david-littlefield

cmake -D CMAKE_BUILD_TYPE=RELEASE \
    -D CMAKE_INSTALL_PREFIX=/usr/local \
    -D OPENCV_EXTRA_MODULES_PATH=~/opencv_contrib/modules \
    -D PYTHON3_LIBRARY=`python -c 'import subprocess ; import sys ; s = subprocess.check_output("python-config --configdir", shell=True).decode("utf-8").strip() ; (M, m) = sys.version_info[:2] ; print("{}/libpython{}.{}.dylib".format(s, M, m))'` \
    -D PYTHON3_INCLUDE_DIR=`python -c 'import distutils.sysconfig as s; print(s.get_python_inc())'` \
    -D PYTHON3_EXECUTABLE=$VIRTUAL_ENV/bin/python \
    -D BUILD_opencv_python2=OFF \
    -D BUILD_opencv_python3=ON \
    -D INSTALL_PYTHON_EXAMPLES=ON \
    -D INSTALL_C_EXAMPLES=OFF \

david-littlefield

parser = argparse.ArgumentParser(description='DSFD:Dual Shot Face Detector')

parser.add_argument('--trained_model', default='weights/WIDERFace_DSFD_RES152.pth',
                    type=str, help='Trained state_dict file path to open')
parser.add_argument('--save_folder', default='eval_tools/', type=str,
                    help='Dir to save results')
parser.add_argument('--visual_threshold', default=0.1, type=float,
                    help='Final confidence threshold')
parser.add_argument('--cuda', default=False, type=bool,
                    help='Use cuda to train model')

david-littlefield

{
	"NotebookApp": {
        "open_browser": false,
        "port": 8888,
        "ip": "0.0.0.0",
	"allow_origin": "*",
        "notebook_dir": "/mnt/c/users/admin/",
        "certfile": "/home/user/.jupyter/mycert.pem",
        "keyfile": "/home/user/.jupyter/mykey.key",
        "allow_remote_access": true,

david-littlefield

# Line 29

from widerface_val import bbox_vote 
from warnings import filterwarnings # <----------

filterwarnings("ignore") # <----------

plt.switch_backend('agg')

david-littlefield

# Line 231

# load net
cfg = widerface_640
num_classes = len(WIDERFace_CLASSES) + 1 # +1 background
net = build_ssd('test', cfg['min_dim'], num_classes) # initialize SSD
net.load_state_dict(torch.load(args.trained_model))
#net.cuda() # <----------

david-littlefield

# Line 75

def infer(net , img , transform , thresh , cuda , shrink):
    if shrink != 1:
        img = cv2.resize(img, None, None, fx=shrink, fy=shrink, interpolation=cv2.INTER_LINEAR)
    x = torch.from_numpy(transform(img)[0]).permute(2, 0, 1)
    with torch.no_grad(): 
        x = Variable(x.unsqueeze(0)) 
    #if cuda: # <----------
    #    x = x.cuda() # <----------

david-littlefield

# Line 207

def load(f, map_location="cpu", pickle_module=pickle): # <----------
    

david-littlefield

# Line 83

while detections[0, i, j, 0] >= thresh:            
    score = detections[0, i, j, 0] 
    score = score.cpu().numpy() # <----------
    #label_name = labelmap[i-1]            
    pt = (detections[0, i, j, 1:]*scale).cpu().numpy()               
    coords = (pt[0], pt[1], pt[2], pt[3])             
    det.append([pt[0], pt[1], pt[2], pt[3], score])            
    j += 1

david-littlefield

# Line 13

from data import widerface_640
from ..box_utils import log_sum_exp, match, refine_match, sfd_match # <----------

david-littlefield

# Line 25

WIDERFace_CLASSES = ['face']  # always index 0
# note: if you used our download scripts, this should be right
WIDERFace_ROOT = './data/ # <----------