def process():
global framerate
l = os.listdir('.')
for f in l:
if f.endswith('.mov'):
print 'Processing...%s'%f
#mov2avi.convert2avi(f,framerate)
detect_face.detect(f.replace('.mov','.avi'))
generate_jboost.make_jboost_files(xml,f.replace('.mov',''))
print 'Done!'
def process():
global framerate
l = os.listdir('.')
for f in l:
if f.endswith('.mov'):
print 'Processing...%s' % f
#mov2avi.convert2avi(f,framerate)
detect_face.detect(f.replace('.mov', '.avi'))
generate_jboost.make_jboost_files(xml, f.replace('.mov', ''))
print 'Done!'
def predict(input_file, model):
"""
画像ファイルを入力して判別する
:param input_file: 画像ファイル
:param model: 学習済みモデル
:return:
"""
face_list = df.detect(input_file)
face_position_list = df.detect(input_file=input_file, only_position=True)
for orig_image, rect in zip(face_list, face_position_list):
image = cv2.resize(orig_image, (in_size, in_size),
interpolation=cv2.INTER_CUBIC)
image = image.transpose(2, 0, 1) / 255. # 正規化
index = model.predict(np.array([image], dtype=np.float32))
name = label_list[index]
print("{}さんと予測しました。".format(name[0]))
cv2.imshow('image', paint_on_face(input_file, rect, name[1]))
cv2.waitKey(0)
cv2.destroyAllWindows()
def main():
model_path = './mtcnn.pb'
imagePaths = list(
paths.list_images(
'/home/ravikiranb/homedir/images/train_img/somaliya/'))
j = 0
graph = tf.Graph()
with graph.as_default():
with open(model_path, 'rb') as f:
graph_def = tf.GraphDef.FromString(f.read())
tf.import_graph_def(graph_def, name='')
config = tf.ConfigProto(allow_soft_placement=True,
intra_op_parallelism_threads=4,
inter_op_parallelism_threads=4)
config.gpu_options.allow_growth = True
sess = tf.Session(graph=graph, config=config)
for i in imagePaths:
j = j + 1
img = cv2.imread(i, 1)
img_h, img_w = img.shape[:2]
img_a = img_h * img_w
bbox, scores, landmarks = detect(img,
sess,
graph,
min_size=40,
factor=0.709,
thresholds=[0.6, 0.7, 0.7])
print('total box:', len(bbox))
for box, pts in zip(bbox, landmarks):
box = box.astype('int32')
box_w = box[3] - box[1]
box_h = box[2] - box[0]
box_a = box_w * box_h
percent = box_a * 100 / img_a
if percent > 3.0:
print('percentage of bounding box in total image : {:.2f}'.
format(percent))
img = cv2.rectangle(img, (box[1], box[0]), (box[3], box[2]),
(255, 0, 0), 3)
pts = pts.astype('int32')
for i in range(5):
img = cv2.circle(img, (pts[i + 5], pts[i]), 4, (0, 0, 255),
8)
cv2.imwrite('./opimages/imageeee' + str(j) + '.jpg', img)
def draw(image, coords, preds):
for i in range(len(coords)):
x, y, w, h = coords[i]
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
if preds[i][0] == 1:
text = 'no mask'
else:
text = 'mask'
font = cv2.FONT_HERSHEY_SIMPLEX
bottomLeftCornerOfText = (x, y)
fontScale = 1
fontColor = (0, 0, 255)
lineType = 2
cv2.putText(image, text, bottomLeftCornerOfText, font, fontScale,
fontColor, lineType)
return image
image = cv2.imread(args.image)
coords = detect_face.detect(image)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
preds = predict(image, coords)
image = draw(image, coords, preds)
cv2.imshow('', cv2.cvtColor(image, cv2.COLOR_RGB2BGR))
#window_name='image'
#cv2.imshow(window_name, image)
cv2.waitKey(0)
from detect_face import detect
import cv2
img=cv2.imread("./data/test01.jpg")
print(detect(img))
print(detect(img))
def main():
image = cv2.imread('test1.jpg')
#print(image)
faces, boxes = np.array(detect(image))
count = 0
for face in faces:
scipy.misc.imsave('emotion/real_face/testing/{}.jpg'.format(count),
face)
count += 1
testing_dir = 'emotion/real_face'
IMAGE_SIZE = [48, 48]
testing_datagen = ImageDataGenerator(
rescale=1. / 255, preprocessing_function=preprocess_input)
testing_generator = testing_datagen.flow_from_directory(
testing_dir, target_size=IMAGE_SIZE, batch_size=200)
model = joblib.load('model.pkl')
num_step = len(testing_generator)
print('step should be:', num_step)
#testing_img = cv2.imread('emotion/testing/1.jpg')
predictions = model.predict_generator(testing_generator,
steps=num_step,
max_queue_size=10,
workers=1,
use_multiprocessing=False,
verbose=0)
#predicted_classes = convert_to_class(predictions)
print(predictions)
emotions = [
'anger', 'contempt', 'disgust', 'fear', 'happy', 'sadness', 'surprise'
]
num_label = predictions.shape[0]
labels = []
for i in range(predictions.shape[0]):
labels.append(emotions[np.argmax(predictions[i])])
print(labels)
true_label = labels.count('surprise')
accuracy = true_label / num_label
print('accuracy is:', accuracy)
#confusion_matrix = []
count = 0
for emotion in emotions:
num_emo = labels.count(emotion)
#confusion_matrix.append(count + ':' + num_emo)
print(emotion + ':')
print(num_emo)
count += 1
face_list = []
for ((top, right, bottom, left), label) in zip(boxes, labels):
cv2.rectangle(image, (left, top), (right, bottom), (0, 255, 0), 2)
y = top - 15 if top - 15 > 15 else top + 15
cv2.putText(image, label, (left, y), cv2.FONT_HERSHEY_SIMPLEX, 0.75,
(0, 255, 0), 2)
face_list.append((label, ((top + bottom) / 2, (left + right) / 2)))
print(face_list)
cv2.namedWindow("output", cv2.WINDOW_NORMAL)
im2 = image.copy()
im2[:, :, 0] = image[:, :, 2]
im2[:, :, 2] = image[:, :, 0]
#imS = cv2.resize(im2, (600, 480))
scipy.misc.imsave('kkk.jpg', im2)
cv2.imshow("Image", im2)
cv2.waitKey(0)
return face_list
if __name__ == '__main__':
logfile = open('log.xls', 'w+')
stream_entry('time', 'person', 'bounding box', 'detection_score', 'camera', 'screenshot name')
cap = cv2.VideoCapture(0)
cpt.initialize_capture()
while True:
hasFrame, frame = cap.read()
input_key = cv2.waitKey(10)
if input_key == 27 or not hasFrame:
break
frame_start = datetime.datetime.now()
bounding_boxes, scores = df.detect(frame)
screenshot_filename = cpt.capture_logic(bounding_boxes, scores, frame, frame_start)
for i in range(len(bounding_boxes)):
box = bounding_boxes[i]
score = scores[i]
height, width, channels = frame.shape
x = box[0] if box[0] >= 0 else 0
x2 = box[2] if box[2] <= width else width
y = box[1] if box[1] >= 0 else 0
y2 = box[3] if box[3] <= height else height
crop_img = frame[y:y2, x:x2]
cv2.imshow("Face off", crop_img)