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from face_detection import RetinaFace
from model import SixDRepNet
import math
import re
from matplotlib import pyplot as plt
import sys
import os
import argparse
import numpy as np
import cv2
import matplotlib.pyplot as plt
from numpy.lib.function_base import _quantile_unchecked
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torchvision import transforms
import torch.backends.cudnn as cudnn
import torchvision
import torch.nn.functional as F
import utils
import matplotlib
from PIL import Image
import time
matplotlib.use('TkAgg')
def parse_args():
"""Parse input arguments."""
parser = argparse.ArgumentParser(
description='Head pose estimation using the 6DRepNet.')
parser.add_argument('--gpu',
dest='gpu_id', help='GPU device id to use [0]',
default=0, type=int)
parser.add_argument('--cam',
dest='cam_id', help='Camera device id to use [0]',
default=0, type=int)
parser.add_argument('--snapshot',
dest='snapshot', help='Name of model snapshot.',
default='', type=str)
parser.add_argument('--save_viz',
dest='save_viz', help='Save images with pose cube.',
default=False, type=bool)
args = parser.parse_args()
return args
transformations = transforms.Compose([transforms.Resize(224),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
if __name__ == '__main__':
args = parse_args()
cudnn.enabled = True
gpu = args.gpu_id
cam = args.cam_id
snapshot_path = args.snapshot
model = SixDRepNet(backbone_name='RepVGG-A0',
backbone_file='',
deploy=True,
pretrained=False)
print('Loading data.')
detector = RetinaFace(gpu_id=gpu)
# Load snapshot
saved_state_dict = torch.load(os.path.join(
snapshot_path), map_location='cpu')
if 'model_state_dict' in saved_state_dict:
model.load_state_dict(saved_state_dict['model_state_dict'])
else:
model.load_state_dict(saved_state_dict)
if gpu != -1:
model.cuda(gpu)
# Test the Model
model.eval() # Change model to 'eval' mode (BN uses moving mean/var).
cap = cv2.VideoCapture(cam)
# Check if the webcam is opened correctly
if not cap.isOpened():
raise IOError("Cannot open webcam")
with torch.no_grad():
while True:
ret, frame = cap.read()
faces = detector(frame)
for box, landmarks, score in faces:
# Print the location of each face in this image
if score < .95:
continue
x_min = int(box[0])
y_min = int(box[1])
x_max = int(box[2])
y_max = int(box[3])
bbox_width = abs(x_max - x_min)
bbox_height = abs(y_max - y_min)
x_min = max(0, x_min-int(0.2*bbox_height))
y_min = max(0, y_min-int(0.2*bbox_width))
x_max = x_max+int(0.2*bbox_height)
y_max = y_max+int(0.2*bbox_width)
img = frame[y_min:y_max, x_min:x_max]
img = Image.fromarray(img)
img = img.convert('RGB')
img = transformations(img)
img = torch.Tensor(img[None, :])
if gpu != -1:
img = img.cuda(gpu)
c = cv2.waitKey(1)
if c == 27:
break
start = time.time()
R_pred = model(img)
end = time.time()
print('Head pose estimation: %2f ms' % ((end - start)*1000.))
euler = utils.compute_euler_angles_from_rotation_matrices(
R_pred,use_gpu=False)*180/np.pi
p_pred_deg = euler[:, 0].cpu()
y_pred_deg = euler[:, 1].cpu()
r_pred_deg = euler[:, 2].cpu()
#utils.draw_axis(frame, y_pred_deg, p_pred_deg, r_pred_deg, left+int(.5*(right-left)), top, size=100)
utils.plot_pose_cube(frame, y_pred_deg, p_pred_deg, r_pred_deg, x_min + int(.5*(
x_max-x_min)), y_min + int(.5*(y_max-y_min)), size=bbox_width)
cv2.imshow("Demo", np.array(frame, dtype = np.uint8))
cv2.waitKey(5)
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