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demo_FSANET.py
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demo_FSANET.py
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# The demo credit belongs to Yi-Ting Chen
import os
import cv2
import sys
sys.path.append('..')
import numpy as np
from math import cos, sin
# from moviepy.editor import *
from lib.FSANET_model import *
# from moviepy.editor import *
from keras import backend as K
from keras.layers import Average
def draw_axis(img, yaw, pitch, roll, tdx=None, tdy=None, size = 80):
pitch = pitch * np.pi / 180
yaw = -(yaw * np.pi / 180)
roll = roll * np.pi / 180
if tdx != None and tdy != None:
tdx = tdx
tdy = tdy
else:
height, width = img.shape[:2]
tdx = width / 2
tdy = height / 2
# X-Axis pointing to right. drawn in red
x1 = size * (cos(yaw) * cos(roll)) + tdx
y1 = size * (cos(pitch) * sin(roll) + cos(roll) * sin(pitch) * sin(yaw)) + tdy
# Y-Axis | drawn in green
# v
x2 = size * (-cos(yaw) * sin(roll)) + tdx
y2 = size * (cos(pitch) * cos(roll) - sin(pitch) * sin(yaw) * sin(roll)) + tdy
# Z-Axis (out of the screen) drawn in blue
x3 = size * (sin(yaw)) + tdx
y3 = size * (-cos(yaw) * sin(pitch)) + tdy
cv2.line(img, (int(tdx), int(tdy)), (int(x1),int(y1)),(0,0,255),3)
cv2.line(img, (int(tdx), int(tdy)), (int(x2),int(y2)),(0,255,0),3)
cv2.line(img, (int(tdx), int(tdy)), (int(x3),int(y3)),(255,0,0),2)
return img
def draw_results(detected,input_img,faces,ad,img_size,img_w,img_h,model,time_detection,time_network,time_plot):
if len(detected) > 0:
for i, (x,y,w,h) in enumerate(detected):
#x1, y1, x2, y2, w, h = d.left(), d.top(), d.right() + 1, d.bottom() + 1, d.width(), d.height()
x1 = x
y1 = y
x2 = x+w
y2 = y+h
xw1 = max(int(x1 - ad * w), 0)
yw1 = max(int(y1 - ad * h), 0)
xw2 = min(int(x2 + ad * w), img_w - 1)
yw2 = min(int(y2 + ad * h), img_h - 1)
faces[i,:,:,:] = cv2.resize(input_img[yw1:yw2 + 1, xw1:xw2 + 1, :], (img_size, img_size))
faces[i,:,:,:] = cv2.normalize(faces[i,:,:,:], None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX)
face = np.expand_dims(faces[i,:,:,:], axis=0)
p_result = model.predict(face)
face = face.squeeze()
img = draw_axis(input_img[yw1:yw2 + 1, xw1:xw2 + 1, :], p_result[0][0], p_result[0][1], p_result[0][2])
input_img[yw1:yw2 + 1, xw1:xw2 + 1, :] = img
cv2.imshow("result", input_img)
return input_img #,time_network,time_plot
def main():
try:
os.mkdir('./img')
except OSError:
pass
K.set_learning_phase(0) # make sure its testing mode
face_cascade = cv2.CascadeClassifier('lbpcascade_frontalface_improved.xml')
# load model and weights
img_size = 64
stage_num = [3,3,3]
lambda_local = 1
lambda_d = 1
img_idx = 0
detected = '' #make this not local variable
time_detection = 0
time_network = 0
time_plot = 0
skip_frame = 5 # every 5 frame do 1 detection and network forward propagation
ad = 0.6
#Parameters
num_capsule = 3
dim_capsule = 16
routings = 2
stage_num = [3,3,3]
lambda_d = 1
num_classes = 3
image_size = 64
num_primcaps = 7*3
m_dim = 5
S_set = [num_capsule, dim_capsule, routings, num_primcaps, m_dim]
model1 = FSA_net_Capsule(image_size, num_classes, stage_num, lambda_d, S_set)()
model2 = FSA_net_Var_Capsule(image_size, num_classes, stage_num, lambda_d, S_set)()
num_primcaps = 8*8*3
S_set = [num_capsule, dim_capsule, routings, num_primcaps, m_dim]
model3 = FSA_net_noS_Capsule(image_size, num_classes, stage_num, lambda_d, S_set)()
print('Loading models ...')
weight_file1 = '../pre-trained/300W_LP_models/fsanet_capsule_3_16_2_21_5/fsanet_capsule_3_16_2_21_5.h5'
model1.load_weights(weight_file1)
print('Finished loading model 1.')
weight_file2 = '../pre-trained/300W_LP_models/fsanet_var_capsule_3_16_2_21_5/fsanet_var_capsule_3_16_2_21_5.h5'
model2.load_weights(weight_file2)
print('Finished loading model 2.')
weight_file3 = '../pre-trained/300W_LP_models/fsanet_noS_capsule_3_16_2_192_5/fsanet_noS_capsule_3_16_2_192_5.h5'
model3.load_weights(weight_file3)
print('Finished loading model 3.')
inputs = Input(shape=(64,64,3))
x1 = model1(inputs) #1x1
x2 = model2(inputs) #var
x3 = model3(inputs) #w/o
avg_model = Average()([x1,x2,x3])
model = Model(inputs=inputs, outputs=avg_model)
# capture video
cap = cv2.VideoCapture(0)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1024*1)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 768*1)
print('Start detecting pose ...')
detected_pre = []
while True:
# get video frame
ret, input_img = cap.read()
img_idx = img_idx + 1
img_h, img_w, _ = np.shape(input_img)
if img_idx==1 or img_idx%skip_frame == 0:
time_detection = 0
time_network = 0
time_plot = 0
# detect faces using LBP detector
gray_img = cv2.cvtColor(input_img,cv2.COLOR_BGR2GRAY)
detected = face_cascade.detectMultiScale(gray_img, 1.1)
if len(detected_pre) > 0 and len(detected) == 0:
detected = detected_pre
faces = np.empty((len(detected), img_size, img_size, 3))
input_img = draw_results(detected,input_img,faces,ad,img_size,img_w,img_h,model,time_detection,time_network,time_plot)
cv2.imwrite('img/'+str(img_idx)+'.png',input_img)
else:
input_img = draw_results(detected,input_img,faces,ad,img_size,img_w,img_h,model,time_detection,time_network,time_plot)
if len(detected) > len(detected_pre) or img_idx%(skip_frame*3) == 0:
detected_pre = detected
key = cv2.waitKey(1)
if __name__ == '__main__':
main()