class FRCNN(object):
_defaults = {
"model_path":
'/home/gxt/study/faster-rcnn-keras-master/logs/Epoch100-Total_Loss0.8886-Val_Loss1.0822.h5',
"classes_path": 'model_data/voc_classes.txt',
"confidence": 0.5,
"iou": 0.3
}
@classmethod
def get_defaults(cls, n):
if n in cls._defaults:
return cls._defaults[n]
else:
return "Unrecognized attribute name '" + n + "'"
#---------------------------------------------------#
# Initialize fast RCNN
#---------------------------------------------------#
def __init__(self, **kwargs):
self.__dict__.update(self._defaults)
self.class_names = self._get_class()
self.sess = K.get_session()
self.config = Config()
self.generate()
self.bbox_util = BBoxUtility(classifier_nms=self.iou,
top_k=self.config.num_RPN_predict_pre)
#---------------------------------------------------#
# Get all categories
#---------------------------------------------------#
def _get_class(self):
classes_path = os.path.expanduser(self.classes_path)
with open(classes_path) as f:
class_names = f.readlines()
class_names = [c.strip() for c in class_names]
return class_names
#---------------------------------------------------#
# Load model
#---------------------------------------------------#
def generate(self):
model_path = os.path.expanduser(self.model_path)
assert model_path.endswith(
'.h5'), 'Keras model or weights must be a .h5 file.'
#-------------------------------#
# Calculate the total number of classes
#-------------------------------#
self.num_classes = len(self.class_names) + 1
#-------------------------------#
# Loading model and weight
#-------------------------------#
self.model_rpn, self.model_classifier = frcnn.get_predict_model(
self.config, self.num_classes)
self.model_rpn.load_weights(self.model_path, by_name=True)
self.model_classifier.load_weights(self.model_path, by_name=True)
print('{} model, anchors, and classes loaded.'.format(model_path))
# Set different colors for the frame
hsv_tuples = [(x / len(self.class_names), 1., 1.)
for x in range(len(self.class_names))]
self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
self.colors = list(
map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),
self.colors))
#---------------------------------------------------#
# Used to calculate the size of shared feature layers
#---------------------------------------------------#
def get_img_output_length(self, width, height):
def get_output_length(input_length):
filter_sizes = [7, 3, 1, 1]
padding = [3, 1, 0, 0]
stride = 2
for i in range(4):
# input_length = (input_length - filter_size + stride) // stride
input_length = (input_length + 2 * padding[i] -
filter_sizes[i]) // stride + 1
return input_length
return get_output_length(width), get_output_length(height)
#---------------------------------------------------#
# Detect images
#---------------------------------------------------#
def detect_image(self, image):
image_shape = np.array(np.shape(image)[0:2])
old_width, old_height = image_shape[1], image_shape[0]
old_image = copy.deepcopy(image)
#---------------------------------------------------------#
# Reset the original image to the size of 600 short edges
#---------------------------------------------------------#
width, height = get_new_img_size(old_width, old_height)
image = image.resize([width, height], Image.BICUBIC)
photo = np.array(image, dtype=np.float64)
#-----------------------------------------------------------#
# Image preprocessing and normalization.
#-----------------------------------------------------------#
photo = preprocess_input(np.expand_dims(photo, 0))
rpn_pred = self.model_rpn.predict(photo)
#-----------------------------------------------------------#
# The prediction result of the suggestion box network is decoded
#-----------------------------------------------------------#
base_feature_width, base_feature_height = self.get_img_output_length(
width, height)
anchors = get_anchors([base_feature_width, base_feature_height], width,
height)
rpn_results = self.bbox_util.detection_out_rpn(rpn_pred, anchors)
#-------------------------------------------------------------#
# After obtaining the suggestion box and the shared feature layer, they are passed into the classifier for prediction
#-------------------------------------------------------------#
base_layer = rpn_pred[2]
proposal_box = np.array(rpn_results)[:, :, 1:]
temp_ROIs = np.zeros_like(proposal_box)
temp_ROIs[:, :, [0, 1, 2, 3]] = proposal_box[:, :, [1, 0, 3, 2]]
classifier_pred = self.model_classifier.predict(
[base_layer, temp_ROIs])
#-------------------------------------------------------------#
# The prediction frame is obtained by decoding the suggestion box by using the prediction results of classifier
#-------------------------------------------------------------#
results = self.bbox_util.detection_out_classifier(
classifier_pred, proposal_box, self.config, self.confidence)
if len(results[0]) == 0:
return old_image
results = np.array(results[0])
boxes = results[:, :4]
top_conf = results[:, 4]
top_label_indices = results[:, 5]
boxes[:, [0, 2]] = boxes[:, [0, 2]] * old_width
boxes[:, [1, 3]] = boxes[:, [1, 3]] * old_height
font = ImageFont.truetype(font='model_data/simhei.ttf',
size=np.floor(3e-2 * np.shape(image)[1] +
0.5).astype('int32'))
thickness = max(
(np.shape(old_image)[0] + np.shape(old_image)[1]) // old_width * 2,
1)
image = old_image
for i, c in enumerate(top_label_indices):
predicted_class = self.class_names[int(c)]
score = top_conf[i]
left, top, right, bottom = boxes[i]
top = top - 5
left = left - 5
bottom = bottom + 5
right = right + 5
top = max(0, np.floor(top + 0.5).astype('int32'))
left = max(0, np.floor(left + 0.5).astype('int32'))
bottom = min(
np.shape(image)[0],
np.floor(bottom + 0.5).astype('int32'))
right = min(
np.shape(image)[1],
np.floor(right + 0.5).astype('int32'))
# 画框框
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
label = label.encode('utf-8')
print(label, top, left, bottom, right)
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
for i in range(thickness):
draw.rectangle([left + i, top + i, right - i, bottom - i],
outline=self.colors[int(c)])
draw.rectangle(
[tuple(text_origin),
tuple(text_origin + label_size)],
fill=self.colors[int(c)])
draw.text(text_origin,
str(label, 'UTF-8'),
fill=(0, 0, 0),
font=font)
del draw
return image
def close_session(self):
self.sess.close()
class FRCNN(object):
_defaults = {
"model_path": 'model_data/voc_weights.h5',
"classes_path": 'model_data/voc_classes.txt',
"confidence": 0.5,
"iou": 0.3
}
@classmethod
def get_defaults(cls, n):
if n in cls._defaults:
return cls._defaults[n]
else:
return "Unrecognized attribute name '" + n + "'"
#---------------------------------------------------#
# 初始化faster RCNN
#---------------------------------------------------#
def __init__(self, **kwargs):
self.__dict__.update(self._defaults)
self.class_names = self._get_class()
self.config = Config()
self.generate()
self.bbox_util = BBoxUtility()
#---------------------------------------------------#
# 获得所有的分类
#---------------------------------------------------#
def _get_class(self):
classes_path = os.path.expanduser(self.classes_path)
with open(classes_path) as f:
class_names = f.readlines()
class_names = [c.strip() for c in class_names]
return class_names
#---------------------------------------------------#
# 获得所有的分类
#---------------------------------------------------#
def generate(self):
model_path = os.path.expanduser(self.model_path)
assert model_path.endswith(
'.h5'), 'Keras model or weights must be a .h5 file.'
#-------------------------------#
# 计算总的类的数量
#-------------------------------#
self.num_classes = len(self.class_names) + 1
#-------------------------------#
# 载入模型与权值
#-------------------------------#
self.model_rpn, self.model_classifier = frcnn.get_predict_model(
self.config, self.num_classes)
self.model_rpn.load_weights(self.model_path, by_name=True)
self.model_classifier.load_weights(self.model_path, by_name=True)
print('{} model, anchors, and classes loaded.'.format(model_path))
# 画框设置不同的颜色
hsv_tuples = [(x / len(self.class_names), 1., 1.)
for x in range(len(self.class_names))]
self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
self.colors = list(
map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),
self.colors))
#---------------------------------------------------#
# 用于计算共享特征层的大小
#---------------------------------------------------#
def get_img_output_length(self, width, height):
def get_output_length(input_length):
# input_length += 6
filter_sizes = [7, 3, 1, 1]
padding = [3, 1, 0, 0]
stride = 2
for i in range(4):
# input_length = (input_length - filter_size + stride) // stride
input_length = (input_length + 2 * padding[i] -
filter_sizes[i]) // stride + 1
return input_length
return get_output_length(width), get_output_length(height)
@tf.function(experimental_relax_shapes=True)
def model_rpn_get_pred(self, photo):
preds = self.model_rpn(photo, training=False)
return preds
@tf.function(experimental_relax_shapes=True)
def model_classifier_get_pred(self, photo):
preds = self.model_classifier(photo, training=False)
return preds
#---------------------------------------------------#
# 检测图片
#---------------------------------------------------#
def detect_image(self, image):
#-------------------------------------#
# 转换成RGB图片，可以用于灰度图预测。
#-------------------------------------#
image = image.convert("RGB")
image_shape = np.array(np.shape(image)[0:2])
old_width, old_height = image_shape[1], image_shape[0]
old_image = copy.deepcopy(image)
#---------------------------------------------------------#
# 给原图像进行resize，resize到短边为600的大小上
#---------------------------------------------------------#
width, height = get_new_img_size(old_width, old_height)
image = image.resize([width, height], Image.BICUBIC)
photo = np.array(image, dtype=np.float64)
#-----------------------------------------------------------#
# 图片预处理，归一化。
#-----------------------------------------------------------#
photo = preprocess_input(np.expand_dims(photo, 0))
rpn_pred = self.model_rpn_get_pred(photo)
rpn_pred = [x.numpy() for x in rpn_pred]
#-----------------------------------------------------------#
# 将建议框网络的预测结果进行解码
#-----------------------------------------------------------#
base_feature_width, base_feature_height = self.get_img_output_length(
width, height)
anchors = get_anchors([base_feature_width, base_feature_height], width,
height)
rpn_results = self.bbox_util.detection_out_rpn(rpn_pred, anchors)
#-------------------------------------------------------------#
# 在获得建议框和共享特征层后，将二者传入classifier中进行预测
#-------------------------------------------------------------#
base_layer = rpn_pred[2]
proposal_box = np.array(rpn_results)[:, :, 1:]
temp_ROIs = np.zeros_like(proposal_box)
temp_ROIs[:, :, [0, 1, 2, 3]] = proposal_box[:, :, [1, 0, 3, 2]]
classifier_pred = self.model_classifier_get_pred(
[base_layer, temp_ROIs])
classifier_pred = [x.numpy() for x in classifier_pred]
#-------------------------------------------------------------#
# 利用classifier的预测结果对建议框进行解码，获得预测框
#-------------------------------------------------------------#
results = self.bbox_util.detection_out_classifier(
classifier_pred, proposal_box, self.config, self.confidence)
if len(results[0]) == 0:
return old_image
results = np.array(results[0])
boxes = results[:, :4]
top_conf = results[:, 4]
top_label_indices = results[:, 5]
boxes[:, [0, 2]] = boxes[:, [0, 2]] * old_width
boxes[:, [1, 3]] = boxes[:, [1, 3]] * old_height
font = ImageFont.truetype(font='model_data/simhei.ttf',
size=np.floor(3e-2 * np.shape(image)[1] +
0.5).astype('int32'))
thickness = max(
(np.shape(old_image)[0] + np.shape(old_image)[1]) // old_width * 2,
1)
image = old_image
for i, c in enumerate(top_label_indices):
predicted_class = self.class_names[int(c)]
score = top_conf[i]
left, top, right, bottom = boxes[i]
top = top - 5
left = left - 5
bottom = bottom + 5
right = right + 5
top = max(0, np.floor(top + 0.5).astype('int32'))
left = max(0, np.floor(left + 0.5).astype('int32'))
bottom = min(
np.shape(image)[0],
np.floor(bottom + 0.5).astype('int32'))
right = min(
np.shape(image)[1],
np.floor(right + 0.5).astype('int32'))
# 画框框
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
label = label.encode('utf-8')
print(label, top, left, bottom, right)
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
for i in range(thickness):
draw.rectangle([left + i, top + i, right - i, bottom - i],
outline=self.colors[int(c)])
draw.rectangle(
[tuple(text_origin),
tuple(text_origin + label_size)],
fill=self.colors[int(c)])
draw.text(text_origin,
str(label, 'UTF-8'),
fill=(0, 0, 0),
font=font)
del draw
return image
