def __init__(self):
# hyper-parameters for bounding boxes shape
self.frame_window = 10
self.emotion_offsets = (20, 40)
# loading models
self.face_detector = Decode('data/voc_classes.txt',
'./weights/best_model.h5')
self.emotion_classifier = load_model(
'model/fer2013_mini_XCEPTION.102-0.66.hdf5', compile=False)
self.emotion_labels = get_labels('fer2013')
# getting input model shapes for inference
self.emotion_target_size = self.emotion_classifier.input_shape[1:3]
# starting lists for calculating modes
self.emotion_window = []
def __init__(self):
# self.input_shape越大，精度会上升，但速度会下降。
# self.input_shape = (320, 320)
self.input_shape = (416, 416)
# self.input_shape = (608, 608)
# COCO
self.file = 'data/coco_classes.txt'
self.annotation_path = 'annotation/coco2017_val.txt'
self.classes = read_class_names(self.file)
# 是否保存画框的照片
# self.write_image = False
self.write_image = True
self.write_image_path = "./mAP/detection/"
self.show_label = True
self.num_classes = len(self.classes)
# 只用keras
self._decode = Decode(0.3, 0.45, self.input_shape, 'yolo_bgr_mAP_46.h5', self.file)
num_classes = len(class_names)
# 步id，无需设置，会自动读。
iter_id = 0
# 创建模型
Backbone = select_backbone(cfg.backbone_type)
backbone = Backbone(**cfg.backbone)
Fpn = select_fpn(cfg.fpn_type)
fpn = Fpn(**cfg.fpn)
Loss = select_loss(cfg.fcos_loss_type)
fcos_loss = Loss(**cfg.fcos_loss)
Head = select_head(cfg.head_type)
head = Head(fcos_loss=fcos_loss, nms_cfg=cfg.nms_cfg, **cfg.head)
fcos = FCOS(backbone, fpn, head)
_decode = Decode(fcos, class_names, use_gpu, cfg, for_test=False)
# 加载权重
if cfg.train_cfg['model_path'] is not None:
# 加载参数, 跳过形状不匹配的。
load_weights(fcos, cfg.train_cfg['model_path'])
strs = cfg.train_cfg['model_path'].split('step')
if len(strs) == 2:
iter_id = int(strs[1][:8])
# 冻结，使得需要的显存减少。低显存的卡建议这样配置。
backbone.freeze()
if use_gpu: # 如果有gpu可用，模型（包括了权重weight）存放在gpu显存里
fcos = fcos.cuda()
Backbone = select_backbone(cfg.backbone_type)
backbone = Backbone(**cfg.backbone)
Head = select_head(cfg.head_type)
cfg.head['drop_block'] = False # 预测时关闭DropBlock，以获得一致的推理结果。
head = Head(yolo_loss=None, nms_cfg=cfg.nms_cfg, **cfg.head)
yolo = YOLO(backbone, head)
x = keras.layers.Input(shape=(None, None, 3), name='x', dtype='float32')
im_size = keras.layers.Input(shape=(2, ), name='im_size', dtype='int32')
outputs = yolo.get_outputs(x)
preds = yolo.get_prediction(outputs, im_size)
predict_model = keras.models.Model(inputs=[x, im_size], outputs=preds)
predict_model.load_weights(model_path, by_name=True, skip_mismatch=True)
predict_model.summary(line_length=130)
_decode = Decode(predict_model, all_classes, use_gpu, cfg, for_test=True)
if not os.path.exists('images/res/'): os.mkdir('images/res/')
path_dir = os.listdir('images/test')
capture = cv2.VideoCapture(video_path)
width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
video_name = os.path.split(video_path)[-1]
if not os.path.exists(output_dir):
os.makedirs(output_dir)
out_path = os.path.join(output_dir, video_name)
writer = cv2.VideoWriter(out_path, fourcc, fps, (width, height))
index = 1
start = time.time()
classes_path = 'data/coco_classes.txt'
# model_path可以是'yolov4.h5'、'./weights/step00001000.h5'这些。
# model_path = 'yolov4.h5'
model_path = './weights/step00001000.h5'
# input_shape越大，精度会上升，但速度会下降。
# input_shape = (320, 320)
input_shape = (416, 416)
# input_shape = (608, 608)
num_anchors = 3
all_classes = get_classes(classes_path)
num_classes = len(all_classes)
inputs = layers.Input(shape=(None, None, 3))
yolo = YOLOv4(inputs, num_classes, num_anchors)
yolo.load_weights(model_path, by_name=True)
_decode = Decode(0.05, 0.45, input_shape, yolo, all_classes)
# detect images in test floder.
for (root, dirs, files) in os.walk('images/test'):
if files:
start = time.time()
for f in files:
path = os.path.join(root, f)
image = cv2.imread(path)
image, boxes, scores, classes = _decode.detect_image(
image, draw_image=True)
cv2.imwrite('images/res/' + f, image)
print('total time: {0:.6f}s'.format(time.time() - start))
set_session(tf.Session(config=config))
# parameters for loading data and images
facesDetect = {
'classes_path': 'data/voc_classes.txt',
'model_path': './weights/best_model.h5'
}
emotion_model_path = 'model/fer2013_mini_XCEPTION.102-0.66.hdf5'
emotion_labels = get_labels('fer2013')
# hyper-parameters for bounding boxes shape
frame_window = 10
emotion_offsets = (20, 40)
# loading models
face_detector = Decode(facesDetect['classes_path'], facesDetect['model_path'])
emotion_classifier = load_model(emotion_model_path, compile=False)
# getting input model shapes for inference
emotion_target_size = emotion_classifier.input_shape[1:3]
# starting lists for calculating modes
emotion_window = []
def job1():
global frame1
global a
capture = cv2.VideoCapture(0)
if capture.isOpened():
capture.set(cv2.CAP_PROP_FRAME_WIDTH, 240)
import cv2
import os
import numpy as np
import tensorflow as tf
from model.decode_np import Decode
# 6G的卡，训练时如果要预测，则设置use_gpu = False，否则显存不足。
use_gpu = False
use_gpu = True
# 显存分配。
if use_gpu:
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
else:
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
from keras.backend.tensorflow_backend import set_session
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.75
set_session(tf.Session(config=config))
if __name__ == '__main__':
classes_path = 'data/voc_classes.txt'
model_path = './weights/best_model.h5'
# 是否给图片画框。不画可以提速。读图片、后处理还可以继续优化。
draw_image = True
# draw_image = False
_decode = Decode(classes_path, model_path)
_decode.webcam_detect(draw_image)
class_names = get_classes(cfg.classes_path)
num_classes = len(class_names)
_anchors = copy.deepcopy(cfg.anchors)
num_anchors = len(cfg.anchor_masks[0]) # 每个输出层有几个先验框
_anchors = np.array(_anchors)
_anchors = np.reshape(_anchors, (-1, num_anchors, 2))
_anchors = _anchors.astype(np.float32)
# 步id，无需设置，会自动读。
iter_id = 0
# 多尺度训练
inputs = layers.Input(shape=(None, None, 3))
model_body = YOLOv4(inputs, num_classes, num_anchors)
_decode = Decode(cfg.conf_thresh, cfg.nms_thresh, cfg.input_shape,
model_body, class_names)
# 模式。 0-从头训练，1-读取之前的模型继续训练（model_path可以是'yolov4.h5'、'./weights/step00001000.h5'这些。）
pattern = cfg.pattern
if pattern == 1:
model_body.load_weights(cfg.model_path,
by_name=True) # , skip_mismatch=True)
strs = cfg.model_path.split('step')
if len(strs) == 2:
iter_id = int(strs[1][:8])
# 冻结，使得需要的显存减少。6G的卡建议这样配置。11G的卡建议不冻结。
# freeze_before = 'conv2d_60'
# freeze_before = 'conv2d_72'
freeze_before = 'conv2d_86'
for i in range(len(model_body.layers)):
# 多尺度训练
inputs = P.data(name='input_1',
shape=[-1, 3, -1, -1],
append_batch_size=False,
dtype='float32')
if algorithm == 'YOLOv4':
output_l, output_m, output_s = YOLOv4(inputs,
num_classes,
num_anchors,
is_test=False,
trainable=True)
elif algorithm == 'YOLOv3':
backbone = Resnet50Vd()
head = YOLOv3Head(
keep_prob=1.0) # 一定要设置keep_prob=1.0, 为了得到一致的推理结果
yolov3 = YOLOv3(backbone, head)
output_l, output_m, output_s = yolov3(inputs)
eval_fetch_list = [output_l, output_m, output_s]
eval_prog = eval_prog.clone(for_test=True)
gpu_id = int(os.environ.get('FLAGS_selected_gpus', 0))
place = fluid.CUDAPlace(gpu_id) if use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup_prog)
fluid.load(eval_prog, model_path, executor=exe)
_decode = Decode(algorithm, anchors, conf_thresh, nms_thresh, input_shape,
exe, eval_prog, all_classes)
test_dev(_decode, eval_fetch_list, images, test_pre_path, eval_batch_size,
draw_image)
# 验证时的批大小
eval_batch_size = 4
# 验证集图片的相对路径
# eval_pre_path = '../VOCdevkit/VOC2012/JPEGImages/'
# anno_file = 'annotation_json/voc2012_val.json'
eval_pre_path = '../COCO/val2017/'
anno_file = '../COCO/annotations/instances_val2017.json'
with open(anno_file, 'r', encoding='utf-8') as f2:
for line in f2:
line = line.strip()
dataset = json.loads(line)
images = dataset['images']
num_anchors = 3
all_classes = get_classes(classes_path)
num_classes = len(all_classes)
inputs = layers.Input(shape=(None, None, 3))
yolo = YOLOv4(inputs, num_classes, num_anchors)
yolo.load_weights(model_path, by_name=True)
_clsid2catid = copy.deepcopy(clsid2catid)
if num_classes != 80: # 如果不是COCO数据集，而是自定义数据集
_clsid2catid = {}
for k in range(num_classes):
_clsid2catid[k] = k
_decode = Decode(conf_thresh, nms_thresh, input_shape, yolo, all_classes)
box_ap = eval(_decode, images, eval_pre_path, anno_file, eval_batch_size, _clsid2catid, draw_image)
ins_anno_ids = val_dataset.getAnnIds(imgIds=img_id, iscrowd=False) # 读取这张图片所有标注anno的id
if len(ins_anno_ids) == 0:
continue
img_anno = val_dataset.loadImgs(img_id)[0]
images.append(img_anno)
all_classes = get_classes(cfg.classes_path)
num_classes = len(all_classes)
# 创建模型
Backbone = select_backbone(cfg.backbone_type)
backbone = Backbone(**cfg.backbone)
Head = select_head(cfg.head_type)
head = Head(yolo_loss=None, nms_cfg=cfg.nms_cfg, **cfg.head)
ppyolo = PPYOLO(backbone, head)
if use_gpu:
ppyolo = ppyolo.cuda()
ppyolo.load_state_dict(torch.load(model_path))
ppyolo.eval() # 必须调用model.eval()来设置dropout和batch normalization layers在运行推理前，切换到评估模式. 不这样做的化会产生不一致的推理结果.
_clsid2catid = copy.deepcopy(clsid2catid)
if num_classes != 80: # 如果不是COCO数据集，而是自定义数据集
_clsid2catid = {}
for k in range(num_classes):
_clsid2catid[k] = k
_decode = Decode(ppyolo, all_classes, use_gpu, cfg, for_test=False)
box_ap = eval(_decode, images, eval_pre_path, anno_file, eval_batch_size, _clsid2catid, draw_image, draw_thresh)
# 创建模型
Backbone = select_backbone(cfg.backbone_type)
backbone = Backbone(**cfg.backbone)
Fpn = select_fpn(cfg.fpn_type)
fpn = Fpn(**cfg.fpn)
Head = select_head(cfg.head_type)
head = Head(fcos_loss=None, nms_cfg=cfg.nms_cfg, **cfg.head)
fcos = FCOS(backbone, fpn, head)
if use_gpu:
fcos = fcos.cuda()
fcos.load_state_dict(torch.load(model_path))
fcos.eval() # 必须调用model.eval()来设置dropout和batch normalization layers在运行推理前，切换到评估模式。
_decode = Decode(fcos, all_classes, use_gpu, cfg, for_test=True)
if not os.path.exists('images/res/'): os.mkdir('images/res/')
path_dir = os.listdir('images/test')
# 读数据的线程
test_dic = {}
thr = threading.Thread(target=read_test_data,
args=(path_dir,
_decode,
test_dic))
thr.start()
key_list = list(test_dic.keys())
key_len = len(key_list)
while key_len == 0:
images.append(img_anno)
all_classes = get_classes(cfg.classes_path)
num_classes = len(all_classes)
# 创建模型
Backbone = select_backbone(cfg.backbone_type)
backbone = Backbone(**cfg.backbone)
Head = select_head(cfg.head_type)
cfg.head['drop_block'] = False # 预测时关闭DropBlock，以获得一致的推理结果。
head = Head(yolo_loss=None, nms_cfg=cfg.nms_cfg, **cfg.head)
yolo = YOLO(backbone, head)
x = keras.layers.Input(shape=(None, None, 3), name='x', dtype='float32')
im_size = keras.layers.Input(shape=(2, ), name='im_size', dtype='int32')
outputs = yolo.get_outputs(x)
preds = yolo.get_prediction(outputs, im_size)
predict_model = keras.models.Model(inputs=[x, im_size], outputs=preds)
predict_model.load_weights(model_path, by_name=True, skip_mismatch=True)
predict_model.summary(line_length=130)
_clsid2catid = copy.deepcopy(clsid2catid)
if num_classes != 80: # 如果不是COCO数据集，而是自定义数据集
_clsid2catid = {}
for k in range(num_classes):
_clsid2catid[k] = k
_decode = Decode(predict_model, all_classes, use_gpu, cfg, for_test=False)
box_ap = eval(_decode, images, eval_pre_path, anno_file, eval_batch_size,
_clsid2catid, draw_image, draw_thresh)
Backbone = select_backbone(cfg.backbone_type)
backbone = Backbone(**cfg.backbone)
Head = select_head(cfg.head_type)
cfg.head['drop_block'] = False # 预测时关闭DropBlock，以获得一致的推理结果。
head = Head(yolo_loss=None, nms_cfg=cfg.nms_cfg, **cfg.head)
yolo = YOLO(backbone, head)
x = keras.layers.Input(shape=(None, None, 3), name='x', dtype='float32')
im_size = keras.layers.Input(shape=(2, ), name='im_size', dtype='int32')
outputs = yolo.get_outputs(x)
preds = yolo.get_prediction(outputs, im_size)
predict_model = keras.models.Model(inputs=[x, im_size], outputs=preds)
predict_model.load_weights(model_path, by_name=True, skip_mismatch=True)
predict_model.summary(line_length=130)
_decode = Decode(predict_model, all_classes, use_gpu, cfg, for_test=True)
if not os.path.exists('images/res/'): os.mkdir('images/res/')
path_dir = os.listdir('images/test')
# 读数据的线程
test_dic = {}
thr = threading.Thread(target=read_test_data,
args=(path_dir, _decode, test_dic))
thr.start()
key_list = list(test_dic.keys())
key_len = len(key_list)
while key_len == 0:
time.sleep(0.01)
key_list = list(test_dic.keys())
def job1():
global count1
global frame
use_gpu = True
# 显存分配。
if use_gpu:
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
else:
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
from keras.backend.tensorflow_backend import set_session
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.8
set_session(tf.Session(config=config))
facesDetect = {'classes_path': 'data/voc_classes.txt', 'model_path': './weights/best_model.h5'}
emotion_model_path = 'model/fer2013_mini_XCEPTION.102-0.66.hdf5'
emotion_labels = get_labels('fer2013')
# hyper-parameters for bounding boxes shape
frame_window = 10
emotion_offsets = (20, 40)
# loading models
face_detector = Decode(facesDetect['classes_path'], facesDetect['model_path'])
emotion_classifier = load_model(emotion_model_path, compile=False)
# getting input model shapes for inference
emotion_target_size = emotion_classifier.input_shape[1:3]
emotion_window1 = []
record1 = {'angry':[0], 'disgust':[0], 'fear':[0], 'happy':[0], 'sad':[0], 'surprise':[0], 'neutral':[0]}
#record_diff = {'angry':[], 'disgust':[], 'fear':[], 'happy':[], 'sad':[], 'surprise':[], 'neutral':[]}'''
emo_record1 = []
capture = cv2.VideoCapture(0)
while True:
if True:
bgr_image = capture.read()[1]
gray_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2GRAY)
rgb_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB)
faces = face_detector.detect_image(bgr_image)[1]
if faces is None:
faces = ()
# print(faces)
for face_coordinates in faces:
x1, y1, x2, y2 = face_coordinates
face_coordinates = [int(x1), int(y1), int(x2-x1), int(y2-y1)]
x1, x2, y1, y2 = apply_offsets(face_coordinates, emotion_offsets)
gray_face = gray_image[y1:y2, x1:x2]
try:
gray_face = cv2.resize(gray_face, (emotion_target_size))
except:
continue
gray_face = preprocess_input(gray_face, True)
gray_face = np.expand_dims(gray_face, 0)
gray_face = np.expand_dims(gray_face, -1)
emotion_prediction = emotion_classifier.predict(gray_face)
###############移動平均公式################
'''for idx, probability in enumerate(emotion_prediction[0]):
alpha = 0.5
record[emotion_labels[idx]].append(record[emotion_labels[idx]][-1] + alpha * (round(probability*100, 2)-record[emotion_labels[idx]][-1]))
emotion_prediction[0][idx] = record[emotion_labels[idx]][-1]
if len(record[emotion_labels[idx]])>10:
record[emotion_labels[idx]].pop(0)
#print(record)
#print()'''
#########################################
#################自創權重##############
emotion_prediction[0] = weights_change(emo_record1, emotion_prediction[0])
data=[]
for idx, probability in enumerate(emotion_prediction[0]):
data.append((emotion_labels[idx], probability))
rd1.append(data)
count1+=1
emo_record1.append(np.argmax(emotion_prediction))
if len(emo_record1)>10:
emo_record1.pop(0)
#######################################
emotion_probability = np.max(emotion_prediction)
emotion_label_arg = np.argmax(emotion_prediction)
emotion_text = emotion_labels[emotion_label_arg]
emotion_window1.append(emotion_text)
if len(emotion_window1) > frame_window:
emotion_window1.pop(0)
try:
emotion_mode1 = mode(emotion_window1)
except:
continue
if emotion_text == 'angry':
color = emotion_probability * np.asarray((255, 0, 0))
elif emotion_text == 'sad':
color = emotion_probability * np.asarray((0, 0, 255))
elif emotion_text == 'happy':
color = emotion_probability * np.asarray((255, 255, 0))
elif emotion_text == 'surprise':
color = emotion_probability * np.asarray((0, 255, 255))
else:
color = emotion_probability * np.asarray((0, 255, 0))
color = color.astype(int)
color = color.tolist()
draw_bounding_box(face_coordinates, rgb_image, color)
draw_text(face_coordinates, rgb_image, emotion_mode1,
color, 0, -45, 1, 1)
bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)
cv2.imshow('window_frame1', bgr_image)
if cv2.waitKey(1) & 0xFF == 27:
break
cv2.destroyAllWindows()
output_l, output_m, output_s = YOLOv4(inputs,
num_classes,
num_anchors,
is_test=False,
trainable=True)
eval_fetch_list = [output_l, output_m, output_s]
eval_prog = eval_prog.clone(for_test=True)
# 参数随机初始化
gpu_id = int(os.environ.get('FLAGS_selected_gpus', 0))
place = fluid.CUDAPlace(gpu_id) if use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup_prog)
compiled_eval_prog = fluid.compiler.CompiledProgram(eval_prog)
_decode = Decode(cfg.conf_thresh, cfg.nms_thresh, cfg.input_shape, exe,
compiled_eval_prog, class_names)
if cfg.pattern == 1:
fluid.load(train_prog, cfg.model_path, executor=exe)
strs = cfg.model_path.split('weights/')
if len(strs) == 2:
iter_id = int(strs[1])
# 种类id
_catid2clsid = copy.deepcopy(catid2clsid)
_clsid2catid = copy.deepcopy(clsid2catid)
if num_classes != 80: # 如果不是COCO数据集，而是自定义数据集
_catid2clsid = {}
_clsid2catid = {}
for k in range(num_classes):
_catid2clsid[k] = k
if __name__ == '__main__':
classes_path = 'data/coco_classes.txt'
# model_path可以是'yolov4.h5'、'./weights/step00001000.h5'这些。
model_path = 'yolov4.h5'
# model_path = './weights/step00001000.h5'
# input_shape越大，精度会上升，但速度会下降。
# input_shape = (320, 320)
# input_shape = (416, 416)
input_shape = (608, 608)
# 验证集图片的相对路径
eval_pre_path = '../COCO/val2017/'
anno_file = '../COCO/annotations/instances_val2017.json'
with open(anno_file, 'r', encoding='utf-8') as f2:
for line in f2:
line = line.strip()
dataset = json.loads(line)
images = dataset['images']
num_anchors = 3
all_classes = get_classes(classes_path)
num_classes = len(all_classes)
inputs = layers.Input(shape=(None, None, 3))
yolo = YOLOv4(inputs, num_classes, num_anchors)
yolo.load_weights(model_path, by_name=True)
_decode = Decode(0.05, 0.45, input_shape, yolo, all_classes)
box_ap = eval(_decode, images, eval_pre_path, anno_file)
num_filters, None, None),
name='target2',
dtype='float32')
targets = [target0_tensor, target1_tensor, target2_tensor]
else:
targets = [target0_tensor, target1_tensor]
loss_list = keras.layers.Lambda(yolo.get_loss,
name='yolo_loss',
arguments={
'target_num': target_num,
})([*outputs, gt_bbox_tensor, *targets])
train_model = keras.models.Model(inputs=[x, gt_bbox_tensor, *targets],
outputs=loss_list)
loss_n = len(loss_list)
_decode = Decode(predict_model, class_names, use_gpu, cfg, for_test=False)
# 加载权重
if cfg.train_cfg['model_path'] is not None:
# 加载参数, 跳过形状不匹配的。
train_model.load_weights(cfg.train_cfg['model_path'],
by_name=True,
skip_mismatch=True)
strs = cfg.train_cfg['model_path'].split('step')
if len(strs) == 2:
iter_id = int(strs[1][:8])
# 冻结，使得需要的显存减少。低显存的卡建议这样配置。
backbone.freeze()
append_batch_size=False,
dtype='float32')
output_l, output_m, output_s = YOLOv4(inputs,
num_classes,
num_anchors,
is_test=False,
trainable=True)
eval_fetch_list = [output_l, output_m, output_s]
eval_prog = eval_prog.clone(for_test=True)
gpu_id = int(os.environ.get('FLAGS_selected_gpus', 0))
place = fluid.CUDAPlace(gpu_id) if use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup_prog)
fluid.load(eval_prog, model_path, executor=exe)
_decode = Decode(conf_thresh, nms_thresh, input_shape, exe, eval_prog,
all_classes)
if not os.path.exists('images/res/'): os.mkdir('images/res/')
path_dir = os.listdir('images/test')
# warm up
if use_gpu:
for k, filename in enumerate(path_dir):
image = cv2.imread('images/test/' + filename)
image, boxes, scores, classes = _decode.detect_image(
image, eval_fetch_list, draw_image=False)
if k == 10:
break
time_stat = deque(maxlen=20)
start_time = time.time()
backbone = Resnet50Vd()
head = YOLOv3Head(
keep_prob=1.0) # 一定要设置keep_prob=1.0, 为了得到一致的推理结果
yolov3 = YOLOv3(backbone, head)
output_l, output_m, output_s = yolov3(inputs)
eval_fetch_list = [output_l, output_m, output_s]
eval_prog = eval_prog.clone(for_test=True)
# 参数随机初始化
gpu_id = int(os.environ.get('FLAGS_selected_gpus', 0))
place = fluid.CUDAPlace(gpu_id) if use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup_prog)
compiled_eval_prog = fluid.compiler.CompiledProgram(eval_prog)
_decode = Decode(algorithm, cfg.anchors, cfg.conf_thresh, cfg.nms_thresh,
cfg.input_shape, exe, compiled_eval_prog, class_names)
if cfg.pattern == 1:
fluid.load(train_prog, cfg.model_path, executor=exe)
strs = cfg.model_path.split('weights/')
if len(strs) == 2:
iter_id = int(strs[1])
# 种类id
_catid2clsid = copy.deepcopy(catid2clsid)
_clsid2catid = copy.deepcopy(clsid2catid)
if num_classes != 80: # 如果不是COCO数据集，而是自定义数据集
_catid2clsid = {}
_clsid2catid = {}
for k in range(num_classes):
_catid2clsid[k] = k
all_classes = get_classes(classes_path)
num_classes = len(all_classes)
startup_prog = fluid.Program()
eval_prog = fluid.Program()
with fluid.program_guard(eval_prog, startup_prog):
with fluid.unique_name.guard():
# 多尺度训练
inputs = P.data(name='input_1',
shape=[-1, 3, -1, -1],
append_batch_size=False,
dtype='float32')
output_l, output_m, output_s = YOLOv4(inputs,
num_classes,
num_anchors,
is_test=False,
trainable=True)
eval_fetch_list = [output_l, output_m, output_s]
eval_prog = eval_prog.clone(for_test=True)
gpu_id = int(os.environ.get('FLAGS_selected_gpus', 0))
place = fluid.CUDAPlace(gpu_id) if use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup_prog)
fluid.load(eval_prog, model_path, executor=exe)
_decode = Decode(conf_thresh, nms_thresh, input_shape, exe, eval_prog,
all_classes)
test_dev(_decode, eval_fetch_list, images, test_pre_path, test_batch_size,
draw_image)
# 步id，无需设置，会自动读。
iter_id = 0
# 验证
# input_shape越大，精度会上升，但速度会下降。
# input_shape = (320, 320)
# input_shape = (416, 416)
input_shape = (608, 608)
# 验证时的分数阈值和nms_iou阈值
conf_thresh = 0.05
nms_thresh = 0.45
# 多尺度训练
inputs = layers.Input(shape=(None, None, 3))
model_body = YOLOv4(inputs, num_classes, num_anchors)
_decode = Decode(conf_thresh, nms_thresh, input_shape, model_body,
class_names)
# 模式。 0-从头训练，1-读取之前的模型继续训练（model_path可以是'yolov4.h5'、'./weights/step00001000.h5'这些。）
pattern = 1
max_bbox_per_scale = 150
iou_loss_thresh = 0.7
if pattern == 1:
lr = 0.0001
batch_size = 8
model_path = 'yolov4.h5'
# model_path = './weights/step00001000.h5'
model_body.load_weights(model_path, by_name=True)
strs = model_path.split('step')
if len(strs) == 2:
iter_id = int(strs[1][:8])
iou_loss = IouLoss(**cfg.iou_loss)
iou_aware_loss = None
if cfg.head['iou_aware']:
IouAwareLoss = select_loss(cfg.iou_aware_loss_type)
iou_aware_loss = IouAwareLoss(**cfg.iou_aware_loss)
Loss = select_loss(cfg.yolo_loss_type)
yolo_loss = Loss(iou_loss=iou_loss,
iou_aware_loss=iou_aware_loss,
**cfg.yolo_loss)
Head = select_head(cfg.head_type)
head = Head(yolo_loss=yolo_loss,
is_train=True,
nms_cfg=cfg.nms_cfg,
**cfg.head)
ppyolo = PPYOLO(backbone, head)
_decode = Decode(ppyolo, class_names, use_gpu, cfg, for_test=False)
# 加载权重
if cfg.train_cfg['model_path'] is not None:
# 加载参数, 跳过形状不匹配的。
load_weights(ppyolo, cfg.train_cfg['model_path'])
strs = cfg.train_cfg['model_path'].split('step')
if len(strs) == 2:
iter_id = int(strs[1][:8])
# 冻结，使得需要的显存减少。低显存的卡建议这样配置。
backbone.freeze()
if use_gpu: # 如果有gpu可用，模型（包括了权重weight）存放在gpu显存里
ppyolo = ppyolo.cuda()
continue
img_anno = val_dataset.loadImgs(img_id)[0]
images.append(img_anno)
all_classes = get_classes(cfg.classes_path)
num_classes = len(all_classes)
# 创建模型
Backbone = select_backbone(cfg.backbone_type)
backbone = Backbone(**cfg.backbone)
Fpn = select_fpn(cfg.fpn_type)
fpn = Fpn(**cfg.fpn)
Head = select_head(cfg.head_type)
head = Head(fcos_loss=None, nms_cfg=cfg.nms_cfg, **cfg.head)
fcos = FCOS(backbone, fpn, head)
if use_gpu:
fcos = fcos.cuda()
fcos.load_state_dict(torch.load(model_path))
fcos.eval(
) # 必须调用model.eval()来设置dropout和batch normalization layers在运行推理前，切换到评估模式。
_clsid2catid = copy.deepcopy(clsid2catid)
if num_classes != 80: # 如果不是COCO数据集，而是自定义数据集
_clsid2catid = {}
for k in range(num_classes):
_clsid2catid[k] = k
_decode = Decode(fcos, all_classes, use_gpu, cfg, for_test=False)
box_ap = eval(_decode, images, eval_pre_path, anno_file, eval_batch_size,
_clsid2catid, draw_image, draw_thresh)
# 是否给图片画框。不画可以提速。读图片、后处理还可以继续优化。
draw_image = True
# draw_image = False
num_anchors = 3
all_classes = get_classes(classes_path)
num_classes = len(all_classes)
yolo = YOLOv4(num_classes, num_anchors)
if torch.cuda.is_available(): # 如果有gpu可用，模型（包括了权重weight）存放在gpu显存里
yolo = yolo.cuda()
yolo.load_state_dict(torch.load(model_path))
yolo.eval(
) # 必须调用model.eval()来设置dropout和batch normalization layers在运行推理前，切换到评估模式. 不这样做的化会产生不一致的推理结果.
_decode = Decode(conf_thresh, nms_thresh, input_shape, yolo, all_classes)
if not os.path.exists('images/res/'): os.mkdir('images/res/')
path_dir = os.listdir('images/test')
# warm up
if use_gpu:
for k, filename in enumerate(path_dir):
image = cv2.imread('images/test/' + filename)
image, boxes, scores, classes = _decode.detect_image(
image, draw_image=False)
if k == 10:
break
time_stat = deque(maxlen=20)
start_time = time.time()
class face_detect():
def __init__(self):
# hyper-parameters for bounding boxes shape
self.frame_window = 10
self.emotion_offsets = (20, 40)
# loading models
self.face_detector = Decode('data/voc_classes.txt',
'./weights/best_model.h5')
self.emotion_classifier = load_model(
'model/fer2013_mini_XCEPTION.102-0.66.hdf5', compile=False)
self.emotion_labels = get_labels('fer2013')
# getting input model shapes for inference
self.emotion_target_size = self.emotion_classifier.input_shape[1:3]
# starting lists for calculating modes
self.emotion_window = []
def faceDetect(self, bgr_image):
faces = self.face_detector.detect_image(bgr_image)[1]
probability = [0, 0, 0, 0, 0, 0, 0]
if faces is not None and len(faces) == 1:
bgr_image, probability = self.emo_Detect(bgr_image, faces[0])
return bgr_image, probability
def emo_Detect(self, bgr_image, face_coordinates):
gray_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2GRAY)
x1, y1, x2, y2 = face_coordinates
face_coordinates = [int(x1), int(y1), int(x2 - x1), int(y2 - y1)]
x1, x2, y1, y2 = apply_offsets(face_coordinates, self.emotion_offsets)
gray_face = gray_image[y1:y2, x1:x2]
gray_face = cv2.resize(gray_face, (self.emotion_target_size))
gray_face = preprocess_input(gray_face, True)
gray_face = np.expand_dims(gray_face, 0)
gray_face = np.expand_dims(gray_face, -1)
emotion_prediction = self.emotion_classifier.predict(gray_face)
for idx, probability in enumerate(emotion_prediction[0]):
print(self.emotion_labels[idx], probability)
emotion_probability = np.max(emotion_prediction)
emotion_label_arg = np.argmax(emotion_prediction)
emotion_text = self.emotion_labels[emotion_label_arg]
self.emotion_window.append(emotion_text)
if len(self.emotion_window) > self.frame_window:
self.emotion_window.pop(0)
try:
emotion_mode = mode(self.emotion_window)
print("mode is " + emotion_mode)
except:
pass
if emotion_text == 'angry':
color = emotion_probability * np.asarray((0, 0, 255))
elif emotion_text == 'sad':
color = emotion_probability * np.asarray((255, 0, 0))
elif emotion_text == 'happy':
color = emotion_probability * np.asarray((0, 255, 255))
elif emotion_text == 'surprise':
color = emotion_probability * np.asarray((255, 255, 0))
else:
color = emotion_probability * np.asarray((0, 255, 0))
color = color.astype(int)
color = color.tolist()
draw_bounding_box(face_coordinates, bgr_image, color)
draw_text(face_coordinates, bgr_image, emotion_text, color, 0, -45, 1,
1)
return bgr_image, emotion_prediction[0]
import cv2
import os
import time
from model.decode_np import Decode
if __name__ == '__main__':
file = 'data/coco_classes.txt'
model_path = 'yolo_bgr_mAP_46.h5'
# input_shape越大，精度会上升，但速度会下降。
# input_shape = (320, 320)
input_shape = (416, 416)
# input_shape = (608, 608)
_decode = Decode(0.6, 0.5, input_shape, model_path, file)
# detect images in test floder.
for (root, dirs, files) in os.walk('images/test'):
if files:
start = time.time()
for f in files:
# print(f)
path = os.path.join(root, f)
image = cv2.imread(path)
image = _decode.detect_image(image)
cv2.imwrite('images/res/' + f, image)
print('total time: {0:.6f}s'.format(time.time() - start))
# detect videos one at a time in videos/test folder
# video = 'library1.mp4'
cfg = TrainConfig()
class_names = get_classes(cfg.classes_path)
num_classes = len(class_names)
_anchors = copy.deepcopy(cfg.anchors)
num_anchors = len(cfg.anchor_masks[0]) # 每个输出层有几个先验框
_anchors = np.array(_anchors)
_anchors = np.reshape(_anchors, (-1, num_anchors, 2))
_anchors = _anchors.astype(np.float32)
# 步id，无需设置，会自动读。
iter_id = 0
# 创建模型
yolo = YOLOv4(num_classes, num_anchors)
_decode = Decode(cfg.conf_thresh, cfg.nms_thresh, cfg.input_shape, yolo,
class_names)
# 模式。 0-从头训练，1-读取之前的模型继续训练（model_path可以是'yolov4.h5'、'./weights/step00001000.h5'这些。）
pattern = cfg.pattern
if pattern == 1:
# 加载参数, 跳过形状不匹配的。
yolo_state_dict = yolo.state_dict()
pretrained_dict = torch.load(cfg.model_path)
new_state_dict = OrderedDict()
for k, v in pretrained_dict.items():
if k in yolo_state_dict:
shape_1 = yolo_state_dict[k].shape
shape_2 = pretrained_dict[k].shape
if shape_1 == shape_2:
new_state_dict[k] = v
else:
