def init_model():
model_path = 'models/Grasp_model'
nsamples = 300
max_batchsize = 128
gpu_id = -1
global nbatches, batchsize
## Set up model
if nsamples > max_batchsize:
batchsize = max_batchsize
nbatches = int(nsamples / max_batchsize) + 1
else:
batchsize = nsamples
nbatches = 1
print('Loading grasp model')
st_time = time.time()
G = grasp_obj(model_path, gpu_id)
G.BATCH_SIZE = batchsize
G.test_init()
return G
def predictGraspOnImage(I, objLoc):
# default parameters
model_path = '/home/team18/Grasp-Detector-master/models/Grasp_model' # 'Grasp model you want to use'
nsamples = 200 # 'Number of patch samples. More the better, but it\'ll get slower'
nbest = 1 # 'Number of grasps to display'
gscale = 0.234375 # 'Scale of grasp. Default is the one used in the paper, given a 720X1280 res image'
imsize = max(I.shape[:2])
gsize = int(gscale * imsize) # Size of grasp patch
# max_batchsize = 128
gpu_id = 0 # 'GPU device id; -1 for cpu'
# Set up model
batchsize = nsamples
nbatches = 1
print(
"\n-------------------------------------------------------------------"
)
print('Loading grasp model')
st_time = time.time()
G = grasp_obj(model_path, gpu_id)
G.BATCH_SIZE = batchsize
# G.test_close() # 2/7/2018 added by BW
G.test_init()
P = Predictors(I, G)
print('Time taken: {}s\n'.format(time.time() - st_time))
fc8_predictions = []
patch_Hs = []
patch_Ws = []
print('Predicting on samples')
st_time = time.time()
for _ in range(nbatches):
P.graspNet_grasp(objLoc, patch_size=gsize, num_samples=batchsize)
fc8_predictions.append(P.fc8_norm_vals)
patch_Hs.append(P.patch_hs)
patch_Ws.append(P.patch_ws)
G.test_close() # 12/19/2017 added by BW
fc8_predictions = np.concatenate(fc8_predictions)
patch_Hs = np.concatenate(patch_Hs)
patch_Ws = np.concatenate(patch_Ws)
r = np.sort(fc8_predictions, axis=None)
r_no_keep = r[-nbest]
print('Time taken: {}s'.format(time.time() - st_time))
print(
"-------------------------------------------------------------------\n"
)
for pindex in range(fc8_predictions.shape[0]):
for tindex in range(fc8_predictions.shape[1]):
if fc8_predictions[pindex, tindex] < r_no_keep:
continue
else:
# tindex = 0
I = drawRectangle(I, patch_Hs[pindex], patch_Ws[pindex],
tindex, gsize)
bestH = patch_Hs[pindex]
bestW = patch_Ws[pindex]
bestGraspAngle = tindex * (np.pi / 18.0) - np.pi / 2.0
# bestGraspAngle = 0
# print('displaying image')
cv2.imshow('image', I)
cv2.waitKey(0)
cv2.destroyAllWindows()
# TODO:
bestGraspAngle = 0
return bestH, bestW, bestGraspAngle
imsize = max(I.shape[:2])
gsize = int(gscale * imsize) # Size of grasp patch
max_batchsize = 128
gpu_id = args.gpu
# Set up model
if nsamples > max_batchsize:
batchsize = max_batchsize
nbatches = int(nsamples / max_batchsize) + 1
else:
batchsize = nsamples
nbatches = 1
print('Loading grasp model')
st_time = time.time()
G = grasp_obj(model_path, gpu_id)
G.BATCH_SIZE = batchsize
G.test_init()
P = Predictors(I, G)
print('Time taken: {}s'.format(time.time() - st_time))
fc8_predictions = []
patch_Hs = []
patch_Ws = []
print('Predicting on samples')
st_time = time.time()
for _ in range(nbatches):
P.graspNet_grasp(patch_size=gsize, num_samples=batchsize)
fc8_predictions.append(P.fc8_norm_vals)
patch_Hs.append(P.patch_hs)