# load the model
#model_path = os.path.join("./models", args.model+"_%d_adam_best.t7"%args.inp_dim)
model_path = "./models/mobilenetv2_224_adam_best.t7"
net = CoordRegressionNetwork(n_locations=6, backbone=args.model).to("cpu")
e = ResEstimator(model_path, net, args.inp_dim)
# # initial the camera
# cam = cv2.VideoCapture(args.camera)
# ret_val, image = cam.read()
# image = crop_camera(image)
# while True:
# # read image from the camera and preprocess
# ret_val , image = cam.read()
# image = crop_camera(image)
# # forward the image
# humans = e.inference(image)
# image = ResEstimator.draw_humans(image, humans, imgcopy=False)
# cv2.imshow('MobilePose Demo', image)
# if cv2.waitKey(1) == 27: # ESC
# break
# cv2.destroyAllWindows()
# single person rgb image test
image = cv2.imread("./12.jpg")
humans = e.inference(image)
image = ResEstimator.draw_humans(image, humans, imgcopy=False)
cv2.imwrite("./results/out.png", image)
ret_val, image = cam.read()
image = crop_camera(image)
# small pointy finger image offset from image's border
x_offset = y_offset = 100
value_threshold = 0.6
while True:
# read image from the camera and preprocess
ret_val, image = cam.read()
image = crop_camera(image)
# forward the image
humans = e.inference(image)
image, lr, amount = ResEstimator.draw_humans(image,
humans,
imgcopy=False)
print("lr, amount", lr, amount)
if (lr == "L"):
img_l = cv2.imread(
r'/home/jesse/Koodiprojektit/Junction2019/mobilepose-pythorch/MobilePose-pytorch/left_2.png',
1)
img_l_rs = cv2.resize(img_l, (100, 100),
interpolation=cv2.INTER_AREA)
if (amount > value_threshold):
asd = submitData("L")
#image[y_offset:y_offset+img.shape[0], x_offset:x_offset+img.shape[1]] = img
elif (lr == "R"):
#print("Right")
img_r = cv2.imread(
r'/home/jesse/Koodiprojektit/Junction2019/mobilepose-pythorch/MobilePose-pytorch/right_2.png',