OpenPose(棒人間)を作成したり入手する

pip install controlnet-aux

# -*- coding: utf-8 -*-
import requests
from PIL import Image
from io import BytesIO

from controlnet_aux.processor import Processor

# load image
url = "https://huggingface.co/lllyasviel/sd-controlnet-openpose/resolve/main/images/pose.png"

response = requests.get(url)
img = Image.open(BytesIO(response.content)).convert("RGB").resize((512, 512))

# load processor from processor_id
# options are:
# ["canny", "depth_leres", "depth_leres++", "depth_midas", "depth_zoe", "lineart_anime",
#  "lineart_coarse", "lineart_realistic", "mediapipe_face", "mlsd", "normal_bae", "normal_midas",
#  "openpose", "openpose_face", "openpose_faceonly", "openpose_full", "openpose_hand",
#  "scribble_hed, "scribble_pidinet", "shuffle", "softedge_hed", "sof tedge_hedsafe",
#  "softedge_pidinet", "softedge_pidsafe", "dwpose"]
processor_id = 'openpose_face'
processor_id = 'openpose_faceonly'
processor_id = 'openpose_full'
processor_id = 'openpose_hand'
processor_id = 'openpose'
processor = Processor(processor_id)

processed_image = processor(img, to_pil=True)
processed_image.save(f"{processor_id}.png")

import cv2
import matplotlib.pyplot as plt
import copy
import numpy as np

from src import model
from src import util
from src.body import Body
from src.hand import Hand

body_estimation = Body('model/body_pose_model.pth')
hand_estimation = Hand('model/hand_pose_model.pth')

test_image = 'images/demo.jpg'
oriImg = cv2.imread(test_image)  # B,G,R order

candidate, subset = body_estimation(oriImg)
canvas = copy.deepcopy(oriImg)
cv2.rectangle(canvas, (0, 0), (oriImg.shape[1],oriImg.shape[0]), (0, 0, 0), -1)
canvas = util.draw_bodypose(canvas, candidate, subset)
# detect hand
hands_list = util.handDetect(candidate, subset, oriImg)

all_hand_peaks = []
for x, y, w, is_left in hands_list:
    # cv2.rectangle(canvas, (x, y), (x+w, y+w), (0, 255, 0), 2, lineType=cv2.LINE_AA)
    # cv2.putText(canvas, 'left' if is_left else 'right', (x, y), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)

    # if is_left:
        # plt.imshow(oriImg[y:y+w, x:x+w, :][:, :, [2, 1, 0]])
        # plt.show()
    peaks = hand_estimation(oriImg[y:y+w, x:x+w, :])
    peaks[:, 0] = np.where(peaks[:, 0]==0, peaks[:, 0], peaks[:, 0]+x)
    peaks[:, 1] = np.where(peaks[:, 1]==0, peaks[:, 1], peaks[:, 1]+y)
    # else:
    #     peaks = hand_estimation(cv2.flip(oriImg[y:y+w, x:x+w, :], 1))
    #     peaks[:, 0] = np.where(peaks[:, 0]==0, peaks[:, 0], w-peaks[:, 0]-1+x)
    #     peaks[:, 1] = np.where(peaks[:, 1]==0, peaks[:, 1], peaks[:, 1]+y)
    #     print(peaks)
    all_hand_peaks.append(peaks)

canvas = util.draw_handpose(canvas, all_hand_peaks)

plt.imshow(canvas[:, :, [2, 1, 0]])
plt.axis('off')
plt.show()