def predict():
visualize = request.args.get('visualize', 'none')
resize = request.args.get('resize', 'original')
score_threshold = float(
request.args.get('score_threshold', str(config.score_threshold)))
nms_iou_threshold = float(
request.args.get('nms_iou_threshold', str(config.nms_iou_threshold)))
img = utils.extract_as_jpeg(request)
x = utils.img2tensor(img, resize=True)
x_original = utils.img2tensor(img, resize=False)
scale = (x_original[0].size()[1] / x[0].size()[1],
x_original[0].size()[2] / x[0].size()[2])
y = model(x)
ret = dict()
if len(y[0]['boxes']) > 0:
ret = utils.post_process(y[0]['boxes'], y[0]['scores'],
score_threshold, nms_iou_threshold)
if resize == 'original':
ret['boxes'] = utils.rescale_box(ret['boxes'], scale)
else:
ret['boxes'] = [[]]
ret['scores'] = []
if visualize == 'none':
return jsonify(ret)
img_to_show = x_original[0] if resize == 'original' else x
if visualize == 'bbox':
fig = utils.show_detection(img_to_show,
ret['boxes'],
pred_score=ret['scores'])
output = io.BytesIO()
FigureCanvas(fig).print_png(output)
return Response(output.getvalue(), mimetype='image/png')
elif visualize == 'blur':
return blur({'boxes': ret['boxes'], 'image': img})
import os
import matplotlib.pyplot as plt
from datasets import DetectionTestDataset
print('start setup detector')
detector = DetectionAlgorithm('weights/Jan_net_epoch=25batch=322.pth',
['open', 'close'],
conf_threshold=0.95,
iou_threshold=0.01)
print('end setup detector')
# detect, single image detect API test
if True:
img = imageio.imread(r'E:\agent3\lab\switch\JPEGImages\1001.jpg')
det = detector.detect(r'E:\agent3\lab\switch\JPEGImages\1001.jpg')
show_detection(img, det['absolute_box'])
# test batch convert(cpu)
if False:
for fname in glob.glob('E:/agent3/lab/switch2/JPEGImages/*.jpg'):
img = imageio.imread(fname)
det = detector.detect(img)
name, _ = os.path.splitext(os.path.split(fname)[-1])
target_path = os.path.join('images/eval', name + '.jpg')
print('{} -> {}'.format(fname, target_path))
show_detection(img,
det['absolute_box'],
text_list=det['class'],
datasets = TestDatasets(config.datasets)
num_test = 10
for img_numpy in datasets:
img_tensor = test_transform(img_numpy)
img_wrap = img_tensor.unsqueeze(0)
loc, conf = net(img_wrap)
conf_prob = F.softmax(conf, dim=2)
max_value, max_idx = conf_prob[0].max(0)
print(max_value)
print(max_idx)
loc_decoded = decode(loc, net.priors_center_offset)
h, w, c = img_numpy.shape
loc_abs = loc_decoded * torch.tensor([w, h, w, h]).float()
print(loc_abs[:, max_idx[0]])
show_detection(img_numpy, loc_abs[:, max_idx[0]]) # Find max conf
print(loc_abs[:, max_idx[1]])
show_detection(img_numpy, loc_abs[:, max_idx[1]]) # Find max conf
print(loc_abs[:, max_idx[2]])
show_detection(img_numpy, loc_abs[:, max_idx[2]]) # Find max conf
num_test -= 1
if num_test <= 0:
break
if False:
from torchviz import make_dot, make_dot_from_trace
make_dot(net(img_wrap))
boxes_base = torch.tensor([[0.1, 0.1, 0.3, 0.3], [0.7, 0.7, 0.9, 0.9]])
boxes_noised = boxes_base.repeat(1, 10).view(-1, 4)
boxes_noised = boxes_noised + torch.randn_like(boxes_noised) * 0.02
datasets = TestDatasets(config.datasets)
for img in datasets:
break
print(
'The image is only selected for test, boxes are fake input instead of output of model.'
)
h, w, c = img.shape
print('before nms')
show_detection(img, boxes_noised * torch.tensor([w, h, w, h]).float())
print('after nms')
fake_conf = torch.zeros(boxes_noised.shape[0], 3)
fake_conf[:10, 1] = 10.
fake_conf[10:, 2] = 10.
boxes_idx = nms(boxes_noised, fake_conf)
boxes_nmsed = boxes_noised[boxes_idx, :]
show_detection(img, boxes_nmsed * torch.tensor([w, h, w, h]).float())
print('corner case test(one class miss)')
fake_conf = torch.zeros(boxes_noised.shape[0], 3)
fake_conf[:10, 1] = 10.
#fake_conf[10:,2] = 10.
boxes_idx = nms(boxes_noised, fake_conf)
boxes_nmsed = boxes_noised[boxes_idx, :]
def main(model):
#parser = argparse.ArgumentParser(prog='test.py', description='ref_emotion.')
#parser.add_argument('--model', type=str, default='dgn')
#args = parser.parse_args()
lm_ph = tf.placeholder(tf.float32, [None, 51 * 2])
img_ph = tf.placeholder(tf.float32, [None, 224, 224, 3])
keep_prob = tf.placeholder(tf.float32)
with tf.Session() as sess:
dgn = vgg_face.DGN()
# dgn.train = False
dgn.build(lm_ph, keep_prob)
pred_dgn = tf.argmax(dgn.prob, 1)
dan = vgg_face.Vgg_face()
# dan.train = False
dan.build(img_ph, keep_prob)
pred_dan = tf.argmax(dan.prob, 1)
if model == 'weighted-sum':
prob_sum = tf.nn.softmax(dan.fc8 + dgn.fc3)
pred_sum = tf.argmax(prob_sum, 1)
elif model == 'joint-fine-tune':
saver = tf.train.Saver()
saver.restore(sess, '../model/dgan.ckpt')
prob_joint = tf.nn.softmax(dan.fc8 + dgn.fc3)
pred_joint = tf.argmax(prob_joint, 1)
sess.run(tf.global_variables_initializer())
stream = urllib.urlopen('http://admin:@192.168.0.194:3333/MJPEG.CGI')
#stream = cv2.VideoCapture('../../videos/20180226_154918.mp4')
#stream = cv2.VideoCapture(clip)
#if stream.isOpened() == False:
#print('Error opening video stream of file')
bytes = ''
emo_record = (np.ones(frames, dtype=int) * 5).tolist()
#TODO
'''
emo_buffer = collections.deque(maxlen=10)
state_record = []
final_states = []
'''
import time
time.sleep(10.0)
while True:
#while stream.isOpened():
bytes += stream.read(1024)
a = bytes.find('\xff\xd8')
b = bytes.find('\xff\xd9')
#ret, frame = stream.read()
if a != -1 and b != -1:
#if ret == True:
frame = cv2.imdecode(
np.fromstring(bytes[a:b + 2], dtype=np.uint8), 1)
bytes = bytes[b + 2:]
num, face, shape, shape_origin = dlib_detect(
frame, 2, face_det, lm_det, 224, 224)
if num == 1:
shape_norm = shape[17:] - shape[30]
shape_norm = shape_norm.reshape([1, 51 * 2])
if model == 'dan':
pred = sess.run(pred_dan,
feed_dict={
img_ph:
face.reshape([1, 224, 224, 3]),
keep_prob: 1.0
})
elif model == 'dgn':
pred = sess.run(pred_dgn,
feed_dict={
lm_ph: shape_norm,
keep_prob: 1.0
})
elif model == 'weighted-sum':
pred = sess.run(pred_dgn,
feed_dict={
img_ph:
face.reshape([1, 224, 224, 3]),
lm_ph: shape_norm,
keep_prob: 1.0
})
elif model == 'joint-fine-tune':
pred = sess.run(pred_joint,
feed_dict={
img_ph:
face.reshape([1, 224, 224, 3]),
lm_ph: shape_norm,
keep_prob: 1.0
})
emo_record.append(int(pred))
del emo_record[0]
ctr = collections.Counter(emo_record)
#TODO
'''
emo_buffer.append(ctr)
emo_his = collections.Counter()
emo_his_table = np.zeros(len(emo_list))
emo_now_table = np.zeros(len(emo_list))
for c in emo_buffer:
emo_his += c
emo_his_avg = [v/float(len(emo_buffer)) for v in emo_his.values()]
emo_his = emo_his.items()
emo_his = np.array([np.array([list(emo_his[i])[0], emo_his_avg[i]]) for i in range(len(emo_his))])
emo_his_table[emo_his[:,0].astype(int)] = emo_his[:,1]
emo_now = ctr.items()
emo_now = np.array([np.array([list(e)[0], list(e)[1]]) for e in emo_now])
emo_now_table[emo_now[:,0].astype(int)] = emo_now[:,1]
state = np.array([(emo_now_table[i]>=3 and (emo_now_table[i]-emo_his_table[i])>=0.0) for i in range(len(emo_list))]).astype(int)
state_int = 0
for i, j in enumerate(state):
state_int += j<<i
state_record.append(state_int)
if len(state_record) == 10:
state_ctr = collections.Counter(state_record)
final_state = state_ctr.most_common()[0][0]
final_states.append(final_state)
del state_record[:]
'''
emotion = emo_list[ctr.most_common()[0][0]]
#print(emotion)
im_show = show_detection(frame, shape_origin, 1, emotion)
cv2.imshow('frame', im_show)
else:
cv2.imshow('frame', frame)
if cv2.waitKey(3) & 0xFF == ord('q'):
break
#else:
#break
#stream.release()
cv2.destroyAllWindows()
channel_swap, to_tensor, normalize, train_transform, test_transform, \
enchancement_transform, base_transform,resize
from utils import rectangle, show_detection
if __name__ == '__main__':
dataset = DetectionDataset(r'E:\agent3\lab\switch', ['open', 'close'])
for c, d, e in dataset:
break
if True:
print('test random_horizontal_flip')
for i in range(5):
ct = c.copy()
dt = d.copy()
ct, dt = random_horizontal_flip(ct, dt)
show_detection(ct, dt)
if True:
print('test random_crop')
for i in range(7):
ct = c.copy()
dt = d.copy()
ce = e.copy()
ct, dt, ce = random_crop(ct, dt, ce)
show_detection(ct, dt)
if True:
print('test photometric_distort')
for i in range(5):
ct = c.copy()
dt = d.copy()
def main():
parser = argparse.ArgumentParser(prog='test.py',
description='ref_emotion.')
parser.add_argument('--model', type=str, default='dgn')
args = parser.parse_args()
lm_ph = tf.placeholder(tf.float32, [None, 51 * 2])
img_ph = tf.placeholder(tf.float32, [None, 224, 224, 3])
keep_prob = tf.placeholder(tf.float32)
with tf.Session() as sess:
dgn = vgg_face.DGN()
# dgn.train = False
dgn.build(lm_ph, keep_prob)
pred_dgn = tf.argmax(dgn.prob, 1)
dan = vgg_face.Vgg_face()
# dan.train = False
dan.build(img_ph, keep_prob)
pred_dan = tf.argmax(dan.prob, 1)
if args.model == 'weighted-sum':
prob_sum = tf.nn.softmax(dan.fc8 + dgn.fc3)
pred_sum = tf.argmax(prob_sum, 1)
elif args.model == 'joint-fine-tune':
saver = tf.train.Saver()
saver.restore(sess, '../model/dgan.ckpt')
prob_joint = tf.nn.softmax(dan.fc8 + dgn.fc3)
pred_joint = tf.argmax(prob_joint, 1)
sess.run(tf.global_variables_initializer())
cap = cv2.VideoCapture(0)
emo_record = (np.ones(frames, dtype=int) * 5).tolist()
while (cap.isOpened()):
ret, frame = cap.read()
if ret == True:
num, face, shape, shape_origin = dlib_detect(
frame, 2, face_det, lm_det, 224, 224)
if num == 1:
shape_norm = shape[17:] - shape[30]
shape_norm = shape_norm.reshape([1, 51 * 2])
if args.model == 'dan':
pred = sess.run(pred_dan,
feed_dict={
img_ph:
face.reshape([1, 224, 224, 3]),
keep_prob: 1.0
})
elif args.model == 'dgn':
pred = sess.run(pred_dgn,
feed_dict={
lm_ph: shape_norm,
keep_prob: 1.0
})
elif args.model == 'weighted-sum':
pred = sess.run(pred_dgn,
feed_dict={
img_ph:
face.reshape([1, 224, 224, 3]),
lm_ph: shape_norm,
keep_prob: 1.0
})
elif args.model == 'joint-fine-tune':
pred = sess.run(pred_joint,
feed_dict={
img_ph:
face.reshape([1, 224, 224, 3]),
lm_ph: shape_norm,
keep_prob: 1.0
})
emo_record.append(int(pred))
del emo_record[0]
ctr = collections.Counter(emo_record)
emotion = emo_list[ctr.most_common()[0][0]]
im_show = show_detection(frame, shape_origin, 1, emotion)
cv2.imshow('frame', im_show)
else:
cv2.imshow('frame', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()