'result': 0
}, {
'res': 1080,
'file': './1_1920x1080.jpg',
'minsize': 80,
'cpus': 2,
'result': 0
}, {
'res': 1080,
'file': './1_1920x1080.jpg',
'minsize': 200,
'cpus': 2,
'result': 0
}]
m.init('./model/')
print('warming up')
m.set_minsize(40)
m.set_num_threads(1)
m.set_threshold(0.6, 0.7, 0.8)
result = m.detect('./1_854x480.jpg')
print(result)
m.detect('./1_854x480.jpg')
m.detect('./1_854x480.jpg')
m.detect('./1_854x480.jpg')
m.detect('./1_854x480.jpg')
print('starting up')
rounds = 20
for item in benchmark:
from __future__ import print_function
import face_detection as m
import json, cv2, os
import numpy as np
from scipy import misc
import face_preprocess
import time
minsize = int(os.getenv("MINIMAL_FACE_RESOLUTION", default="100")) # minimum size of face, 100 for 1920x1080 resolution, 70 for 1280x720.
threads_number = int(os.getenv("THREADS_NUM_FACE_DETECTOR", default="1"))
image_size = 160
margin = 16
BLURY_THREHOLD = 5
m.init('./model')
m.set_minsize(minsize)
m.set_threshold(0.6,0.7,0.8)
m.set_num_threads(threads_number)
def get_filePath_fileName_fileExt(filename):
(filepath,tempfilename) = os.path.split(filename)
(shotname,extension) = os.path.splitext(tempfilename)
return filepath, shotname, extension
def prewhiten(x):
mean = np.mean(x)
std = np.std(x)
std_adj = np.maximum(std, 1.0/np.sqrt(x.size))
y = np.multiply(np.subtract(x, mean), 1/std_adj)
return y
import face_detection as m
import time
m.init('./models/ncnn/')
print('warming up')
m.set_minsize(40)
m.set_threshold(0.6, 0.7, 0.8)
m.set_num_threads(1)
m.detect('./images_480p/1_854x480.jpg')
m.detect('./images_480p/1_854x480.jpg')
m.detect('./images_480p/1_854x480.jpg')
m.detect('./images_480p/1_854x480.jpg')
m.detect('./images_480p/1_854x480.jpg')
start = time.time()
for i in range(100):
step_start = time.time()
result = m.detect('./images_480p/1_854x480.jpg')
step_end = time.time()
print('step {} duration is {}'.format(i, step_end - step_start))
end = time.time()
print(result)
print('average duration is {}'.format((end - start) / 100))