mirror of
https://github.com/deepinsight/insightface.git
synced 2025-12-30 16:12:27 +00:00
101 lines
3.6 KiB
Python
101 lines
3.6 KiB
Python
import argparse
|
|
import cv2
|
|
import sys
|
|
import numpy as np
|
|
import os
|
|
import mxnet as mx
|
|
import datetime
|
|
import img_helper
|
|
sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'deploy'))
|
|
from mtcnn_detector import MtcnnDetector
|
|
|
|
|
|
class Handler:
|
|
def __init__(self, prefix, epoch, ctx_id=0):
|
|
print('loading', prefix, epoch)
|
|
if ctx_id >= 0:
|
|
ctx = mx.gpu(ctx_id)
|
|
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']
|
|
image_size = (128, 128)
|
|
self.image_size = image_size
|
|
model = mx.mod.Module(symbol=sym, context=ctx, label_names=None)
|
|
#model = mx.mod.Module(symbol=sym, context=ctx)
|
|
model.bind(for_training=False,
|
|
data_shapes=[('data', (1, 3, image_size[0], image_size[1]))
|
|
])
|
|
model.set_params(arg_params, aux_params)
|
|
self.model = model
|
|
mtcnn_path = os.path.join(os.path.dirname(__file__), '..', 'deploy',
|
|
'mtcnn-model')
|
|
self.det_threshold = [0.6, 0.7, 0.8]
|
|
self.detector = MtcnnDetector(model_folder=mtcnn_path,
|
|
ctx=ctx,
|
|
num_worker=1,
|
|
accurate_landmark=True,
|
|
threshold=self.det_threshold)
|
|
|
|
def get(self, img):
|
|
ret = self.detector.detect_face(img, det_type=0)
|
|
if ret is None:
|
|
return None
|
|
bbox, points = ret
|
|
if bbox.shape[0] == 0:
|
|
return None
|
|
bbox = bbox[0, 0:4]
|
|
points = points[0, :].reshape((2, 5)).T
|
|
M = img_helper.estimate_trans_bbox(bbox, self.image_size[0], s=2.0)
|
|
rimg = cv2.warpAffine(img, M, self.image_size, borderValue=0.0)
|
|
img = cv2.cvtColor(rimg, cv2.COLOR_BGR2RGB)
|
|
img = np.transpose(img, (2, 0, 1)) #3*112*112, RGB
|
|
input_blob = np.zeros((1, 3, self.image_size[1], self.image_size[0]),
|
|
dtype=np.uint8)
|
|
input_blob[0] = img
|
|
ta = datetime.datetime.now()
|
|
data = mx.nd.array(input_blob)
|
|
db = mx.io.DataBatch(data=(data, ))
|
|
self.model.forward(db, is_train=False)
|
|
alabel = self.model.get_outputs()[-1].asnumpy()[0]
|
|
tb = datetime.datetime.now()
|
|
print('module time cost', (tb - ta).total_seconds())
|
|
ret = np.zeros((alabel.shape[0], 2), dtype=np.float32)
|
|
for i in range(alabel.shape[0]):
|
|
a = cv2.resize(alabel[i], (self.image_size[1], self.image_size[0]))
|
|
ind = np.unravel_index(np.argmax(a, axis=None), a.shape)
|
|
#ret[i] = (ind[0], ind[1]) #h, w
|
|
ret[i] = (ind[1], ind[0]) #w, h
|
|
return ret, M
|
|
|
|
|
|
ctx_id = 4
|
|
img_path = '../deploy/Tom_Hanks_54745.png'
|
|
img = cv2.imread(img_path)
|
|
#img = np.zeros( (128,128,3), dtype=np.uint8 )
|
|
|
|
handler = Handler('./model/HG', 1, ctx_id)
|
|
for _ in range(10):
|
|
ta = datetime.datetime.now()
|
|
landmark, M = handler.get(img)
|
|
tb = datetime.datetime.now()
|
|
print('get time cost', (tb - ta).total_seconds())
|
|
#visualize landmark
|
|
IM = cv2.invertAffineTransform(M)
|
|
for i in range(landmark.shape[0]):
|
|
p = landmark[i]
|
|
point = np.ones((3, ), dtype=np.float32)
|
|
point[0:2] = p
|
|
point = np.dot(IM, point)
|
|
landmark[i] = point[0:2]
|
|
|
|
for i in range(landmark.shape[0]):
|
|
p = landmark[i]
|
|
point = (int(p[0]), int(p[1]))
|
|
cv2.circle(img, point, 1, (0, 255, 0), 2)
|
|
|
|
filename = './landmark_test.png'
|
|
print('writing', filename)
|
|
cv2.imwrite(filename, img)
|