def growing(self):
self.game.tps_max += 0.2
if self.game.tps_max > 30:
self.game.tps_max = 30
index = len(self.sneak)
if index > 1:
self.sneak.append(
Body(self.game, index,
self.sneak[len(self.sneak) - 1].last_pos,
self.sneak[len(self.sneak) - 1].dir,
self.sneak[len(self.sneak) - 2].dir))
elif index > 0:
self.sneak.append(
Body(self.game, index,
self.sneak[len(self.sneak) - 1].last_pos,
self.sneak[len(self.sneak) - 1].dir, self.head.dir))
def detect_keypoint(test_image, is_vis):
body_estimation = Body('model/body_pose_model.pth')
hand_estimation = Hand('model/hand_pose_model.pth')
oriImg = cv2.imread(test_image) # B,G,R order
# detect body
# subset: n*20 array, n is the human_number in the index, 0-17 is the index in candidate, 18 is the total score, 19 is the total parts
# candidate: m*4, m is the keypoint number in the image, [x, y, confidence, id]
candidate, subset = body_estimation(
oriImg
) # candidate: output the keypoints([25, 4]), x, y, score, keypoint_index
canvas = copy.deepcopy(oriImg)
canvas, bodypoints = util.draw_bodypose(canvas, candidate, subset)
# detect hand
hands_list = util.handDetect(candidate, subset, oriImg)
all_hand_peaks = []
hand_personid_isleft = []
for x, y, w, is_left, person_id 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)
hand_personid_isleft.append([person_id, is_left])
# all_hand_peaks: [p, 21, 2] p is the hand number in the image
# hand_personid_isleft: [p, 2] is_isleft, person_id
all_hand_peaks = np.asarray(all_hand_peaks)
hand_personid_isleft = np.asarray(hand_personid_isleft)
canvas = util.draw_handpose(canvas, all_hand_peaks)
if is_vis:
plt.imshow(canvas[:, :, [2, 1, 0]])
plt.axis('off')
plt.show()
return bodypoints, all_hand_peaks, hand_personid_isleft
def __init__(self, game, states):
self.game = game
self.game.tps_max = 5
self.States = states
self.border_color = (int(87 * 0.75), int(189 * 0.75), int(134 * 0.75))
self.screen_size = self.game.screen.get_size()
self.matrix_size = Vector2(20, 19)
self.field_size = Vector2((self.screen_size[0] * 32) // 1280,
(self.screen_size[1] * 32) // 720)
self.draw_start_pos = Vector2(
self.screen_size[0] / 2.0 -
(self.matrix_size.x * self.field_size.x) / 2.0, 80)
# Apple config
self.apple_pos = Vector2(randint(1, self.matrix_size.x - 1),
randint(1, self.matrix_size.y - 1))
self.apple_color = (250, 50, 50)
self.apple_image = pygame.image.load("Data/apple2.png")
self.apple_image = pygame.transform.scale(
self.apple_image, (int(self.field_size.x), int(self.field_size.y)))
self.background_image = pygame.image.load("Data/background.png")
self.foreground_image = pygame.image.load("Data/foreground.png")
self.background_size = 1
self.background_image = pygame.transform.scale(
self.background_image,
(int(self.screen_size[0] * self.background_size),
int(self.screen_size[1] * self.background_size)))
self.foreground_image = pygame.transform.scale(
self.foreground_image,
(int(self.screen_size[0] * self.background_size),
int(self.screen_size[1] * self.background_size)))
self.head = Head(game, states)
self.sneak = [Body(self.game, 0, self.head.last_pos, 1, 1)]
self.points = len(self.sneak) - 1
# Music config
pygame.mixer.music.load("Data/Popcorn.mp3")
pygame.mixer.music.play(-1)
pygame.mixer.Channel(0).stop()
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
import time
body_estimation = Body('model/body_pose_model.pth')
input_filename = 'london-men-1'
capture = cv2.VideoCapture("videos/{}.mp4".format(input_filename))
# Default resolutions of the frame are obtained.The default resolutions are system dependent.
# We convert the resolutions from float to integer.
frame_width = int(capture.get(3))
frame_height = int(capture.get(4))
output_filename = "{}-output".format(input_filename)
# Define the codec and create VideoWriter object.
out = cv2.VideoWriter("results/{}.avi".format(output_filename),
cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), 10,
(frame_width, frame_height))
frame_count = 0
start = time.time()
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('/home2/lgfm95/openposehzzone/model/body_pose_model.pth')
hand_estimation = Hand('/home2/lgfm95/openposehzzone/model/hand_pose_model.pth')
def main(oriImg):
shape0 = oriImg.shape
candidate, subset = body_estimation(oriImg)
canvas = copy.deepcopy(oriImg)
shape1 = canvas.shape
canvas = util.draw_bodypose(canvas, candidate, subset)
# detect hand
hands_list = util.handDetect(candidate, subset, oriImg)
all_hand_peaks = []
shape2 = canvas.shape
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()
def __init__(self):
super().__init__()
model_dir = os.path.dirname(__file__) + '/model/'
self.body_estimation = Body(model_dir + 'body_pose_model.pth')
self.hand_estimation = Hand(model_dir + 'hand_pose_model.pth')
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
body_estimation = Body(
'model/body_pose_model.pth') # 포즈 예측기, 인자: 학습 시킨 모델 파라미터 저장 경로
pose_compare = util.Pose_compare() # 내가 만든 모듈, 두개의 자세에서 오차를 알려줌
test_image1 = 'images/aa.jpg' # 비교할 이미지 1
test_image2 = 'images/dragon2.jpg' # 비교할 이미지 2
oriImg1 = cv2.imread(test_image1) # 이미지 불러오기
candidate1, subset1 = body_estimation(
oriImg1) # candidate: x, y, score, id | subset: index
# x와 y가 관절의 위치, index는 관절의 이름이라고 생각하면 됨
# score는 단순하게 확실 정도라고 보면됨
# id는 무시해도 되고
oriImg2 = cv2.imread(test_image2) # 이미지2 불러오기
candidate2, subset2 = body_estimation(oriImg2)
canvas1 = copy.deepcopy(oriImg1) # 이미지 복사
canvas1 = util.draw_bodypose(canvas1, candidate1, subset1) # 화면에 예측한 스켈레톤을 보여줌
canvas2 = copy.deepcopy(oriImg2)
canvas2 = util.draw_bodypose(canvas2, candidate2, subset2)
