PoseCorrection/backend/demo.py

import copy

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

from src import util
from src.body import Body

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

test_image = 'pose_source_images/4-0.png'
oriImg = cv2.imread(test_image)  # B,G,R order
candidate, subset = body_estimation(oriImg)
print(candidate)
np.save('pose_temp_data/4-0.npy', candidate)
canvas = copy.deepcopy(oriImg)
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.savefig('pose_processed_images/4-0-result.jpg')
plt.show()