module.py

# coding:utf-8
# Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import os
import copy
from collections import OrderedDict

import cv2
import paddle
import paddle.nn as nn
import numpy as np
from paddlehub.module.module import moduleinfo
import paddlehub.process.transforms as T

import openpose_body_estimation.processor as P


@moduleinfo(
    name="openpose_body_estimation",
    type="CV/image_editing",
    author="paddlepaddle",
    author_email="",
    summary="Openpose_body_estimation is a body pose estimation model based on Realtime Multi-Person 2D Pose \
            Estimation using Part Affinity Fields.",
    version="1.0.0")
class BodyPoseModel(nn.Layer):
    """
    BodyPoseModel

    Args:
        load_checkpoint(str): Checkpoint save path, default is None.
        visualization (bool): Whether to save the estimation result. Default is True.
    """

    def __init__(self, load_checkpoint: str = None, visualization: bool = True):
        super(BodyPoseModel, self).__init__()

        self.resize_func = T.ResizeScaling()
        self.norm_func = T.Normalize(std=[1, 1, 1])
        self.pad_func = P.PadDownRight()
        self.remove_pad = P.RemovePadding()
        self.get_peak = P.GetPeak()
        self.get_connection = P.Connection()
        self.get_candidate = P.Candidate()
        self.draw_pose = P.DrawPose()
        self.visualization = visualization

        no_relu_layers = ['conv5_5_CPM_L1', 'conv5_5_CPM_L2', 'Mconv7_stage2_L1', \
                          'Mconv7_stage2_L2', 'Mconv7_stage3_L1', 'Mconv7_stage3_L2', \
                          'Mconv7_stage4_L1', 'Mconv7_stage4_L2', 'Mconv7_stage5_L1', \
                          'Mconv7_stage5_L2', 'Mconv7_stage6_L1', 'Mconv7_stage6_L1']
        blocks = {}
        block0 = OrderedDict([('conv1_1', [3, 64, 3, 1, 1]), ('conv1_2', [64, 64, 3, 1, 1]), ('pool1_stage1', [2, 2,
                                                                                                               0]),
                              ('conv2_1', [64, 128, 3, 1, 1]), ('conv2_2', [128, 128, 3, 1, 1]),
                              ('pool2_stage1', [2, 2, 0]), ('conv3_1', [128, 256, 3, 1, 1]),
                              ('conv3_2', [256, 256, 3, 1, 1]), ('conv3_3', [256, 256, 3, 1, 1]),
                              ('conv3_4', [256, 256, 3, 1, 1]), ('pool3_stage1', [2, 2, 0]),
                              ('conv4_1', [256, 512, 3, 1, 1]), ('conv4_2', [512, 512, 3, 1, 1]),
                              ('conv4_3_CPM', [512, 256, 3, 1, 1]), ('conv4_4_CPM', [256, 128, 3, 1, 1])])

        block1_1 = OrderedDict([('conv5_1_CPM_L1', [128, 128, 3, 1, 1]), ('conv5_2_CPM_L1', [128, 128, 3, 1, 1]),
                                ('conv5_3_CPM_L1', [128, 128, 3, 1, 1]), ('conv5_4_CPM_L1', [128, 512, 1, 1, 0]),
                                ('conv5_5_CPM_L1', [512, 38, 1, 1, 0])])

        block1_2 = OrderedDict([('conv5_1_CPM_L2', [128, 128, 3, 1, 1]), ('conv5_2_CPM_L2', [128, 128, 3, 1, 1]),
                                ('conv5_3_CPM_L2', [128, 128, 3, 1, 1]), ('conv5_4_CPM_L2', [128, 512, 1, 1, 0]),
                                ('conv5_5_CPM_L2', [512, 19, 1, 1, 0])])
        blocks['block1_1'] = block1_1
        blocks['block1_2'] = block1_2

        self.model0 = self.make_layers(block0, no_relu_layers)

        for i in range(2, 7):
            blocks['block%d_1' % i] = OrderedDict([('Mconv1_stage%d_L1' % i, [185, 128, 7, 1, 3]),
                                                   ('Mconv2_stage%d_L1' % i, [128, 128, 7, 1, 3]),
                                                   ('Mconv3_stage%d_L1' % i, [128, 128, 7, 1, 3]),
                                                   ('Mconv4_stage%d_L1' % i, [128, 128, 7, 1, 3]),
                                                   ('Mconv5_stage%d_L1' % i, [128, 128, 7, 1, 3]),
                                                   ('Mconv6_stage%d_L1' % i, [128, 128, 1, 1, 0]),
                                                   ('Mconv7_stage%d_L1' % i, [128, 38, 1, 1, 0])])

            blocks['block%d_2' % i] = OrderedDict([('Mconv1_stage%d_L2' % i, [185, 128, 7, 1, 3]),
                                                   ('Mconv2_stage%d_L2' % i, [128, 128, 7, 1, 3]),
                                                   ('Mconv3_stage%d_L2' % i, [128, 128, 7, 1, 3]),
                                                   ('Mconv4_stage%d_L2' % i, [128, 128, 7, 1, 3]),
                                                   ('Mconv5_stage%d_L2' % i, [128, 128, 7, 1, 3]),
                                                   ('Mconv6_stage%d_L2' % i, [128, 128, 1, 1, 0]),
                                                   ('Mconv7_stage%d_L2' % i, [128, 19, 1, 1, 0])])

        for k in blocks.keys():
            blocks[k] = self.make_layers(blocks[k], no_relu_layers)

        self.model1_1 = blocks['block1_1']
        self.model2_1 = blocks['block2_1']
        self.model3_1 = blocks['block3_1']
        self.model4_1 = blocks['block4_1']
        self.model5_1 = blocks['block5_1']
        self.model6_1 = blocks['block6_1']

        self.model1_2 = blocks['block1_2']
        self.model2_2 = blocks['block2_2']
        self.model3_2 = blocks['block3_2']
        self.model4_2 = blocks['block4_2']
        self.model5_2 = blocks['block5_2']
        self.model6_2 = blocks['block6_2']

        if load_checkpoint is not None:
            model_dict = paddle.load(load_checkpoint)[0]
            self.set_dict(model_dict)
            print("load custom checkpoint success")

        else:
            checkpoint = os.path.join(self.directory, 'openpose_body.pdparams')
            if not os.path.exists(checkpoint):
                os.system('wget https://paddlehub.bj.bcebos.com/dygraph/pose/openpose_body.pdparams -O ' + checkpoint)
            model_dict = paddle.load(checkpoint)[0]
            self.set_dict(model_dict)
            print("load pretrained checkpoint success")

    def make_layers(self, block: dict, no_relu_layers: list):
        layers = []
        for layer_name, v in block.items():
            if 'pool' in layer_name:
                layer = nn.MaxPool2d(kernel_size=v[0], stride=v[1], padding=v[2])
                layers.append((layer_name, layer))
            else:
                conv2d = nn.Conv2d(in_channels=v[0], out_channels=v[1], kernel_size=v[2], stride=v[3], padding=v[4])
                layers.append((layer_name, conv2d))
                if layer_name not in no_relu_layers:
                    layers.append(('relu_' + layer_name, nn.ReLU()))
        layers = tuple(layers)
        return nn.Sequential(*layers)

    def transform(self, orgimg: np.ndarray, scale_search: float = 0.5):
        process = self.resize_func(orgimg, scale_search)
        imageToTest_padded, pad = self.pad_func(process)
        process = self.norm_func(imageToTest_padded)
        process = np.ascontiguousarray(np.transpose(process[:, :, :, np.newaxis], (3, 2, 0, 1))).astype("float32")

        return process, imageToTest_padded, pad

    def forward(self, x: paddle.Tensor):

        out1 = self.model0(x)

        out1_1 = self.model1_1(out1)
        out1_2 = self.model1_2(out1)
        out2 = paddle.concat([out1_1, out1_2, out1], 1)

        out2_1 = self.model2_1(out2)
        out2_2 = self.model2_2(out2)
        out3 = paddle.concat([out2_1, out2_2, out1], 1)

        out3_1 = self.model3_1(out3)
        out3_2 = self.model3_2(out3)
        out4 = paddle.concat([out3_1, out3_2, out1], 1)

        out4_1 = self.model4_1(out4)
        out4_2 = self.model4_2(out4)
        out5 = paddle.concat([out4_1, out4_2, out1], 1)

        out5_1 = self.model5_1(out5)
        out5_2 = self.model5_2(out5)
        out6 = paddle.concat([out5_1, out5_2, out1], 1)

        out6_1 = self.model6_1(out6)
        out6_2 = self.model6_2(out6)

        return out6_1, out6_2

    def predict(self, img_path: str, save_path: str = "result"):
        self.eval()
        orgImg = cv2.imread(img_path)
        data, imageToTest_padded, pad = self.transform(orgImg)
        Mconv7_stage6_L1, Mconv7_stage6_L2 = self.forward(paddle.to_tensor(data))
        Mconv7_stage6_L1 = Mconv7_stage6_L1.numpy()
        Mconv7_stage6_L2 = Mconv7_stage6_L2.numpy()

        heatmap_avg = self.remove_pad(Mconv7_stage6_L2, imageToTest_padded, orgImg, pad)
        paf_avg = self.remove_pad(Mconv7_stage6_L1, imageToTest_padded, orgImg, pad)

        all_peaks = self.get_peak(heatmap_avg)
        connection_all, special_k = self.get_connection(all_peaks, paf_avg, orgImg)
        candidate, subset = self.get_candidate(all_peaks, connection_all, special_k)

        if self.visualization:
            canvas = copy.deepcopy(orgImg)
            canvas = self.draw_pose(canvas, candidate, subset)
            if not os.path.exists(save_path):
                os.mkdir(save_path)
            save_path = os.path.join(save_path, img_path.rsplit("/", 1)[-1])
            cv2.imwrite(save_path, canvas)
        return candidate, subset


if __name__ == "__main__":

    paddle.disable_static()
    model = BodyPoseModel()
    model.eval()
    out1, out2 = model.predict("demo.jpg")