video2.py
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import dlib
import cv2
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
import math
import os
import pathlib
import time
import pandas as pd
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator, load_img
from tensorflow.keras.models import load_model
from tensorflow.keras import regularizers
from tensorflow import keras
import time
# predictor = dlib.shape_predictor(
# 'face_emotion_recognition/models/shape_predictor_68_face_landmarks.dat')
# facerec = dlib.face_recognition_model_v1(
# 'face_emotion_recognition/models/dlib_face_recognition_resnet_model_v1.dat')
# model = load_model(
# 'checkpoint/er-best-mobilenet1-bt32-model-classweight-adam.h5')
def get_key(val):
for key, value in labels_dict_.items():
if(value == val):
return key
def convertMillis(millis):
seconds = (millis/1000) % 60
minutes = (millis/(1000*60)) % 60
hours = (millis/(1000*60*60)) % 24
return seconds, int(minutes), int(hours)
def videoDetector(input_fps, video_name):
# face & emotion model load
predictor = dlib.shape_predictor(
'face_emotion_recognition/models/shape_predictor_68_face_landmarks.dat')
facerec = dlib.face_recognition_model_v1(
'face_emotion_recognition/models/dlib_face_recognition_resnet_model_v1.dat')
model = load_model(
'checkpoint/er-best-mobilenet1-bt32-model-classweight-adam.h5')
start = time.time()
detector = dlib.get_frontal_face_detector()
# face & emotion detection time dict
descs = np.load('../static/img/descs.npy', allow_pickle=True)[()]
labels_dict_ = {0: 'angry', 1: 'fear', 2: 'happy',
3: 'neutral', 4: 'sad', 5: 'surprise'}
face_emotion_dict = {}
for name, saved_desc in descs.items():
face_emotion_dict[name] = {'angry': [], 'fear': [
], 'happy': [], 'neutral': [], 'sad': [], 'surprise': []}
# video 정보 불러오기
video_path = '../static/video/' + video_name + '.mp4'
cap = cv2.VideoCapture(video_path)
_, img_bgr = cap.read() # (800, 1920, 3)
resized_width = 1920
video_size = (resized_width, int(
img_bgr.shape[0] * resized_width // img_bgr.shape[1]))
prev_time = 0
while True:
retval, frameBGR = cap.read() # 영상을 한 frame씩 읽어오기
current_time = time.time() - prev_time
if(type(frameBGR) == type(None)):
pass
else:
frameBGR = cv2.resize(frameBGR, video_size)
frame = cv2.cvtColor(frameBGR, cv2.COLOR_BGR2RGB)
if (retval is True) and (current_time > input_fps):
prev_time = time.time()
faces = detector(frame, 1)
for (i, face) in enumerate(faces):
shape = predictor(frame, face)
face_descriptor = facerec.compute_face_descriptor(
frame, shape)
img = cv2.resize(frame[face.top():face.bottom(), face.left(
):face.right()], dsize=(224, 224), interpolation=cv2.INTER_CUBIC)
imgarr = np.array(img).reshape(1, 224, 224, 3) / 255
emotion = labels_dict_[
model.predict(imgarr).argmax(axis=-1)[0]]
last_found = {'name': 'unknown',
'dist': 0.6, 'color': (0, 0, 255)}
for name, saved_desc in descs.items():
dist = np.linalg.norm(
[face_descriptor] - saved_desc, axis=1)
if dist < last_found['dist']:
last_found = {
'name': name, 'dist': dist, 'color': (255, 255, 255)}
cv2.rectangle(frameBGR, pt1=(face.left(), face.top()), pt2=(
face.right(), face.bottom()), color=last_found['color'], thickness=2)
cv2.putText(frameBGR, last_found['name'] + ',' + emotion, org=(face.left(), face.top(
)), fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=1, color=last_found['color'], thickness=2)
con_sec, con_min, con_hour = convertMillis(
cap.get(cv2.CAP_PROP_POS_MSEC))
face_emotion_dict[last_found['name']][emotion].append(
"{0}:{1}:{2}".format(con_hour, con_min, round(con_sec, 3)))
print("{0}:{1}:{2} {3}".format(
con_hour, con_min, round(con_sec, 3), emotion))
cv2.imshow('frame', frameBGR)
key = cv2.waitKey(25)
if key == 27:
break
print(face_emotion_dict)
print("총 시간 : ", time.time() - start)
if cap.isOpened():
cap.release()
for i in range(1, 5):
cv2.destroyAllWindows()
cv2.waitKey(1)