mirror of
https://github.com/deepinsight/insightface.git
synced 2026-05-14 20:35:30 +00:00
57 lines
1.9 KiB
Python
57 lines
1.9 KiB
Python
import argparse
|
|
import cv2
|
|
import numpy as np
|
|
import sys
|
|
import mxnet as mx
|
|
|
|
parser = argparse.ArgumentParser(description='face model test')
|
|
# general
|
|
parser.add_argument('--image-size', default='128,128', help='')
|
|
parser.add_argument('--model', default='./models/test,15', help='path to load model.')
|
|
parser.add_argument('--gpu', default=-1, type=int, help='gpu id')
|
|
args = parser.parse_args()
|
|
|
|
_vec = args.image_size.split(',')
|
|
assert len(_vec)==2
|
|
image_size = (int(_vec[0]), int(_vec[1]))
|
|
_vec = args.model.split(',')
|
|
assert len(_vec)==2
|
|
prefix = _vec[0]
|
|
epoch = int(_vec[1])
|
|
print('loading',prefix, epoch)
|
|
if args.gpu>=0:
|
|
ctx = mx.gpu(args.gpu)
|
|
else:
|
|
ctx = mx.cpu()
|
|
sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch)
|
|
all_layers = sym.get_internals()
|
|
sym = all_layers['heatmap_output']
|
|
model = mx.mod.Module(symbol=sym, context=ctx, label_names = None)
|
|
#model.bind(data_shapes=[('data', (args.batch_size, 3, image_size[0], image_size[1]))], label_shapes=[('softmax_label', (args.batch_size,))])
|
|
model.bind(data_shapes=[('data', (1, 3, image_size[0], image_size[1]))])
|
|
model.set_params(arg_params, aux_params)
|
|
#img_path = '/raid5data/dplearn/megaface/facescrubr/112x112/Tom_Hanks/Tom_Hanks_54745.png'
|
|
img_path = './test.png'
|
|
|
|
img = cv2.imread(img_path)
|
|
|
|
rimg = cv2.resize(img, (image_size[1], image_size[0]))
|
|
img = cv2.cvtColor(rimg, cv2.COLOR_BGR2RGB)
|
|
img = np.transpose(img, (2,0,1)) #3*112*112, RGB
|
|
input_blob = np.expand_dims(img, axis=0) #1*3*112*112
|
|
data = mx.nd.array(input_blob)
|
|
db = mx.io.DataBatch(data=(data,))
|
|
model.forward(db, is_train=False)
|
|
output = model.get_outputs()[0].asnumpy()
|
|
#print(output[0,80])
|
|
#sys.exit(0)
|
|
filename = "./vis/draw_%s" % img_path.split('/')[-1]
|
|
for i in xrange(output.shape[1]):
|
|
a = output[0,i,:,:]
|
|
a = cv2.resize(a, (image_size[1], image_size[0]))
|
|
ind = np.unravel_index(np.argmax(a, axis=None), a.shape)
|
|
cv2.circle(rimg, (ind[1], ind[0]), 1, (0, 0, 255), 2)
|
|
print(i, ind)
|
|
cv2.imwrite(filename, rimg)
|
|
|