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agegender_demo-ipcamera.py
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agegender_demo-ipcamera.py
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# ----------------------------------------------
# Yolo Keras Face Detection from WebCamera
# ----------------------------------------------
from datetime import datetime
import numpy as np
import sys, getopt
import cv2
import os
import random
from keras import backend as K
os.environ['KERAS_BACKEND'] = 'tensorflow'
from keras.models import load_model
from keras.preprocessing import image
import tensorflow as tf
import keras.backend.tensorflow_backend as ktf
def get_session(gpu_fraction=0.333):
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_fraction,
allow_growth=True)
return tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
#YOLOV1
#reference from https://github.com/xingwangsfu/caffe-yolo
def interpret_output_yolov1(output, img_width, img_height):
classes = ["face"]
w_img = img_width
h_img = img_height
threshold = 0.2
iou_threshold = 0.5
num_class = 1
num_box = 2
grid_size = 11
probs = np.zeros((grid_size,grid_size,2,20))
class_probs = np.reshape(output[0:grid_size*grid_size*num_class],(grid_size,grid_size,num_class))
scales = np.reshape(output[grid_size*grid_size*num_class:grid_size*grid_size*num_class+grid_size*grid_size*2],(grid_size,grid_size,2))
boxes = np.reshape(output[grid_size*grid_size*num_class+grid_size*grid_size*2:],(grid_size,grid_size,2,4))
offset = np.transpose(np.reshape(np.array([np.arange(grid_size)]*grid_size*2),(2,grid_size,grid_size)),(1,2,0))
boxes[:,:,:,0] += offset
boxes[:,:,:,1] += np.transpose(offset,(1,0,2))
boxes[:,:,:,0:2] = boxes[:,:,:,0:2] / grid_size
boxes[:,:,:,2] = np.multiply(boxes[:,:,:,2],boxes[:,:,:,2])
boxes[:,:,:,3] = np.multiply(boxes[:,:,:,3],boxes[:,:,:,3])
boxes[:,:,:,0] *= w_img
boxes[:,:,:,1] *= h_img
boxes[:,:,:,2] *= w_img
boxes[:,:,:,3] *= h_img
for i in range(2):
for j in range(num_class):
probs[:,:,i,j] = np.multiply(class_probs[:,:,j],scales[:,:,i])
filter_mat_probs = np.array(probs>=threshold,dtype='bool')
filter_mat_boxes = np.nonzero(filter_mat_probs)
boxes_filtered = boxes[filter_mat_boxes[0],filter_mat_boxes[1],filter_mat_boxes[2]]
probs_filtered = probs[filter_mat_probs]
classes_num_filtered = np.argmax(probs,axis=3)[filter_mat_boxes[0],filter_mat_boxes[1],filter_mat_boxes[2]]
argsort = np.array(np.argsort(probs_filtered))[::-1]
boxes_filtered = boxes_filtered[argsort]
probs_filtered = probs_filtered[argsort]
classes_num_filtered = classes_num_filtered[argsort]
for i in range(len(boxes_filtered)):
if probs_filtered[i] == 0 : continue
for j in range(i+1,len(boxes_filtered)):
if iou(boxes_filtered[i],boxes_filtered[j]) > iou_threshold :
probs_filtered[j] = 0.0
filter_iou = np.array(probs_filtered>0.0,dtype='bool')
boxes_filtered = boxes_filtered[filter_iou]
probs_filtered = probs_filtered[filter_iou]
classes_num_filtered = classes_num_filtered[filter_iou]
result = []
for i in range(len(boxes_filtered)):
result.append([classes[classes_num_filtered[i]],boxes_filtered[i][0],boxes_filtered[i][1],boxes_filtered[i][2],boxes_filtered[i][3],probs_filtered[i]])
return result
def iou(box1,box2):
tb = min(box1[0]+0.5*box1[2],box2[0]+0.5*box2[2])-max(box1[0]-0.5*box1[2],box2[0]-0.5*box2[2])
lr = min(box1[1]+0.5*box1[3],box2[1]+0.5*box2[3])-max(box1[1]-0.5*box1[3],box2[1]-0.5*box2[3])
if tb < 0 or lr < 0 : intersection = 0
else : intersection = tb*lr
return intersection / (box1[2]*box1[3] + box2[2]*box2[3] - intersection)
#YOLOV2
#reference from https://github.com/experiencor/keras-yolo2
# https://github.com/experiencor/keras-yolo2/blob/master/LICENSE
def interpret_output_yolov2(output, img_width, img_height):
anchors=[0.57273, 0.677385, 1.87446, 2.06253, 3.33843, 5.47434, 7.88282, 3.52778, 9.77052, 9.16828]
netout=output
nb_class=1
obj_threshold=0.4
nms_threshold=0.3
grid_h, grid_w, nb_box = netout.shape[:3]
size = 4 + nb_class + 1;
nb_box=5
netout=netout.reshape(grid_h,grid_w,nb_box,size)
boxes = []
# decode the output by the network
netout[..., 4] = _sigmoid(netout[..., 4])
netout[..., 5:] = netout[..., 4][..., np.newaxis] * _softmax(netout[..., 5:])
netout[..., 5:] *= netout[..., 5:] > obj_threshold
for row in range(grid_h):
for col in range(grid_w):
for b in range(nb_box):
# from 4th element onwards are confidence and class classes
classes = netout[row,col,b,5:]
if np.sum(classes) > 0:
# first 4 elements are x, y, w, and h
x, y, w, h = netout[row,col,b,:4]
x = (col + _sigmoid(x)) / grid_w # center position, unit: image width
y = (row + _sigmoid(y)) / grid_h # center position, unit: image height
w = anchors[2 * b + 0] * np.exp(w) / grid_w # unit: image width
h = anchors[2 * b + 1] * np.exp(h) / grid_h # unit: image height
confidence = netout[row,col,b,4]
box = bounding_box(x-w/2, y-h/2, x+w/2, y+h/2, confidence, classes)
boxes.append(box)
# suppress non-maximal boxes
for c in range(nb_class):
sorted_indices = list(reversed(np.argsort([box.classes[c] for box in boxes])))
for i in range(len(sorted_indices)):
index_i = sorted_indices[i]
if boxes[index_i].classes[c] == 0:
continue
else:
for j in range(i+1, len(sorted_indices)):
index_j = sorted_indices[j]
if bbox_iou(boxes[index_i], boxes[index_j]) >= nms_threshold:
boxes[index_j].classes[c] = 0
# remove the boxes which are less likely than a obj_threshold
boxes = [box for box in boxes if box.get_score() > obj_threshold]
result = []
for i in range(len(boxes)):
if(boxes[i].classes[0]==0):
continue
predicted_class = "face"
score = boxes[i].score
result.append([predicted_class,(boxes[i].xmax+boxes[i].xmin)*img_width/2,(boxes[i].ymax+boxes[i].ymin)*img_height/2,(boxes[i].xmax-boxes[i].xmin)*img_width,(boxes[i].ymax-boxes[i].ymin)*img_height,score])
return result
class bounding_box:
def __init__(self, xmin, ymin, xmax, ymax, c = None, classes = None):
self.xmin = xmin
self.ymin = ymin
self.xmax = xmax
self.ymax = ymax
self.c = c
self.classes = classes
self.label = -1
self.score = -1
def get_label(self):
if self.label == -1:
self.label = np.argmax(self.classes)
return self.label
def get_score(self):
if self.score == -1:
self.score = self.classes[self.get_label()]
return self.score
def bbox_iou(box1, box2):
intersect_w = _interval_overlap([box1.xmin, box1.xmax], [box2.xmin, box2.xmax])
intersect_h = _interval_overlap([box1.ymin, box1.ymax], [box2.ymin, box2.ymax])
intersect = intersect_w * intersect_h
w1, h1 = box1.xmax-box1.xmin, box1.ymax-box1.ymin
w2, h2 = box2.xmax-box2.xmin, box2.ymax-box2.ymin
union = w1*h1 + w2*h2 - intersect
return float(intersect) / union
def _interval_overlap(interval_a, interval_b):
x1, x2 = interval_a
x3, x4 = interval_b
if x3 < x1:
if x4 < x1:
return 0
else:
return min(x2,x4) - x1
else:
if x2 < x3:
return 0
else:
return min(x2,x4) - x3
def _sigmoid(x):
return 1. / (1. + np.exp(-x))
def _softmax(x, axis=-1, t=-100.):
x = x - np.max(x)
if np.min(x) < t:
x = x/np.min(x)*t
e_x = np.exp(x)
return e_x / e_x.sum(axis, keepdims=True)
#crop
def crop(x,y,w,h,margin,img_width,img_height):
xmin = int(x-w*margin)
xmax = int(x+w*margin)
ymin = int(y-h*margin)
ymax = int(y+h*margin)
if xmin<0:
xmin = 0
if ymin<0:
ymin = 0
if xmax>img_width:
xmax = img_width
if ymax>img_height:
ymax = img_height
return xmin,xmax,ymin,ymax
#display result
def show_results(img,results, img_width, img_height, model_age, model_gender, model_emotion, time_start, lines_age, lines_gender, lines_fer2013):
img_cp = img.copy()
target_image=img_cp
global last_update, last_age, last_emotion, last_gender, last_emotion_prob, last_rank
last_update = last_update + 1
for i in range(len(results)):
#display detected face
x = int(results[i][1])
y = int(results[i][2])
w = int(results[i][3])//2
h = int(results[i][4])//2
if(w<h):
w=h
else:
h=w
xmin,xmax,ymin,ymax=crop(x,y,w,h,1.0,img_width,img_height)
#cv2.rectangle(img_cp,(xmin,ymin),(xmax,ymax),(0,255,0),2)
#cv2.rectangle(img_cp,(xmin,ymin-20),(xmax,ymin),(125,125,125),-1)
#cv2.putText(img_cp,results[i][0] + ' : %.2f' % results[i][5],(xmin+5,ymin-7),cv2.FONT_HERSHEY_SIMPLEX,0.5,(0,0,0),1)
#analyze detected face
xmin2,xmax2,ymin2,ymax2=crop(x,y,w,h,1.1,img_width,img_height)
face_image = img[ymin2:ymax2, xmin2:xmax2]
if(face_image.shape[0]<=0 or face_image.shape[1]<=0):
continue
cv2.rectangle(target_image, (xmin2,ymin2), (xmax2,ymax2), color=(0,0,255), thickness=3)
offset=20
if(model_age!=None):
if last_update > 10:
shape = model_age.layers[0].get_output_at(0).get_shape().as_list()
img_keras = cv2.resize(face_image, (shape[1],shape[2]))
#img_keras = img_keras[::-1, :, ::-1].copy() #BGR to RGB
img_keras = np.expand_dims(img_keras, axis=0)
img_keras = img_keras / 255.0
pred_age_keras = model_age.predict(img_keras)[0]
prob_age_keras = np.max(pred_age_keras)
cls_age_keras = pred_age_keras.argmax()
age=0
for i in range(101):
age=age+pred_age_keras[i]*i
age = int(age)
last_age = age
facerank = int((100-age)/10)+85+random.randint(1,5)
last_rank = facerank
label=str(last_age)
#label="%.2f" % prob_age_keras + " " + lines_age[cls_age_keras]
cv2.putText(target_image, "Age : "+label, (xmin2,ymax2+offset), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0,0,250));
offset=offset+20
#Generate random face rank
cv2.putText(target_image, "Face Rank : " + str(last_rank), (xmin2,ymax2+offset), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0,0,250));
offset=offset+20
if(model_gender!=None):
if last_update > 10:
shape = model_gender.layers[0].get_output_at(0).get_shape().as_list()
img_gender = cv2.resize(face_image, (shape[1],shape[2]))
#img_gender = img_gender[::-1, :, ::-1].copy() #BGR to RGB
img_gender = np.expand_dims(img_gender, axis=0)
img_gender = img_gender / 255.0
pred_gender_keras = model_gender.predict(img_gender)[0]
prob_gender_keras = np.max(pred_gender_keras)
cls_gender_keras = pred_gender_keras.argmax()
last_gender = cls_gender_keras
#cv2.putText(target_image, "Gender : %.2f" % prob_gender_keras + " " + lines_gender[cls_gender_keras], (xmin2,ymax2+offset), cv2.FONT_HERSHEY_COMPLEX_SMALL, 0.8, (0,0,250));
cv2.putText(target_image, "Gender : " + lines_gender[last_gender], (xmin2,ymax2+offset), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0,0,250));
offset=offset+20
if(model_emotion!=None):
if last_update > 10:
shape = model_emotion.layers[0].get_output_at(0).get_shape().as_list()
img_fer2013 = cv2.resize(face_image, (shape[1],shape[2]))
img_fer2013 = cv2.cvtColor(img_fer2013,cv2.COLOR_BGR2GRAY)
img_fer2013 = np.expand_dims(img_fer2013, axis=0)
img_fer2013 = np.expand_dims(img_fer2013, axis=3)
img_fer2013 = img_fer2013 / 255.0 *2 -1
pred_emotion_keras = model_emotion.predict(img_fer2013)[0]
prob_emotion_keras = np.max(pred_emotion_keras)
cls_emotion_keras = pred_emotion_keras.argmax()
last_emotion = cls_emotion_keras
last_emotion_prob = prob_emotion_keras
last_update = 0
#cv2.putText(target_image, "Emotion : %.2f" % prob_emotion_keras + " " + lines_fer2013[cls_emotion_keras], (xmin2,ymax2+offset), cv2.FONT_HERSHEY_COMPLEX_SMALL, 0.8, (0,0,250));
cv2.putText(target_image, "Emotion : " + str(int(last_emotion_prob*100)) + "% " + lines_fer2013[last_emotion], (xmin2,ymax2+offset), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0,0,250));
offset=offset+20
time_used = (datetime.now() - time_start).microseconds/1000
fps = 1000/time_used
cv2.putText(target_image, "FPS : %.1f" % fps , (20,40), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1.5, (0,250,0));
cv2.namedWindow('Tongfang mkl-dnn inference Demo', cv2.WINDOW_NORMAL)
cv2.imshow('Tongfang mkl-dnn inference Demo',img_cp)
def main(argv):
MODEL_ROOT_PATH="./pretrain/"
ktf.set_session(get_session())
#Load Model
model_face = load_model(MODEL_ROOT_PATH+'yolov2_tiny-face.h5')
#model_age = load_model(MODEL_ROOT_PATH+'agegender_age_squeezenet.hdf5')
model_age = load_model(MODEL_ROOT_PATH+'agegender_age101_squeezenet.hdf5')
model_gender = load_model(MODEL_ROOT_PATH+'agegender_gender_squeezenet.hdf5')
if(os.path.exists(MODEL_ROOT_PATH+'fer2013_mini_XCEPTION.102-0.66.hdf5')):
#model_emotion = load_model(MODEL_ROOT_PATH+'fer2013_mini_XCEPTION.102-0.66.hdf5')
model_emotion = load_model(MODEL_ROOT_PATH+'fer2013_mini_XCEPTION.107-0.66.hdf5')
else:
model_emotion = None
#Prepare WebCamera
#cap = cv2.VideoCapture(0)
cap = cv2.VideoCapture("rtsp://admin:[email protected]:554//Streaming/Channels/1")
#cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1920)
#cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 1080)
#cap.set(cv2.CAP_PROP_FPS,30)
lines_age=open('words/agegender_age_words.txt').readlines()
lines_gender=open('words/agegender_gender_words.txt').readlines()
lines_fer2013=open('words/emotion_words.txt').readlines()
global last_update, last_age, last_emotion, last_emotion_prob, last_gender, last_rank
last_update = 20 #we need to update all variables for the first time
last_gender = 1
last_age = 18
last_emotion = 6
last_emotion_prob = 0.8
last_rank = 95
#Detection
while True:
time_start = datetime.now()
#Face Detection
ret, frame = cap.read() #BGR
#Flip the image
frame = cv2.flip(frame,1)
img=frame
img = img[...,::-1] #BGR 2 RGB
inputs = img.copy() / 255.0
img_cv = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
img_camera = cv2.resize(inputs, (416,416))
img_camera = np.expand_dims(img_camera, axis=0)
out2 = model_face.predict(img_camera)[0]
results = interpret_output_yolov2(out2, img.shape[1], img.shape[0])
#Age and Gender Detection
show_results(img_cv,results, img.shape[1], img.shape[0], model_age, model_gender, model_emotion, time_start, lines_age, lines_gender, lines_fer2013)
k = cv2.waitKey(1)
#k = cv2.waitKey(1)
if k == 27 or k == 'q' :
break
cap.release()
cv2.destroyAllWindows()
if __name__=='__main__':
main(sys.argv[1:])