x))
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start_y = int(math.floor(new_start_y))
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end_x = int(math.ceil(new_end_x))
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end_y = int(math.ceil(new_end_y))
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# Define the new box coordinates
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new_start_x = max(0, start_x)
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new_start_y = max(0, start_y)
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new_end_x = min(ishape[1] - 1, end_x)
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new_end_y = min(ishape[0] - 1, end_y)
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new_box = [new_start_x, new_start_y, new_end_x, new_end_y]
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# Calculate resized crop size
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bbox_w = new_box[2] - new_box[0]
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bbox_h = new_box[3] - new_box[1]
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scale = min(target_w / bbox_w, target_h / bbox_h)
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new_w = round(bbox_w * scale)
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new_h = round(bbox_h * scale)
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# Calculate paddings
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pad_w = target_w - new_w
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pad_h = target_h - new_h
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pad_left, pad_right, pad_top, pad_bottom = 0, 0, 0, 0
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if pad_w > 0:
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if start_x < 0:
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pad_left = pad_w
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pad_right = 0
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elif end_x > ishape[1]:
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pad_left = 0
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pad_right = pad_w
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else:
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# Can be caused by bbox rounding
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pad_left = pad_w // 2
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pad_right = pad_w - pad_left
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if pad_h > 0:
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if start_y < 0:
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pad_top = pad_h
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pad_bottom = 0
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elif end_y > ishape[0]:
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pad_top = 0
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pad_bottom = pad_h
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else:
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# Can be caused by bbox rounding
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pad_top = pad_h // 2
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pad_bottom = pad_h - pad_top
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paddings = (pad_left, pad_right, pad_top, pad_bottom)
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return paddings, scale, new_box, (new_w, new_h)
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def crop_resize_box(self, image, bbox):
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paddings, _, new_box, new_size = self.calc_params(image.shape, bbox)
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# Extract the bounding box
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cropped_img = image[new_box[1] : new_box[3], new_box[0] : new_box[2]]
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# Resize the image
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new_w, new_h = new_size
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resized_img = cv2.resize(
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cropped_img,
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(new_w, new_h),
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interpolation=cv2.INTER_NEAREST,
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)
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# Optionally pad the image
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pad_left, pad_right, pad_top, pad_bottom = paddings
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if pad_left == 0 and pad_right == 0 and pad_top == 0 and pad_bottom == 0:
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final_img = resized_img
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else:
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final_img = cv2.copyMakeBorder(
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resized_img,
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pad_top,
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pad_bottom,
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pad_left,
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pad_right,
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borderType=cv2.BORDER_CONSTANT,
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value=[self.fill_value, self.fill_value, self.fill_value],
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)
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return final_img
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# ==================================================================================================
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class RTMDet(BaseModel):
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def __init__(
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self,
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model_path: str,
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conf_threshold: float,
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min_area_fraction: float,
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warmup: int = 30,
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):
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super(RTMDet, self).__init__(model_path, warmup)
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self.target_size = (320, 320)
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self.conf_threshold = conf_threshold
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self.letterbox = LetterBox(self.target_size, fill_value=114)
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img_area = self.target_size[0] * self.target_size[1]
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self.min_area = img_area * min_area_fraction
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def preprocess(self, image: np.ndarray):
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image = self.letterbox.resize_image(image)
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tensor = np.asarray(image).astype(self.input_types[0], copy=False)
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tensor = np.expand_dims(tensor, axis=0)
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tensor = [tensor]
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return tensor
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def postprocess(self, result: List[np.ndarray], image: np.ndarray):
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boxes = np.squeeze(result[0], axis=0)
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classes = np.squeeze(result[1], axis=0)
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human_class = classes[:] == 0
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boxes = boxes[human_class]
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keep = boxes[:, 4] > self.conf_threshold
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boxes = boxes[keep]
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if len(boxes) == 0:
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return np.array([])
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# Drop boxes with too small area
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boxes = boxes.astype(np.float32)
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areas = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
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keep = areas >= self.min_area
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boxes = boxes[keep]
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if len(boxes) == 0:
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return np.array([])
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paddings, scale, _ = self.letterbox.calc_params(image.shape)
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boxes[:, 0] -= paddings[0]
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boxes[:, 2] -= paddings[0]
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boxes[:, 1] -= paddings[2]
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boxes[:, 3] -= paddings[2]
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boxes = np.maximum(boxes, 0)
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th, tw = self.target_size
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pad_w = paddings[0] + paddings[1]
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pad_h = paddings[2] + paddings[3]
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max_w = tw - pad_w - 1
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max_h = th - pad_h - 1
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boxes[:, 0] = np.minimum(boxes[:, 0], max_w)
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boxes[:, 1] = np.minimum(boxes[:, 1], max_h)
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boxes[:, 2] = np.minimum(boxes[:, 2], max_w)
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boxes[:, 3] = np.minimum(boxes[:, 3], max_h)
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boxes[:, 0:4] /= scale
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return boxes
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# ==================================================================================================
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class RTMPose(BaseModel):
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def __init__(self, model_path: str, warmup: int = 30):
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super(RTMPose, self).__init__(model_path, warmup)
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self.target_size = (384, 288)
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self.boxcrop = BoxCrop(self.target_size, padding_scale=1.25, fill_value=0)
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def preprocess(self, image: np.ndarray, bboxes: np.ndarray):
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cutouts = []
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for i in range(len(bboxes)):
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bbox = np.asarray(bboxes[i])[0:4]
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bbox += np.array([-0.5, -0.5, 0.5 - 1e-8, 0.5 - 1e-8])
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bbox = bbox.round().astype(np.int32)
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region = self.boxcrop.crop_resize_box(image, bbox)
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tensor = np.asarray(region).astype(self.input_types[0], copy=False)
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cutouts.append(tensor)
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if len(bboxes) == 1:
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cutouts = np.expand_dims(cutouts[0], axis=0)
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else:
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cutouts = np.stack(cutouts, axis=0)
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tensor = [cutouts]
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return tensor
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def postprocess(
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self, result: List[np.ndarray], image: np.ndarray, bboxes: np.ndarray
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):
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kpts = []
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for i in range(len(bboxes)):
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scores = np.clip(result[1][i], 0, 1)
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kp = np.concatenate(
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[result[0][i], np.expand_dims(scores, axis=-1)], axis=-1
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)
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paddings, scale, bbox, _ = self.boxcrop.calc_params(image.shape, bboxes[i])
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kp[:, 0] -= paddings[0]
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kp[:, 1] -= paddings[2]
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kp[:, 0:2] /= scale
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kp[:, 0] += bbox[0]
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kp[:, 1] += bbox[1]
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kp[:, 0:2] = np.maximum(kp[:, 0:2], 0)
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max_w = image.shape[1] - 1
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max_h = image.shape[0] - 1
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kp[:, 0] = np.minimum(kp[:, 0], max_w)
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kp[:, 1] = np.minimum(kp[:, 1], max_h)
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kpts.append(kp)
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return kpts
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# ==================================================================================================
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class TopDown:
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def __init__(
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self,
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det_model_path: str,
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pose_model_path: str,
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box_conf_threshold: float,
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box_min_area: float,
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warmup: int = 30,
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):
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self.batch_poses = bool("Bx" in pose_model_path)
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self.det_model = RTMDet(
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det_model_path, box_conf_threshold, box_min_area, warmup
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)
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self.pose_model = RTMPose(pose_model_path, warmup)
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def predict(self, image):
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boxes = self.det_model(image=image)
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if len(boxes) == 0:
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return []
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results = []
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if self.batch_poses:
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results = self.pose_model(image=image, bboxes=boxes)
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else:
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for i in range(boxes.shape[0]):
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kp = self.pose_model(image=image, bboxes=[boxes[i]])
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results.append(kp[0])
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return results
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# ==================================================================================================
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def load_model(min_bbox_score=0.3, min_bbox_area=0.1 * 0.1, batch_poses=False):
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print("Loading 2D model ...")
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model = TopDown(
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"/RapidPoseTriangulation/extras/mmdeploy/exports/rtmdet-nano_1x320x320x3_fp16_extra-steps.onnx",
|
||||
f"/RapidPoseTriangulation/extras/mmdeploy/exports/rtmpose-m_{'B' if batch_poses else '1'}x384x288x3_fp16_extra-steps.onnx",
|
||||
box_conf_threshold=min_bbox_score,
|
||||
box_min_area=min_bbox_area,
|
||||
warmup=30,
|
||||
)
|
||||
|
||||
print("Loaded 2D model")
|
||||
return model
|
||||
|
||||
|
||||
def load_wb_model(min_bbox_score=0.3, min_bbox_area=0.1 * 0.1, batch_poses=False):
|
||||
print("Loading 2D-WB model ...")
|
||||
|
||||
# The FP16 pose model is much worse than the FP32 for whole-body keypoints
|
||||
model = TopDown(
|
||||
"/RapidPoseTriangulation/extras/mmdeploy/exports/rtmdet-nano_1x320x320x3_fp16_extra-steps.onnx",
|
||||
f"/RapidPoseTriangulation/extras/mmdeploy/exports/rtmpose-l_wb_{'B' if batch_poses else '1'}x384x288x3_extra-steps.onnx",
|
||||
box_conf_threshold=min_bbox_score,
|
||||
box_min_area=min_bbox_area,
|
||||
warmup=30,
|
||||
)
|
||||
|
||||
print("Loaded 2D-WB model")
|
||||
return model
|
||||
|
||||
|
||||
# ==================================================================================================
|
||||
|
||||
|
||||
def get_2d_pose(model, imgs, num_joints=17):
|
||||
|
||||
new_poses = []
|
||||
for i in range(len(imgs)):
|
||||
img = imgs[i]
|
||||
dets = model.predict(img)
|
||||
|
||||
if len(dets) == 0:
|
||||
poses = np.zeros([1, num_joints, 3], dtype=float)
|
||||
else:
|
||||
poses = np.asarray(dets, dtype=float)
|
||||
new_poses.append(poses)
|
||||
|
||||
return new_poses
|
||||
Reference in New Issue
Block a user