def infer():
#Load model
network = EmotionRecognition()
network.build_network()
#Predict
result = network.predict(format_image_norm(image))
label = np.argmax(result[0])
print("Emotion: " + str(EMOTIONS[np.argmax(result[0])]))
return label
def test():
# images, labels, images_testing, labels_testing = loadData()
images_testing, labels_testing = loadDate('./emotionDataset/test')
network = EmotionRecognition()
# network.build_network()
network.load_model2()
print('[+] Testing load model')
result = network.predict(images_testing)
# num = 0
y_true = []
y_pred = []
for label in range(7):
for ii in range(len(result)):
pre = list(result[ii]).index(np.max(result[ii]))
gt = list(labels_testing[ii]).index(np.max(labels_testing[ii]))
# y_true.append(gt)
# y_pred.append(pre)
# target_names = ['class 0', 'class 1', 'class 2', 'class 3', 'class 4', 'class 5', 'class 6']
# print(classification_report(y_true, y_pred, target_names=target_names))
if gt == label:
y_true.append(1)
else:
y_true.append(0)
if pre == label:
y_pred.append(1)
else:
y_pred.append(0)
recall = recall_score(y_true, y_pred)
precision = precision_score(y_true, y_pred)
accuracy = accuracy_score(y_true, y_pred)
f1_measure = f1_score(y_true, y_pred)
similarity = jaccard_similarity_score(y_true, y_pred)
print('###label :%d' % label)
print(
'recall:%s precision:%s similarity:%s F1_measure:%s accuracy:%s'
% (recall, precision, similarity, f1_measure, accuracy))
print(
"========================================================================="
)
y_true = []
y_pred = []
network = EmotionRecognition()
network.build_network()
video_capture = cv2.VideoCapture(0)
font = cv2.FONT_HERSHEY_SIMPLEX
feelings_faces = []
for index, emotion in enumerate(EMOTIONS):
feelings_faces.append(cv2.imread('./emojis/' + emotion + '.png', -1))
while True:
# Capture frame-by-frame
ret, frame = video_capture.read()
# Predict result with network
result = network.predict(format_image(frame))
# Draw face in frame
# for (x,y,w,h) in faces:
# cv2.rectangle(frame, (x,y), (x+w,y+h), (255,0,0), 2)
# Write results in frame
if result is not None:
for index, emotion in enumerate(EMOTIONS):
cv2.putText(frame, emotion, (10, index * 20 + 20),
cv2.FONT_HERSHEY_PLAIN, 0.5, (0, 255, 0), 1)
cv2.rectangle(frame, (130, index * 20 + 10),
(130 + int(result[0][index] * 100),
(index + 1) * 20 + 4), (255, 0, 0), -1)
#face_image = feelings_faces[result[0].index(max(result[0]))]
network.build_network()
video_capture = cv2.VideoCapture(0)
font = cv2.FONT_HERSHEY_SIMPLEX
feelings_faces = []
for index, emotion in enumerate(EMOTIONS):
feelings_faces.append(cv2.imread('./emojis/' + emotion + '.png', -1))
while True:
# Capture frame-by-frame
ret, frame = video_capture.read()
img, faces = format_image(frame)
# Predict result with network
result = network.predict(img)
# Draw face in frame
if faces is not None:
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
# Write results in frame
if result is not None:
for index, emotion in enumerate(EMOTIONS):
cv2.putText(frame, emotion, (10, index * 20 + 20),
cv2.FONT_HERSHEY_PLAIN, 0.5, (0, 255, 0), 1)
cv2.rectangle(frame, (130, index * 20 + 10),
(130 + int(result[0][index] * 100),
(index + 1) * 20 + 4), (255, 0, 0), -1)
import numpy as np
import matplotlib.pyplot as plt
# Load Model
network = EmotionRecognition()
network.build_network()
images = np.load(join(SAVE_DIRECTORY, SAVE_DATASET_IMAGES_FILENAME))
labels = np.load(join(SAVE_DIRECTORY, SAVE_DATASET_LABELS_FILENAME))
images = images.reshape([-1, SIZE_FACE, SIZE_FACE, 1])
labels = labels.reshape([-1, len(EMOTIONS)])
print('[+] Loading Data')
data = np.zeros((len(EMOTIONS), len(EMOTIONS)))
for i in range(images.shape[0]):
result = network.predict(images[i])
data[np.argmax(labels[i]), result[0].tolist().index(max(result[0]))] += 1
#print x[i], ' vs ', y[i]
# Take % by column
for i in range(len(data)):
total = np.sum(data[i])
for x in range(len(data[0])):
data[i][x] = data[i][x] / total
print(data)
print('[+] Generating graph')
c = plt.pcolor(data,
edgecolors='k',
linewidths=4,
cmap='Blues',
network = EmotionRecognition()
network.build_network()
video_capture = cv2.VideoCapture(0)
font = cv2.FONT_HERSHEY_SIMPLEX
feelings_faces = []
for index, emotion in enumerate(EMOTIONS):
feelings_faces.append(cv2.imread('./emojis/' + emotion + '.png', -1))
while True:
# Capture frame-by-frame
ret, frame = video_capture.read()
# Predict result with network
result = network.predict(format_image(frame))
# Draw face in frame
# for (x,y,w,h) in faces:
# cv2.rectangle(frame, (x,y), (x+w,y+h), (255,0,0), 2)
# Write results in frame
if result is not None:
for index, emotion in enumerate(EMOTIONS):
cv2.putText(frame, emotion, (10, index * 20 + 20), cv2.FONT_HERSHEY_PLAIN, 0.5, (0, 255, 0), 1);
cv2.rectangle(frame, (130, index * 20 + 10), (130 + int(result[0][index] * 100), (index + 1) * 20 + 4), (255, 0, 0), -1)
face_image = feelings_faces[result[0].index(max(result[0]))]
# Ugly transparent fix
for c in range(0, 3):
# it into OpenCV format
resp = urllib.urlopen(url)
image = np.asarray(bytearray(resp.read()), dtype="uint8")
image = cv2.imdecode(image, cv2.IMREAD_COLOR)
return image
num = input("wanna take image(1) or webcam(2)")
if (num == 1):
img = url_to_image(
"http://www.mobilealabamadentist.com/sites/default/files/BoywithToothBrush.jpg"
)
video_capture = img
frame = video_capture
result = network.predict(format_image(video_capture))
result[0][3] = result[0][3] + 0.2
print type(result)
feelings_faces = []
for index, emotion in enumerate(EMOTIONS):
feelings_faces.append(cv2.imread('./emojis/' + emotion + '.png', -1))
print "result is"
print result
elif (num == 2):
#cv2.namedWindow("preview")
#video_capture = cv2.VideoCapture(0)
#feelings_faces = []
#for index, emotion in enumerate(EMOTIONS):
# feelings_faces.append(cv2.imread('./emojis/' + emotion + '.png', -1))
#while True:
for index, emotion in enumerate(EMOTIONS):
feelings_faces.append(cv2.imread('./emojis/' + emotion + '.png', -1))
if __name__ == '__main__':
if len(sys.argv) > 2:
im = sys.argv[1]
else:
print("Please pass a image file as argument")
exit(1);
image = cv2.imread(im)
# Predict result with network
result = network.predict(format_image(image))
SAVE_DIR = os.path.join("..","output");
# Write results to output folder
if result is not None:
for index, emotion in enumerate(EMOTIONS):
# Appends a descriptive text of the detected image
cv2.putText(image, emotion, (10, index * 20 + 20), cv2.FONT_HERSHEY_PLAIN, 0.5, (0, 255, 0), 1);
# Append a rectangle area against the detect image
cv2.rectangle(image, (130, index * 20 + 10), (130 + int(result[0][index] * 100), (index + 1) * 20 + 4), (255, 0, 0), -1)
#Appends the emotion
def detect():
video_capture = cv2.VideoCapture(0)
font = cv2.FONT_HERSHEY_SIMPLEX
feelings_faces = []
for index, emotion in enumerate(EMOTIONS):
feelings_faces.append(cv2.imread('./emojis/' + emotion + '.png', -1))
voting_1 = []
cal_1 = []
voting_2 = []
cal_2 = []
count = 0
network = EmotionRecognition()
# network.build_network()
network.load_model2()
print('[+] Testing load model')
while True:
# Capture frame-by-frame
ret, frame = video_capture.read()
#cv2.imshow('Video', frame)
cv2.imwrite("%05d.jpg", frame)
# Predict result with network
result = network.predict(format_image(frame))
# Draw face in frame
# for (x,y,w,h) in faces:
# cv2.rectangle(frame, (x,y), (x+w,y+h), (255,0,0), 2)
# Write results in frame
if result is not None:
for index, emotion in enumerate(
EMOTIONS): # 3 顯示每一frame之偵測資訊(文字、直方圖)
cv2.putText(frame, emotion, (10, index * 20 + 20),
cv2.FONT_HERSHEY_PLAIN, 0.5, (0, 255, 0), 1)
cv2.rectangle(frame, (130, index * 20 + 10),
(130 + int(result[0][index] * 100),
(index + 1) * 20 + 4), (255, 0, 0), -1)
count += 1
print(result[0].tolist().index(max(result[0])))
voting_1.append(result[0].tolist().index(max(result[0])))
if len(voting_1) == 60:
for i in range(7):
cal_1.append(voting_1.count(i))
maximum_face_times = np.max(cal_1)
maximum_face = cal_1.index(maximum_face_times)
# print(maximum_face, maximum_face_times)
voting_2.append(maximum_face)
voting_1.clear()
cal_1.clear()
if (
len(voting_2) == 7
): # 之後改次數--!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
for i in range(7):
cal_2.append(voting_2.count(i))
maximum_face_times_2 = np.max(cal_2)
maximum_face_2 = cal_2.index(maximum_face_times_2)
print("Voting 結果︰第", maximum_face_2, "類")
face_image = feelings_faces[maximum_face_2]
# emotion = ['angry', 'disgusted', 'fearful', 'happy', 'sad', 'surprised', 'neutral']
for c in range(0, 3):
frame[200:320, 10:130, c] = face_image[:, :, c] * (
face_image[:, :, 3] /
255.0) + frame[200:320, 10:130,
c] * (1.0 - face_image[:, :, 3] / 255.0)
voting_2.clear()
cal_2.clear()
###response
from pygame import mixer
import random
mixer.init()
mixer.music.load(
join('./response/', str(maximum_face_2)) + '/' +
str(random.randint(0, 3)) + '.mp3')
mixer.music.play()
insert_mongo(int(maximum_face_2))
# connection = MongoClient('140.138.145.77', 27017)
# connection.admin.authenticate("bigmms", "bigmms1413b")
# tdb = connection.musicky
# post = tdb.test
#
# # for i in tdb.test.find({"emotion": EMOTIONS[maximum_face_2]}): print(i)
# pipeline = [{"$match": {"emotion": EMOTIONS[maximum_face_2]}},
# {"$sample": {"size": 1}}] # 隨機取出一個"emotion":"sad"的資料
# data = list(tdb.test.aggregate(pipeline))
# print(data)
#
# a = str(data)
#
# delete = ["[", "{", "}", "]", "\'"] # 刪除不必要的符號
# for i in range(len(delete)):
# a = a.replace(delete[i], "")
#
# replace = [": ", ", "] # 替換不必要的符號
# for j in range(len(replace)):
# a = a.replace(replace[j], ",")
#
# a = a.split(",") # 以逗號區分不同字串
# rand_keyword = a[a.index("keyword") + 1] # 根據不同的情緒，抓出所要使用的keyword
# print(rand_keyword)
# keyword = rand_keyword
# keyword = urllib.parse.quote(rand_keyword)
# url = "https://www.youtube.com/results?search_query=" + keyword
# crawler(url)
if (
count >= 420
): # and (count%180)<=20): # Ugly transparent fix # 表情圖片顯示於螢幕 (停留20 frames)
for c in range(0, 3):
frame[200:320, 10:130, c] = face_image[:, :, c] * (
face_image[:, :, 3] /
255.0) + frame[200:320, 10:130,
c] * (1.0 - face_image[:, :, 3] / 255.0)
# # Display the resulting frame
cv2.imshow('Video', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
video_capture.release()
cv2.destroyAllWindows()