def __init__(self, **kwargs):
self.__dict__.update(self._defaults)
for name, value in kwargs.items():
setattr(self, name, value)
#---------------------------------------------------#
# 计算总的类的数量
#---------------------------------------------------#
self.class_names, self.num_classes = get_classes(self.classes_path)
self.anchors = torch.from_numpy(
get_anchors(self.input_shape, self.anchors_size,
self.backbone)).type(torch.FloatTensor)
if self.cuda:
self.anchors = self.anchors.cuda()
self.num_classes = self.num_classes + 1
#---------------------------------------------------#
# 画框设置不同的颜色
#---------------------------------------------------#
hsv_tuples = [(x / self.num_classes, 1., 1.)
for x in range(self.num_classes)]
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))
self.bbox_util = BBoxUtility(self.num_classes)
self.generate()
show_config(**self._defaults)
def __init__(self, **kwargs):
self.__dict__.update(self._defaults)
for name, value in kwargs.items():
setattr(self, name, value)
#---------------------------------------------------#
# 获得种类和先验框的数量
#---------------------------------------------------#
self.class_names, self.num_classes = get_classes(self.classes_path)
self.num_classes = self.num_classes + 1
#---------------------------------------------------#
# 创建一个工具箱，用于进行解码
# 最大使用min_k个建议框，默认为150
#---------------------------------------------------#
self.bbox_util = BBoxUtility(self.num_classes,
nms_iou=self.nms_iou,
min_k=150)
#---------------------------------------------------#
# 画框设置不同的颜色
#---------------------------------------------------#
hsv_tuples = [(x / self.num_classes, 1., 1.)
for x in range(self.num_classes)]
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))
self.generate()
def __init__(self, **kwargs):
self.__dict__.update(self._defaults)
for name, value in kwargs.items():
setattr(self, name, value)
#---------------------------------------------------#
# 计算总的类的数量
#---------------------------------------------------#
self.class_names, self.num_classes = get_classes(self.classes_path)
#---------------------------------------------------#
# 画框设置不同的颜色
#---------------------------------------------------#
hsv_tuples = [(x / self.num_classes, 1., 1.) for x in range(self.num_classes)]
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))
self.bbox_util = BBoxUtility(nms_thresh=self.nms_iou)
self.generate()
class SSD(object):
_defaults = {
#--------------------------------------------------------------------------#
# 使用自己训练好的模型进行预测一定要修改model_path和classes_path！
# model_path指向logs文件夹下的权值文件，classes_path指向model_data下的txt
#
# 训练好后logs文件夹下存在多个权值文件，选择验证集损失较低的即可。
# 验证集损失较低不代表mAP较高，仅代表该权值在验证集上泛化性能较好。
# 如果出现shape不匹配，同时要注意训练时的model_path和classes_path参数的修改
#--------------------------------------------------------------------------#
"model_path" : 'model_data/ssd_weights.h5',
"classes_path" : 'model_data/voc_classes.txt',
#---------------------------------------------------------------------#
# 用于预测的图像大小，和train时使用同一个即可
#---------------------------------------------------------------------#
"input_shape" : [300, 300],
#---------------------------------------------------------------------#
# 只有得分大于置信度的预测框会被保留下来
#---------------------------------------------------------------------#
"confidence" : 0.5,
#---------------------------------------------------------------------#
# 非极大抑制所用到的nms_iou大小
#---------------------------------------------------------------------#
"nms_iou" : 0.45,
#---------------------------------------------------------------------#
# 用于指定先验框的大小
#---------------------------------------------------------------------#
'anchors_size' : [30, 60, 111, 162, 213, 264, 315],
#---------------------------------------------------------------------#
# 该变量用于控制是否使用letterbox_image对输入图像进行不失真的resize，
# 在多次测试后，发现关闭letterbox_image直接resize的效果更好
#---------------------------------------------------------------------#
"letterbox_image" : False,
}
@classmethod
def get_defaults(cls, n):
if n in cls._defaults:
return cls._defaults[n]
else:
return "Unrecognized attribute name '" + n + "'"
#---------------------------------------------------#
# 初始化ssd
#---------------------------------------------------#
def __init__(self, **kwargs):
self.__dict__.update(self._defaults)
for name, value in kwargs.items():
setattr(self, name, value)
#---------------------------------------------------#
# 计算总的类的数量
#---------------------------------------------------#
self.class_names, self.num_classes = get_classes(self.classes_path)
self.anchors = get_anchors(self.input_shape, self.anchors_size)
self.num_classes = self.num_classes + 1
#---------------------------------------------------#
# 画框设置不同的颜色
#---------------------------------------------------#
hsv_tuples = [(x / self.num_classes, 1., 1.) for x in range(self.num_classes)]
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))
self.bbox_util = BBoxUtility(self.num_classes, nms_thresh=self.nms_iou)
self.generate()
show_config(**self._defaults)
#---------------------------------------------------#
# 载入模型
#---------------------------------------------------#
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.'
#-------------------------------#
# 载入模型与权值
#-------------------------------#
self.ssd = SSD300([self.input_shape[0], self.input_shape[1], 3], self.num_classes)
self.ssd.load_weights(self.model_path, by_name=True)
print('{} model, anchors, and classes loaded.'.format(model_path))
@tf.function
def get_pred(self, photo):
preds = self.ssd(photo, training=False)
return preds
#---------------------------------------------------#
# 检测图片
#---------------------------------------------------#
def detect_image(self, image, crop = False, count = False):
image_shape = np.array(np.shape(image)[0:2])
#---------------------------------------------------------#
# 在这里将图像转换成RGB图像，防止灰度图在预测时报错。
# 代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
#---------------------------------------------------------#
image = cvtColor(image)
#---------------------------------------------------------#
# 给图像增加灰条，实现不失真的resize
# 也可以直接resize进行识别
#---------------------------------------------------------#
image_data = resize_image(image, (self.input_shape[1], self.input_shape[0]), self.letterbox_image)
#---------------------------------------------------------#
# 添加上batch_size维度，图片预处理，归一化。
#---------------------------------------------------------#
image_data = preprocess_input(np.expand_dims(np.array(image_data, dtype='float32'), 0))
preds = self.get_pred(image_data).numpy()
#-----------------------------------------------------------#
# 将预测结果进行解码
#-----------------------------------------------------------#
results = self.bbox_util.decode_box(preds, self.anchors, image_shape,
self.input_shape, self.letterbox_image, confidence=self.confidence)
#--------------------------------------#
# 如果没有检测到物体，则返回原图
#--------------------------------------#
if len(results[0])<=0:
return image
top_label = np.array(results[0][:, 4], dtype = 'int32')
top_conf = results[0][:, 5]
top_boxes = results[0][:, :4]
#---------------------------------------------------------#
# 设置字体与边框厚度
#---------------------------------------------------------#
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(image)[0] + np.shape(image)[1]) // self.input_shape[0], 1)
#---------------------------------------------------------#
# 计数
#---------------------------------------------------------#
if count:
print("top_label:", top_label)
classes_nums = np.zeros([self.num_classes])
for i in range(self.num_classes):
num = np.sum(top_label == i)
if num > 0:
print(self.class_names[i], " : ", num)
classes_nums[i] = num
print("classes_nums:", classes_nums)
#---------------------------------------------------------#
# 是否进行目标的裁剪
#---------------------------------------------------------#
if crop:
for i, c in list(enumerate(top_boxes)):
top, left, bottom, right = top_boxes[i]
top = max(0, np.floor(top).astype('int32'))
left = max(0, np.floor(left).astype('int32'))
bottom = min(image.size[1], np.floor(bottom).astype('int32'))
right = min(image.size[0], np.floor(right).astype('int32'))
dir_save_path = "img_crop"
if not os.path.exists(dir_save_path):
os.makedirs(dir_save_path)
crop_image = image.crop([left, top, right, bottom])
crop_image.save(os.path.join(dir_save_path, "crop_" + str(i) + ".png"), quality=95, subsampling=0)
print("save crop_" + str(i) + ".png to " + dir_save_path)
#---------------------------------------------------------#
# 图像绘制
#---------------------------------------------------------#
for i, c in list(enumerate(top_label)):
predicted_class = self.class_names[int(c)]
box = top_boxes[i]
score = top_conf[i]
top, left, bottom, right = box
top = max(0, np.floor(top).astype('int32'))
left = max(0, np.floor(left).astype('int32'))
bottom = min(image.size[1], np.floor(bottom).astype('int32'))
right = min(image.size[0], np.floor(right).astype('int32'))
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[c])
draw.rectangle([tuple(text_origin), tuple(text_origin + label_size)], fill=self.colors[c])
draw.text(text_origin, str(label,'UTF-8'), fill=(0, 0, 0), font=font)
del draw
return image
def get_FPS(self, image, test_interval):
image_shape = np.array(np.shape(image)[0:2])
#---------------------------------------------------------#
# 在这里将图像转换成RGB图像，防止灰度图在预测时报错。
# 代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
#---------------------------------------------------------#
image = cvtColor(image)
#---------------------------------------------------------#
# 给图像增加灰条，实现不失真的resize
# 也可以直接resize进行识别
#---------------------------------------------------------#
image_data = resize_image(image, (self.input_shape[1], self.input_shape[0]), self.letterbox_image)
#---------------------------------------------------------#
# 添加上batch_size维度，图片预处理，归一化。
#---------------------------------------------------------#
image_data = preprocess_input(np.expand_dims(np.array(image_data, dtype='float32'), 0))
preds = self.get_pred(image_data).numpy()
#-----------------------------------------------------------#
# 将预测结果进行解码
#-----------------------------------------------------------#
results = self.bbox_util.decode_box(preds, self.anchors, image_shape,
self.input_shape, self.letterbox_image, confidence=self.confidence)
t1 = time.time()
for _ in range(test_interval):
preds = self.get_pred(image_data).numpy()
#-----------------------------------------------------------#
# 将预测结果进行解码
#-----------------------------------------------------------#
results = self.bbox_util.decode_box(preds, self.anchors, image_shape,
self.input_shape, self.letterbox_image, confidence=self.confidence)
t2 = time.time()
tact_time = (t2 - t1) / test_interval
return tact_time
def get_map_txt(self, image_id, image, class_names, map_out_path):
f = open(os.path.join(map_out_path, "detection-results/"+image_id+".txt"),"w")
image_shape = np.array(np.shape(image)[0:2])
#---------------------------------------------------------#
# 在这里将图像转换成RGB图像，防止灰度图在预测时报错。
# 代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
#---------------------------------------------------------#
image = cvtColor(image)
#---------------------------------------------------------#
# 给图像增加灰条，实现不失真的resize
# 也可以直接resize进行识别
#---------------------------------------------------------#
image_data = resize_image(image, (self.input_shape[1], self.input_shape[0]), self.letterbox_image)
#---------------------------------------------------------#
# 添加上batch_size维度，图片预处理，归一化。
#---------------------------------------------------------#
image_data = preprocess_input(np.expand_dims(np.array(image_data, dtype='float32'), 0))
preds = self.get_pred(image_data).numpy()
#-----------------------------------------------------------#
# 将预测结果进行解码
#-----------------------------------------------------------#
results = self.bbox_util.decode_box(preds, self.anchors, image_shape,
self.input_shape, self.letterbox_image, confidence=self.confidence)
#--------------------------------------#
# 如果没有检测到物体，则返回原图
#--------------------------------------#
if len(results[0])<=0:
return
top_label = results[0][:, 4]
top_conf = results[0][:, 5]
top_boxes = results[0][:, :4]
for i, c in list(enumerate(top_label)):
predicted_class = self.class_names[int(c)]
box = top_boxes[i]
score = str(top_conf[i])
top, left, bottom, right = box
if predicted_class not in class_names:
continue
f.write("%s %s %s %s %s %s\n" % (predicted_class, score[:6], str(int(left)), str(int(top)), str(int(right)),str(int(bottom))))
f.close()
return
class CenterNet(object):
_defaults = {
#--------------------------------------------------------------------------#
# 使用自己训练好的模型进行预测一定要修改model_path和classes_path！
# model_path指向logs文件夹下的权值文件，classes_path指向model_data下的txt
#
# 训练好后logs文件夹下存在多个权值文件，选择验证集损失较低的即可。
# 验证集损失较低不代表mAP较高，仅代表该权值在验证集上泛化性能较好。
# 如果出现shape不匹配，同时要注意训练时的model_path和classes_path参数的修改
#--------------------------------------------------------------------------#
"model_path" : 'model_data/centernet_resnet50_voc.h5',
"classes_path" : 'model_data/voc_classes.txt',
#--------------------------------------------------------------------------#
# 输入图片的大小
#--------------------------------------------------------------------------#
"input_shape" : [512, 512],
#--------------------------------------------------------------------------#
# 用于选择所使用的模型的主干
# resnet50, hourglass
#--------------------------------------------------------------------------#
"backbone" : 'resnet50',
#--------------------------------------------------------------------------#
# 只有得分大于置信度的预测框会被保留下来
#--------------------------------------------------------------------------#
"confidence" : 0.3,
#---------------------------------------------------------------------#
# 非极大抑制所用到的nms_iou大小
#---------------------------------------------------------------------#
"nms_iou" : 0.3,
#--------------------------------------------------------------------------#
# 是否进行非极大抑制，可以根据检测效果自行选择
# backbone为resnet50时建议设置为True、backbone为hourglass时建议设置为False
#--------------------------------------------------------------------------#
"nms" : True,
#---------------------------------------------------------------------#
# 该变量用于控制是否使用letterbox_image对输入图像进行不失真的resize，
# 在多次测试后，发现关闭letterbox_image直接resize的效果更好
#---------------------------------------------------------------------#
"letterbox_image" : False,
}
@classmethod
def get_defaults(cls, n):
if n in cls._defaults:
return cls._defaults[n]
else:
return "Unrecognized attribute name '" + n + "'"
#---------------------------------------------------#
# 初始化centernet
#---------------------------------------------------#
def __init__(self, **kwargs):
self.__dict__.update(self._defaults)
show_config(**self._defaults)
self.heatmap = False
for name, value in kwargs.items():
setattr(self, name, value)
if name == "heatmap" and value == True:
self.heatmap = True
#---------------------------------------------------#
# 计算总的类的数量
#---------------------------------------------------#
self.class_names, self.num_classes = get_classes(self.classes_path)
#---------------------------------------------------#
# 画框设置不同的颜色
#---------------------------------------------------#
hsv_tuples = [(x / self.num_classes, 1., 1.) for x in range(self.num_classes)]
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))
self.bbox_util = BBoxUtility(nms_thresh=self.nms_iou)
self.generate()
#---------------------------------------------------#
# 载入模型
#---------------------------------------------------#
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.'
#----------------------------------------#
# 创建centernet模型
#----------------------------------------#
self.centernet = centernet([self.input_shape[0], self.input_shape[1], 3], num_classes=self.num_classes, backbone=self.backbone, mode='predict' if not self.heatmap else 'heatmap')
self.centernet.load_weights(self.model_path, by_name=True)
print('{} model, anchors, and classes loaded.'.format(self.model_path))
@tf.function
def get_pred(self, photo):
preds = self.centernet(photo, training=False)
return preds
#---------------------------------------------------#
# 检测图片
#---------------------------------------------------#
def detect_image(self, image, crop = False, count = False):
#---------------------------------------------------#
# 获得输入图片的高和宽
#---------------------------------------------------#
image_shape = np.array(np.shape(image)[0:2])
#---------------------------------------------------------#
# 在这里将图像转换成RGB图像，防止灰度图在预测时报错。
# 代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
#---------------------------------------------------------#
image = cvtColor(image)
#---------------------------------------------------------#
# 给图像增加灰条，实现不失真的resize
# 也可以直接resize进行识别
#---------------------------------------------------------#
image_data = resize_image(image, (self.input_shape[1], self.input_shape[0]), self.letterbox_image)
#---------------------------------------------------------#
# 添加上batch_size维度，图片预处理，归一化。
#---------------------------------------------------------#
image_data = np.expand_dims(preprocess_input(np.array(image_data, dtype='float32')), 0)
outputs = self.get_pred(image_data).numpy()
#--------------------------------------------------------------------------------------------#
# centernet后处理的过程，包括门限判断和传统非极大抑制。
# 对于centernet网络来讲，确立中心非常重要。对于大目标而言，会存在许多的局部信息。
# 此时大目标中心点比较难以确定。使用最大池化的非极大抑制方法无法去除局部框
# 这里面存在传统的nms处理方法，可以选择关闭和开启。
# 实际测试中，hourglass为主干网络时有无额外的nms相差不大，resnet相差较大。
#--------------------------------------------------------------------------------------------#
results = self.bbox_util.postprocess(outputs, self.nms, image_shape, self.input_shape, self.letterbox_image, confidence=self.confidence)
#--------------------------------------#
# 如果没有检测到物体，则返回原图
#--------------------------------------#
if results[0] is None:
return image
top_label = np.array(results[0][:, 5], dtype = 'int32')
top_conf = results[0][:, 4]
top_boxes = results[0][:, :4]
#---------------------------------------------------------#
# 设置字体与边框厚度
#---------------------------------------------------------#
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(image)[0] + np.shape(image)[1]) // self.input_shape[0], 1)
#---------------------------------------------------------#
# 计数
#---------------------------------------------------------#
if count:
print("top_label:", top_label)
classes_nums = np.zeros([self.num_classes])
for i in range(self.num_classes):
num = np.sum(top_label == i)
if num > 0:
print(self.class_names[i], " : ", num)
classes_nums[i] = num
print("classes_nums:", classes_nums)
#---------------------------------------------------------#
# 是否进行目标的裁剪
#---------------------------------------------------------#
if crop:
for i, c in list(enumerate(top_label)):
top, left, bottom, right = top_boxes[i]
top = max(0, np.floor(top).astype('int32'))
left = max(0, np.floor(left).astype('int32'))
bottom = min(image.size[1], np.floor(bottom).astype('int32'))
right = min(image.size[0], np.floor(right).astype('int32'))
dir_save_path = "img_crop"
if not os.path.exists(dir_save_path):
os.makedirs(dir_save_path)
crop_image = image.crop([left, top, right, bottom])
crop_image.save(os.path.join(dir_save_path, "crop_" + str(i) + ".png"), quality=95, subsampling=0)
print("save crop_" + str(i) + ".png to " + dir_save_path)
#---------------------------------------------------------#
# 图像绘制
#---------------------------------------------------------#
for i, c in list(enumerate(top_label)):
predicted_class = self.class_names[int(c)]
box = top_boxes[i]
score = top_conf[i]
top, left, bottom, right = box
top = max(0, np.floor(top).astype('int32'))
left = max(0, np.floor(left).astype('int32'))
bottom = min(image.size[1], np.floor(bottom).astype('int32'))
right = min(image.size[0], np.floor(right).astype('int32'))
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[c])
draw.rectangle([tuple(text_origin), tuple(text_origin + label_size)], fill=self.colors[c])
draw.text(text_origin, str(label,'UTF-8'), fill=(0, 0, 0), font=font)
del draw
return image
def get_FPS(self, image, test_interval):
#---------------------------------------------------#
# 获得输入图片的高和宽
#---------------------------------------------------#
image_shape = np.array(np.shape(image)[0:2])
#---------------------------------------------------------#
# 在这里将图像转换成RGB图像，防止灰度图在预测时报错。
# 代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
#---------------------------------------------------------#
image = cvtColor(image)
#---------------------------------------------------------#
# 给图像增加灰条，实现不失真的resize
# 也可以直接resize进行识别
#---------------------------------------------------------#
image_data = resize_image(image, (self.input_shape[1], self.input_shape[0]), self.letterbox_image)
#---------------------------------------------------------#
# 添加上batch_size维度，图片预处理，归一化。
#---------------------------------------------------------#
image_data = np.expand_dims(preprocess_input(np.array(image_data, dtype='float32')), 0)
outputs = self.get_pred(image_data).numpy()
#--------------------------------------------------------------------------------------------#
# centernet后处理的过程，包括门限判断和传统非极大抑制。
# 对于centernet网络来讲，确立中心非常重要。对于大目标而言，会存在许多的局部信息。
# 此时大目标中心点比较难以确定。使用最大池化的非极大抑制方法无法去除局部框
# 这里面存在传统的nms处理方法，可以选择关闭和开启。
# 实际测试中，hourglass为主干网络时有无额外的nms相差不大，resnet相差较大。
#--------------------------------------------------------------------------------------------#
results = self.bbox_util.postprocess(outputs, self.nms, image_shape, self.input_shape, self.letterbox_image, confidence=self.confidence)
t1 = time.time()
for _ in range(test_interval):
outputs = self.get_pred(image_data).numpy()
#--------------------------------------------------------------------------------------------#
# centernet后处理的过程，包括门限判断和传统非极大抑制。
# 对于centernet网络来讲，确立中心非常重要。对于大目标而言，会存在许多的局部信息。
# 此时大目标中心点比较难以确定。使用最大池化的非极大抑制方法无法去除局部框
# 这里面存在传统的nms处理方法，可以选择关闭和开启。
# 实际测试中，hourglass为主干网络时有无额外的nms相差不大，resnet相差较大。
#--------------------------------------------------------------------------------------------#
results = self.bbox_util.postprocess(outputs, self.nms, image_shape, self.input_shape, self.letterbox_image, confidence=self.confidence)
t2 = time.time()
tact_time = (t2 - t1) / test_interval
return tact_time
def detect_heatmap(self, image, heatmap_save_path):
import cv2
import matplotlib.pyplot as plt
#---------------------------------------------------------#
# 在这里将图像转换成RGB图像，防止灰度图在预测时报错。
# 代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
#---------------------------------------------------------#
image = cvtColor(image)
#---------------------------------------------------------#
# 给图像增加灰条，实现不失真的resize
# 也可以直接resize进行识别
#---------------------------------------------------------#
image_data = resize_image(image, (self.input_shape[1], self.input_shape[0]), self.letterbox_image)
#---------------------------------------------------------#
# 添加上batch_size维度，并进行归一化
#---------------------------------------------------------#
image_data = np.expand_dims(preprocess_input(np.array(image_data, dtype='float32')), 0)
output = self.centernet.predict(image_data)
plt.imshow(image, alpha=1)
plt.axis('off')
mask = np.zeros((image.size[1], image.size[0]))
score = np.max(output[0], -1)
score = cv2.resize(score, (image.size[0], image.size[1]))
normed_score = (score * 255).astype('uint8')
mask = np.maximum(mask, normed_score)
plt.imshow(mask, alpha=0.5, interpolation='nearest', cmap="jet")
plt.axis('off')
plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)
plt.margins(0, 0)
plt.savefig(heatmap_save_path, dpi=200, bbox_inches='tight', pad_inches = -0.1)
print("Save to the " + heatmap_save_path)
plt.show()
def get_map_txt(self, image_id, image, class_names, map_out_path):
f = open(os.path.join(map_out_path, "detection-results/"+image_id+".txt"),"w")
#---------------------------------------------------#
# 获得输入图片的高和宽
#---------------------------------------------------#
image_shape = np.array(np.shape(image)[0:2])
#---------------------------------------------------------#
# 在这里将图像转换成RGB图像，防止灰度图在预测时报错。
# 代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
#---------------------------------------------------------#
image = cvtColor(image)
#---------------------------------------------------------#
# 给图像增加灰条，实现不失真的resize
# 也可以直接resize进行识别
#---------------------------------------------------------#
image_data = resize_image(image, (self.input_shape[1], self.input_shape[0]), self.letterbox_image)
#---------------------------------------------------------#
# 添加上batch_size维度，图片预处理，归一化。
#---------------------------------------------------------#
image_data = np.expand_dims(preprocess_input(np.array(image_data, dtype='float32')), 0)
outputs = self.get_pred(image_data).numpy()
#--------------------------------------------------------------------------------------------#
# centernet后处理的过程，包括门限判断和传统非极大抑制。
# 对于centernet网络来讲，确立中心非常重要。对于大目标而言，会存在许多的局部信息。
# 此时大目标中心点比较难以确定。使用最大池化的非极大抑制方法无法去除局部框
# 这里面存在传统的nms处理方法，可以选择关闭和开启。
# 实际测试中，hourglass为主干网络时有无额外的nms相差不大，resnet相差较大。
#--------------------------------------------------------------------------------------------#
results = self.bbox_util.postprocess(outputs, self.nms, image_shape, self.input_shape, self.letterbox_image, confidence=self.confidence)
#--------------------------------------#
# 如果没有检测到物体，则返回原图
#--------------------------------------#
if results[0] is None:
return
top_label = np.array(results[0][:, 5], dtype = 'int32')
top_conf = results[0][:, 4]
top_boxes = results[0][:, :4]
for i, c in list(enumerate(top_label)):
predicted_class = self.class_names[int(c)]
box = top_boxes[i]
score = str(top_conf[i])
top, left, bottom, right = box
if predicted_class not in class_names:
continue
f.write("%s %s %s %s %s %s\n" % (predicted_class, score[:6], str(int(left)), str(int(top)), str(int(right)),str(int(bottom))))
f.close()
return
class FRCNN(object):
_defaults = {
#--------------------------------------------------------------------------#
# 使用自己训练好的模型进行预测一定要修改model_path和classes_path！
# model_path指向logs文件夹下的权值文件，classes_path指向model_data下的txt
#
# 训练好后logs文件夹下存在多个权值文件，选择验证集损失较低的即可。
# 验证集损失较低不代表mAP较高，仅代表该权值在验证集上泛化性能较好。
# 如果出现shape不匹配，同时要注意训练时的model_path和classes_path参数的修改
#--------------------------------------------------------------------------#
"model_path": 'model_data/voc_weights_resnet.h5',
"classes_path": 'model_data/voc_classes.txt',
#---------------------------------------------------------------------#
# 网络的主干特征提取网络，resnet50或者vgg
#---------------------------------------------------------------------#
"backbone": "resnet50",
#---------------------------------------------------------------------#
# 只有得分大于置信度的预测框会被保留下来
#---------------------------------------------------------------------#
"confidence": 0.5,
#---------------------------------------------------------------------#
# 非极大抑制所用到的nms_iou大小
#---------------------------------------------------------------------#
"nms_iou": 0.3,
#---------------------------------------------------------------------#
# 用于指定先验框的大小
#---------------------------------------------------------------------#
'anchors_size': [128, 256, 512],
}
@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)
for name, value in kwargs.items():
setattr(self, name, value)
#---------------------------------------------------#
# 获得种类和先验框的数量
#---------------------------------------------------#
self.class_names, self.num_classes = get_classes(self.classes_path)
self.num_classes = self.num_classes + 1
#---------------------------------------------------#
# 创建一个工具箱，用于进行解码
# 最大使用min_k个建议框，默认为150
#---------------------------------------------------#
self.bbox_util = BBoxUtility(self.num_classes,
nms_iou=self.nms_iou,
min_k=150)
#---------------------------------------------------#
# 画框设置不同的颜色
#---------------------------------------------------#
hsv_tuples = [(x / self.num_classes, 1., 1.)
for x in range(self.num_classes)]
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))
self.generate()
#---------------------------------------------------#
# 载入模型
#---------------------------------------------------#
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.'
#-------------------------------#
# 载入模型与权值
#-------------------------------#
self.model_rpn, self.model_classifier = frcnn.get_predict_model(
self.num_classes, self.backbone)
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))
#---------------------------------------------------#
# 检测图片
#---------------------------------------------------#
def detect_image(self, image, crop=False):
#---------------------------------------------------#
# 计算输入图片的高和宽
#---------------------------------------------------#
image_shape = np.array(np.shape(image)[0:2])
#---------------------------------------------------#
# 计算输入到网络中进行运算的图片的高和宽
# 保证短边是600的
#---------------------------------------------------#
input_shape = get_new_img_size(image_shape[0], image_shape[1])
#---------------------------------------------------------#
# 在这里将图像转换成RGB图像，防止灰度图在预测时报错。
# 代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
#---------------------------------------------------------#
image = cvtColor(image)
#---------------------------------------------------------#
# 给原图像进行resize，resize到短边为600的大小上
#---------------------------------------------------------#
image_data = resize_image(image, [input_shape[1], input_shape[0]])
#---------------------------------------------------------#
# 添加上batch_size维度
#---------------------------------------------------------#
image_data = np.expand_dims(
preprocess_input(np.array(image_data, dtype='float32')), 0)
#---------------------------------------------------------#
# 获得rpn网络预测结果和base_layer
#---------------------------------------------------------#
rpn_pred = self.model_rpn(image_data)
rpn_pred = [x.numpy() for x in rpn_pred]
#---------------------------------------------------------#
# 生成先验框并解码
#---------------------------------------------------------#
anchors = get_anchors(input_shape, self.backbone, self.anchors_size)
rpn_results = self.bbox_util.detection_out_rpn(rpn_pred, anchors)
#-------------------------------------------------------------#
# 利用建议框获得classifier网络预测结果
#-------------------------------------------------------------#
classifier_pred = self.model_classifier(
[rpn_pred[2], rpn_results[:, :, [1, 0, 3, 2]]])
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, rpn_results, image_shape, input_shape,
self.confidence)
if len(results[0]) == 0:
return image
top_label = np.array(results[0][:, 5], dtype='int32')
top_conf = results[0][:, 4]
top_boxes = results[0][:, :4]
#---------------------------------------------------------#
# 设置字体与边框厚度
#---------------------------------------------------------#
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(image)[0] + np.shape(image)[1]) // input_shape[0], 1)
#---------------------------------------------------------#
# 是否进行目标的裁剪
#---------------------------------------------------------#
if crop:
for i, c in list(enumerate(top_label)):
top, left, bottom, right = top_boxes[i]
top = max(0, np.floor(top).astype('int32'))
left = max(0, np.floor(left).astype('int32'))
bottom = min(image.size[1], np.floor(bottom).astype('int32'))
right = min(image.size[0], np.floor(right).astype('int32'))
dir_save_path = "img_crop"
if not os.path.exists(dir_save_path):
os.makedirs(dir_save_path)
crop_image = image.crop([left, top, right, bottom])
crop_image.save(os.path.join(dir_save_path,
"crop_" + str(i) + ".png"),
quality=95,
subsampling=0)
print("save crop_" + str(i) + ".png to " + dir_save_path)
#---------------------------------------------------------#
# 图像绘制
#---------------------------------------------------------#
for i, c in list(enumerate(top_label)):
predicted_class = self.class_names[int(c)]
box = top_boxes[i]
score = top_conf[i]
top, left, bottom, right = box
top = max(0, np.floor(top).astype('int32'))
left = max(0, np.floor(left).astype('int32'))
bottom = min(image.size[1], np.floor(bottom).astype('int32'))
right = min(image.size[0], np.floor(right).astype('int32'))
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[c])
draw.rectangle(
[tuple(text_origin),
tuple(text_origin + label_size)],
fill=self.colors[c])
draw.text(text_origin,
str(label, 'UTF-8'),
fill=(0, 0, 0),
font=font)
del draw
return image
def get_FPS(self, image, test_interval):
#---------------------------------------------------#
# 计算输入图片的高和宽
#---------------------------------------------------#
image_shape = np.array(np.shape(image)[0:2])
input_shape = get_new_img_size(image_shape[0], image_shape[1])
#---------------------------------------------------------#
# 在这里将图像转换成RGB图像，防止灰度图在预测时报错。
# 代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
#---------------------------------------------------------#
image = cvtColor(image)
#---------------------------------------------------------#
# 给原图像进行resize，resize到短边为600的大小上
#---------------------------------------------------------#
image_data = resize_image(image, [input_shape[1], input_shape[0]])
#---------------------------------------------------------#
# 添加上batch_size维度
#---------------------------------------------------------#
image_data = np.expand_dims(
preprocess_input(np.array(image_data, dtype='float32')), 0)
#---------------------------------------------------------#
# 获得rpn网络预测结果和base_layer
#---------------------------------------------------------#
rpn_pred = self.model_rpn(image_data)
rpn_pred = [x.numpy() for x in rpn_pred]
#---------------------------------------------------------#
# 生成先验框并解码
#---------------------------------------------------------#
anchors = get_anchors(input_shape, self.backbone, self.anchors_size)
rpn_results = self.bbox_util.detection_out_rpn(rpn_pred, anchors)
#-------------------------------------------------------------#
# 利用建议框获得classifier网络预测结果
#-------------------------------------------------------------#
classifier_pred = self.model_classifier(
[rpn_pred[2], rpn_results[:, :, [1, 0, 3, 2]]])
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, rpn_results, image_shape, input_shape,
self.confidence)
t1 = time.time()
for _ in range(test_interval):
#---------------------------------------------------------#
# 获得rpn网络预测结果和base_layer
#---------------------------------------------------------#
rpn_pred = self.model_rpn(image_data)
rpn_pred = [x.numpy() for x in rpn_pred]
#---------------------------------------------------------#
# 生成先验框并解码
#---------------------------------------------------------#
anchors = get_anchors(input_shape, self.backbone,
self.anchors_size)
rpn_results = self.bbox_util.detection_out_rpn(rpn_pred, anchors)
temp_ROIs = rpn_results[:, :, [1, 0, 3, 2]]
#-------------------------------------------------------------#
# 利用建议框获得classifier网络预测结果
#-------------------------------------------------------------#
classifier_pred = self.model_classifier([rpn_pred[2], 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, rpn_results, image_shape, input_shape,
self.confidence)
t2 = time.time()
tact_time = (t2 - t1) / test_interval
return tact_time
def get_map_txt(self, image_id, image, class_names, map_out_path):
f = open(
os.path.join(map_out_path,
"detection-results/" + image_id + ".txt"), "w")
#---------------------------------------------------#
# 计算输入图片的高和宽
#---------------------------------------------------#
image_shape = np.array(np.shape(image)[0:2])
input_shape = get_new_img_size(image_shape[0], image_shape[1])
#---------------------------------------------------------#
# 在这里将图像转换成RGB图像，防止灰度图在预测时报错。
# 代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
#---------------------------------------------------------#
image = cvtColor(image)
#---------------------------------------------------------#
# 给原图像进行resize，resize到短边为600的大小上
#---------------------------------------------------------#
image_data = resize_image(image, [input_shape[1], input_shape[0]])
#---------------------------------------------------------#
# 添加上batch_size维度
#---------------------------------------------------------#
image_data = np.expand_dims(
preprocess_input(np.array(image_data, dtype='float32')), 0)
#---------------------------------------------------------#
# 获得rpn网络预测结果和base_layer
#---------------------------------------------------------#
rpn_pred = self.model_rpn(image_data)
rpn_pred = [x.numpy() for x in rpn_pred]
#---------------------------------------------------------#
# 生成先验框并解码
#---------------------------------------------------------#
anchors = get_anchors(input_shape, self.backbone, self.anchors_size)
rpn_results = self.bbox_util.detection_out_rpn(rpn_pred, anchors)
#-------------------------------------------------------------#
# 利用建议框获得classifier网络预测结果
#-------------------------------------------------------------#
classifier_pred = self.model_classifier(
[rpn_pred[2], rpn_results[:, :, [1, 0, 3, 2]]])
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, rpn_results, image_shape, input_shape,
self.confidence)
#--------------------------------------#
# 如果没有检测到物体，则返回原图
#--------------------------------------#
if len(results[0]) <= 0:
return
top_label = np.array(results[0][:, 5], dtype='int32')
top_conf = results[0][:, 4]
top_boxes = results[0][:, :4]
for i, c in list(enumerate(top_label)):
predicted_class = self.class_names[int(c)]
box = top_boxes[i]
score = str(top_conf[i])
top, left, bottom, right = box
if predicted_class not in class_names:
continue
f.write("%s %s %s %s %s %s\n" %
(predicted_class, score[:6], str(int(left)), str(
int(top)), str(int(right)), str(int(bottom))))
f.close()
return
class SSD(object):
_defaults = {
#--------------------------------------------------------------------------#
# 使用自己训练好的模型进行预测一定要修改model_path和classes_path！
# model_path指向logs文件夹下的权值文件，classes_path指向model_data下的txt
#
# 训练好后logs文件夹下存在多个权值文件，选择验证集损失较低的即可。
# 验证集损失较低不代表mAP较高，仅代表该权值在验证集上泛化性能较好。
# 如果出现shape不匹配，同时要注意训练时的model_path和classes_path参数的修改
#--------------------------------------------------------------------------#
"model_path": 'model_data/ssd_weights.pth',
"classes_path": 'model_data/voc_classes.txt',
#---------------------------------------------------------------------#
# 用于预测的图像大小，和train时使用同一个即可
#---------------------------------------------------------------------#
"input_shape": [300, 300],
#-------------------------------#
# 主干网络的选择
# vgg或者mobilenetv2
#-------------------------------#
"backbone": "vgg",
#---------------------------------------------------------------------#
# 只有得分大于置信度的预测框会被保留下来
#---------------------------------------------------------------------#
"confidence": 0.5,
#---------------------------------------------------------------------#
# 非极大抑制所用到的nms_iou大小
#---------------------------------------------------------------------#
"nms_iou": 0.45,
#---------------------------------------------------------------------#
# 用于指定先验框的大小
#---------------------------------------------------------------------#
'anchors_size': [30, 60, 111, 162, 213, 264, 315],
#---------------------------------------------------------------------#
# 该变量用于控制是否使用letterbox_image对输入图像进行不失真的resize，
# 在多次测试后，发现关闭letterbox_image直接resize的效果更好
#---------------------------------------------------------------------#
"letterbox_image": False,
#-------------------------------#
# 是否使用Cuda
# 没有GPU可以设置成False
#-------------------------------#
"cuda": True,
}
@classmethod
def get_defaults(cls, n):
if n in cls._defaults:
return cls._defaults[n]
else:
return "Unrecognized attribute name '" + n + "'"
#---------------------------------------------------#
# 初始化ssd
#---------------------------------------------------#
def __init__(self, **kwargs):
self.__dict__.update(self._defaults)
for name, value in kwargs.items():
setattr(self, name, value)
#---------------------------------------------------#
# 计算总的类的数量
#---------------------------------------------------#
self.class_names, self.num_classes = get_classes(self.classes_path)
self.anchors = torch.from_numpy(
get_anchors(self.input_shape, self.anchors_size,
self.backbone)).type(torch.FloatTensor)
if self.cuda:
self.anchors = self.anchors.cuda()
self.num_classes = self.num_classes + 1
#---------------------------------------------------#
# 画框设置不同的颜色
#---------------------------------------------------#
hsv_tuples = [(x / self.num_classes, 1., 1.)
for x in range(self.num_classes)]
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))
self.bbox_util = BBoxUtility(self.num_classes)
self.generate()
show_config(**self._defaults)
#---------------------------------------------------#
# 载入模型
#---------------------------------------------------#
def generate(self, onnx=False):
#-------------------------------#
# 载入模型与权值
#-------------------------------#
self.net = SSD300(self.num_classes, self.backbone)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.net.load_state_dict(
torch.load(self.model_path, map_location=device))
self.net = self.net.eval()
print('{} model, anchors, and classes loaded.'.format(self.model_path))
if not onnx:
if self.cuda:
self.net = torch.nn.DataParallel(self.net)
self.net = self.net.cuda()
#---------------------------------------------------#
# 检测图片
#---------------------------------------------------#
def detect_image(self, image, crop=False, count=False):
#---------------------------------------------------#
# 计算输入图片的高和宽
#---------------------------------------------------#
image_shape = np.array(np.shape(image)[0:2])
#---------------------------------------------------------#
# 在这里将图像转换成RGB图像，防止灰度图在预测时报错。
# 代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
#---------------------------------------------------------#
image = cvtColor(image)
#---------------------------------------------------------#
# 给图像增加灰条，实现不失真的resize
# 也可以直接resize进行识别
#---------------------------------------------------------#
image_data = resize_image(image,
(self.input_shape[1], self.input_shape[0]),
self.letterbox_image)
#---------------------------------------------------------#
# 添加上batch_size维度，图片预处理，归一化。
#---------------------------------------------------------#
image_data = np.expand_dims(
np.transpose(
preprocess_input(np.array(image_data, dtype='float32')),
(2, 0, 1)), 0)
with torch.no_grad():
#---------------------------------------------------#
# 转化成torch的形式
#---------------------------------------------------#
images = torch.from_numpy(image_data).type(torch.FloatTensor)
if self.cuda:
images = images.cuda()
#---------------------------------------------------------#
# 将图像输入网络当中进行预测！
#---------------------------------------------------------#
outputs = self.net(images)
#-----------------------------------------------------------#
# 将预测结果进行解码
#-----------------------------------------------------------#
results = self.bbox_util.decode_box(outputs,
self.anchors,
image_shape,
self.input_shape,
self.letterbox_image,
nms_iou=self.nms_iou,
confidence=self.confidence)
#--------------------------------------#
# 如果没有检测到物体，则返回原图
#--------------------------------------#
if len(results[0]) <= 0:
return image
top_label = np.array(results[0][:, 4], dtype='int32')
top_conf = results[0][:, 5]
top_boxes = results[0][:, :4]
#---------------------------------------------------------#
# 设置字体与边框厚度
#---------------------------------------------------------#
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(image)[0] + np.shape(image)[1]) // self.input_shape[0],
1)
#---------------------------------------------------------#
# 计数
#---------------------------------------------------------#
if count:
print("top_label:", top_label)
classes_nums = np.zeros([self.num_classes])
for i in range(self.num_classes):
num = np.sum(top_label == i)
if num > 0:
print(self.class_names[i], " : ", num)
classes_nums[i] = num
print("classes_nums:", classes_nums)
#---------------------------------------------------------#
# 是否进行目标的裁剪
#---------------------------------------------------------#
if crop:
for i, c in list(enumerate(top_boxes)):
top, left, bottom, right = top_boxes[i]
top = max(0, np.floor(top).astype('int32'))
left = max(0, np.floor(left).astype('int32'))
bottom = min(image.size[1], np.floor(bottom).astype('int32'))
right = min(image.size[0], np.floor(right).astype('int32'))
dir_save_path = "img_crop"
if not os.path.exists(dir_save_path):
os.makedirs(dir_save_path)
crop_image = image.crop([left, top, right, bottom])
crop_image.save(os.path.join(dir_save_path,
"crop_" + str(i) + ".png"),
quality=95,
subsampling=0)
print("save crop_" + str(i) + ".png to " + dir_save_path)
#---------------------------------------------------------#
# 图像绘制
#---------------------------------------------------------#
for i, c in list(enumerate(top_label)):
predicted_class = self.class_names[int(c)]
box = top_boxes[i]
score = top_conf[i]
top, left, bottom, right = box
top = max(0, np.floor(top).astype('int32'))
left = max(0, np.floor(left).astype('int32'))
bottom = min(image.size[1], np.floor(bottom).astype('int32'))
right = min(image.size[0], np.floor(right).astype('int32'))
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[c])
draw.rectangle(
[tuple(text_origin),
tuple(text_origin + label_size)],
fill=self.colors[c])
draw.text(text_origin,
str(label, 'UTF-8'),
fill=(0, 0, 0),
font=font)
del draw
return image
def get_FPS(self, image, test_interval):
#---------------------------------------------------#
# 计算输入图片的高和宽
#---------------------------------------------------#
image_shape = np.array(np.shape(image)[0:2])
#---------------------------------------------------------#
# 在这里将图像转换成RGB图像，防止灰度图在预测时报错。
# 代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
#---------------------------------------------------------#
image = cvtColor(image)
#---------------------------------------------------------#
# 给图像增加灰条，实现不失真的resize
# 也可以直接resize进行识别
#---------------------------------------------------------#
image_data = resize_image(image,
(self.input_shape[1], self.input_shape[0]),
self.letterbox_image)
#---------------------------------------------------------#
# 添加上batch_size维度，图片预处理，归一化。
#---------------------------------------------------------#
image_data = np.expand_dims(
np.transpose(
preprocess_input(np.array(image_data, dtype='float32')),
(2, 0, 1)), 0)
with torch.no_grad():
#---------------------------------------------------#
# 转化成torch的形式
#---------------------------------------------------#
images = torch.from_numpy(image_data).type(torch.FloatTensor)
if self.cuda:
images = images.cuda()
#---------------------------------------------------------#
# 将图像输入网络当中进行预测！
#---------------------------------------------------------#
outputs = self.net(images)
#-----------------------------------------------------------#
# 将预测结果进行解码
#-----------------------------------------------------------#
results = self.bbox_util.decode_box(outputs,
self.anchors,
image_shape,
self.input_shape,
self.letterbox_image,
nms_iou=self.nms_iou,
confidence=self.confidence)
t1 = time.time()
for _ in range(test_interval):
with torch.no_grad():
#---------------------------------------------------------#
# 将图像输入网络当中进行预测！
#---------------------------------------------------------#
outputs = self.net(images)
#-----------------------------------------------------------#
# 将预测结果进行解码
#-----------------------------------------------------------#
results = self.bbox_util.decode_box(outputs,
self.anchors,
image_shape,
self.input_shape,
self.letterbox_image,
nms_iou=self.nms_iou,
confidence=self.confidence)
t2 = time.time()
tact_time = (t2 - t1) / test_interval
return tact_time
def convert_to_onnx(self, simplify, model_path):
import onnx
self.generate(onnx=True)
im = torch.zeros(1, 3, *self.input_shape).to(
'cpu') # image size(1, 3, 512, 512) BCHW
input_layer_names = ["images"]
output_layer_names = ["output"]
# Export the model
print(f'Starting export with onnx {onnx.__version__}.')
torch.onnx.export(self.net,
im,
f=model_path,
verbose=False,
opset_version=12,
training=torch.onnx.TrainingMode.EVAL,
do_constant_folding=True,
input_names=input_layer_names,
output_names=output_layer_names,
dynamic_axes=None)
# Checks
model_onnx = onnx.load(model_path) # load onnx model
onnx.checker.check_model(model_onnx) # check onnx model
# Simplify onnx
if simplify:
import onnxsim
print(f'Simplifying with onnx-simplifier {onnxsim.__version__}.')
model_onnx, check = onnxsim.simplify(model_onnx,
dynamic_input_shape=False,
input_shapes=None)
assert check, 'assert check failed'
onnx.save(model_onnx, model_path)
print('Onnx model save as {}'.format(model_path))
def get_map_txt(self, image_id, image, class_names, map_out_path):
f = open(
os.path.join(map_out_path,
"detection-results/" + image_id + ".txt"), "w")
#---------------------------------------------------#
# 计算输入图片的高和宽
#---------------------------------------------------#
image_shape = np.array(np.shape(image)[0:2])
#---------------------------------------------------------#
# 在这里将图像转换成RGB图像，防止灰度图在预测时报错。
# 代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
#---------------------------------------------------------#
image = cvtColor(image)
#---------------------------------------------------------#
# 给图像增加灰条，实现不失真的resize
# 也可以直接resize进行识别
#---------------------------------------------------------#
image_data = resize_image(image,
(self.input_shape[1], self.input_shape[0]),
self.letterbox_image)
#---------------------------------------------------------#
# 添加上batch_size维度，图片预处理，归一化。
#---------------------------------------------------------#
image_data = np.expand_dims(
np.transpose(
preprocess_input(np.array(image_data, dtype='float32')),
(2, 0, 1)), 0)
with torch.no_grad():
#---------------------------------------------------#
# 转化成torch的形式
#---------------------------------------------------#
images = torch.from_numpy(image_data).type(torch.FloatTensor)
if self.cuda:
images = images.cuda()
#---------------------------------------------------------#
# 将图像输入网络当中进行预测！
#---------------------------------------------------------#
outputs = self.net(images)
#-----------------------------------------------------------#
# 将预测结果进行解码
#-----------------------------------------------------------#
results = self.bbox_util.decode_box(outputs,
self.anchors,
image_shape,
self.input_shape,
self.letterbox_image,
nms_iou=self.nms_iou,
confidence=self.confidence)
#--------------------------------------#
# 如果没有检测到物体，则返回原图
#--------------------------------------#
if len(results[0]) <= 0:
return
top_label = np.array(results[0][:, 4], dtype='int32')
top_conf = results[0][:, 5]
top_boxes = results[0][:, :4]
for i, c in list(enumerate(top_label)):
predicted_class = self.class_names[int(c)]
box = top_boxes[i]
score = str(top_conf[i])
top, left, bottom, right = box
if predicted_class not in class_names:
continue
f.write("%s %s %s %s %s %s\n" %
(predicted_class, score[:6], str(int(left)), str(
int(top)), str(int(right)), str(int(bottom))))
f.close()
return
model_rpn, model_all = get_model(num_classes, backbone=backbone)
if model_path != '':
#------------------------------------------------------#
# 载入预训练权重
#------------------------------------------------------#
print('Load weights {}.'.format(model_path))
model_rpn.load_weights(model_path, by_name=True)
model_all.load_weights(model_path, by_name=True)
#--------------------------------------------#
# 训练参数的设置
#--------------------------------------------#
callback = tf.summary.create_file_writer("logs")
loss_history = LossHistory("logs/")
bbox_util = BBoxUtility(num_classes)
roi_helper = ProposalTargetCreator(num_classes)
#---------------------------#
# 读取数据集对应的txt
#---------------------------#
with open(train_annotation_path) as f:
train_lines = f.readlines()
with open(val_annotation_path) as f:
val_lines = f.readlines()
num_train = len(train_lines)
num_val = len(val_lines)
freeze_layers = {'vgg': 17, 'resnet50': 141}[backbone]
if Freeze_Train:
for i in range(freeze_layers):
if type(model_all.layers[i]) != tf.keras.layers.BatchNormalization:
class OneNet(object):
# Please check {model_path, structure, backbone, output_layers, shape, use_anchor}
_defaults = {
#--------------------------------------------------------------------------#
# 使用自己训练好的模型进行预测一定要修改model_path和classes_path！
# model_path指向logs文件夹下的权值文件，classes_path指向model_data下的txt
#
# 训练好后logs文件夹下存在多个权值文件，选择验证集损失较低的即可。
# 验证集损失较低不代表mAP较高，仅代表该权值在验证集上泛化性能较好。
# 如果出现shape不匹配，同时要注意训练时的model_path和classes_path参数的修改
#--------------------------------------------------------------------------#
"model_path" : 'model_data/onenet_resnet50.h5',
"classes_path" : 'model_data/voc_classes.txt',
#--------------------------------------------------------------------------#
# 输入图片的大小
#--------------------------------------------------------------------------#
"input_shape" : [320, 320, 3],
#--------------------------------------------------------------------------#
# 用于选择所使用的模型的主干
# resnet50, hourglass
#--------------------------------------------------------------------------#
"backbone" : 'resnet50',
#--------------------------------------------------------------------------#
# 只有得分大于置信度的预测框会被保留下来
#--------------------------------------------------------------------------#
"confidence" : 0.3,
"max_objects" : 100,
#---------------------------------------------------------------------#
# 非极大抑制所用到的nms_iou大小
#---------------------------------------------------------------------#
"nms_iou" : 0.5,
#--------------------------------------------------------------------------#
# 是否进行非极大抑制，可以根据检测效果自行选择
# backbone为resnet50时建议设置为True、backbone为hourglass时建议设置为False
#--------------------------------------------------------------------------#
"nms" : True,
#---------------------------------------------------------------------#
# 该变量用于控制是否使用letterbox_image对输入图像进行不失真的resize，
# 在多次测试后，发现关闭letterbox_image直接resize的效果更好
#---------------------------------------------------------------------#
"letterbox_image" : False,
}
@classmethod
def get_defaults(cls, n):
if n in cls._defaults:
return cls._defaults[n]
else:
return "Unrecognized attribute name '" + n + "'"
# ---------------------------------------------------#
# 初始化onenet
# ---------------------------------------------------#
def __init__(self, **kwargs):
self.__dict__.update(self._defaults)
for name, value in kwargs.items():
setattr(self, name, value)
#---------------------------------------------------#
# 计算总的类的数量
#---------------------------------------------------#
self.class_names, self.num_classes = get_classes(self.classes_path)
#---------------------------------------------------#
# 画框设置不同的颜色
#---------------------------------------------------#
hsv_tuples = [(x / self.num_classes, 1., 1.) for x in range(self.num_classes)]
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))
self.bbox_util = BBoxUtility(nms_thresh=self.nms_iou)
self.generate()
# ---------------------------------------------------#
# 载入模型
# ---------------------------------------------------#
def generate(self):
self.model = build_model(self.input_shape,
num_classes=self.num_classes,
backbone=self.backbone,
max_objects=self.max_objects,
mode='predict')
if hasattr(self, "model_path"):
self.model.load_weights(self.model_path, by_name=True, skip_mismatch=True)
print('{} model, anchors, and classes loaded.'.format(self.model_path))
@tf.function
def get_pred(self, photo):
preds = self.model(photo, training=False)
return preds
# ---------------------------------------------------#
# 检测图片
# ---------------------------------------------------#
def detect_image(self, image):
#---------------------------------------------------#
# 获得输入图片的高和宽
#---------------------------------------------------#
image_shape = np.array(np.shape(image)[0:2])
#---------------------------------------------------------#
# 在这里将图像转换成RGB图像，防止灰度图在预测时报错。
# 代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
#---------------------------------------------------------#
image = cvtColor(image)
#---------------------------------------------------------#
# 给图像增加灰条，实现不失真的resize
# 也可以直接resize进行识别
#---------------------------------------------------------#
image_data = resize_image(image, (self.input_shape[1], self.input_shape[0]), self.letterbox_image)
#---------------------------------------------------------#
# 添加上batch_size维度，图片预处理，归一化。
#---------------------------------------------------------#BBoxUtility
image_data = np.expand_dims(preprocess_input(np.array(image_data, dtype='float32')), 0)
outputs = self.get_pred(image_data).numpy()
#--------------------------------------------------------------------------------------------#
# centernet后处理的过程，包括门限判断和传统非极大抑制。
# 对于centernet网络来讲，确立中心非常重要。对于大目标而言，会存在许多的局部信息。
# 此时大目标中心点比较难以确定。使用最大池化的非极大抑制方法无法去除局部框
# 这里面存在传统的nms处理方法，可以选择关闭和开启。
# 实际测试中，hourglass为主干网络时有无额外的nms相差不大，resnet相差较大。BBoxUtility
#--------------------------------------------------------------------------------------------#
results = self.bbox_util.postprocess(outputs, self.nms, image_shape, self.input_shape, self.letterbox_image, confidence=self.confidence)
#--------------------------------------#
# 如果没有检测到物体，则返回原图
#--------------------------------------#
if results[0] is None:
return image
top_label = np.array(results[0][:, 5], dtype = 'int32')
top_conf = results[0][:, 4]
top_boxes = results[0][:, :4]
#---------------------------------------------------------#
# 设置字体与边框厚度
#---------------------------------------------------------#
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(image)[0] + np.shape(image)[1]) // self.input_shape[0], 1)
#---------------------------------------------------------#
# 图像绘制
#---------------------------------------------------------#
for i, c in list(enumerate(top_label)):
predicted_class = self.class_names[int(c)]
box = top_boxes[i]
score = top_conf[i]
top, left, bottom, right = box
top = max(0, np.floor(top).astype('int32'))
left = max(0, np.floor(left).astype('int32'))
bottom = min(image.size[1], np.floor(bottom).astype('int32'))
right = min(image.size[0], np.floor(right).astype('int32'))
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[c])
draw.rectangle([tuple(text_origin), tuple(text_origin + label_size)], fill=self.colors[c])
draw.text(text_origin, str(label, 'UTF-8'), fill=(0, 0, 0), font=font)
del draw
return image
def get_FPS(self, image, test_interval):
# ---------------------------------------------------#
# 获得输入图片的高和宽
# ---------------------------------------------------#
image_shape = np.array(np.shape(image)[0:2])
# ---------------------------------------------------------#
# 在这里将图像转换成RGB图像，防止灰度图在预测时报错。
# 代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
# ---------------------------------------------------------#
image = cvtColor(image)
# ---------------------------------------------------------#
# 给图像增加灰条，实现不失真的resize
# 也可以直接resize进行识别
# ---------------------------------------------------------#
image_data = resize_image(image, (self.input_shape[1], self.input_shape[0]), self.letterbox_image)
# ---------------------------------------------------------#
# 添加上batch_size维度，图片预处理，归一化。
# ---------------------------------------------------------#
image_data = np.expand_dims(preprocess_input(np.array(image_data, dtype='float32')), 0)
outputs = self.get_pred(image_data).numpy()
# --------------------------------------------------------------------------------------------#
# centernet后处理的过程，包括门限判断和传统非极大抑制。
# 对于centernet网络来讲，确立中心非常重要。对于大目标而言，会存在许多的局部信息。
# 此时大目标中心点比较难以确定。使用最大池化的非极大抑制方法无法去除局部框
# 这里面存在传统的nms处理方法，可以选择关闭和开启。
# 实际测试中，hourglass为主干网络时有无额外的nms相差不大，resnet相差较大。
# --------------------------------------------------------------------------------------------#
results = self.bbox_util.postprocess(outputs, self.nms, image_shape, self.input_shape, self.letterbox_image,
confidence=self.confidence)
t1 = time.time()
for _ in range(test_interval):
outputs = self.get_pred(image_data).numpy()
# --------------------------------------------------------------------------------------------#
# centernet后处理的过程，包括门限判断和传统非极大抑制。
# 对于centernet网络来讲，确立中心非常重要。对于大目标而言，会存在许多的局部信息。
# 此时大目标中心点比较难以确定。使用最大池化的非极大抑制方法无法去除局部框
# 这里面存在传统的nms处理方法，可以选择关闭和开启。
# 实际测试中，hourglass为主干网络时有无额外的nms相差不大，resnet相差较大。
# --------------------------------------------------------------------------------------------#
results = self.bbox_util.postprocess(outputs, self.nms, image_shape, self.input_shape, self.letterbox_image,
confidence=self.confidence)
t2 = time.time()
tact_time = (t2 - t1) / test_interval
return tact_time
def get_map_txt(self, image_id, image, class_names, map_out_path):
f = open(os.path.join(map_out_path, "detection-results/" + image_id + ".txt"), "w")
# ---------------------------------------------------#
# 获得输入图片的高和宽
# ---------------------------------------------------#
image_shape = np.array(np.shape(image)[0:2])
# ---------------------------------------------------------#
# 在这里将图像转换成RGB图像，防止灰度图在预测时报错。
# 代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
# ---------------------------------------------------------#
image = cvtColor(image)
# ---------------------------------------------------------#
# 给图像增加灰条，实现不失真的resize
# 也可以直接resize进行识别
# ---------------------------------------------------------#
image_data = resize_image(image, (self.input_shape[1], self.input_shape[0]), self.letterbox_image)
# ---------------------------------------------------------#
# 添加上batch_size维度，图片预处理，归一化。
# ---------------------------------------------------------#
image_data = np.expand_dims(preprocess_input(np.array(image_data, dtype='float32')), 0)
outputs = self.get_pred(image_data).numpy()
# --------------------------------------------------------------------------------------------#
# centernet后处理的过程，包括门限判断和传统非极大抑制。
# 对于centernet网络来讲，确立中心非常重要。对于大目标而言，会存在许多的局部信息。
# 此时大目标中心点比较难以确定。使用最大池化的非极大抑制方法无法去除局部框
# 这里面存在传统的nms处理方法，可以选择关闭和开启。
# 实际测试中，hourglass为主干网络时有无额外的nms相差不大，resnet相差较大。
# --------------------------------------------------------------------------------------------#
results = self.bbox_util.postprocess(outputs, self.nms, image_shape, self.input_shape, self.letterbox_image,
confidence=self.confidence)
# --------------------------------------#
# 如果没有检测到物体，则返回原图
# --------------------------------------#
if results[0] is None:
return
top_label = np.array(results[0][:, 5], dtype='int32')
top_conf = results[0][:, 4]
top_boxes = results[0][:, :4]
for i, c in list(enumerate(top_label)):
predicted_class = self.class_names[int(c)]
box = top_boxes[i]
score = str(top_conf[i])
top, left, bottom, right = box
if predicted_class not in class_names:
continue
f.write("%s %s %s %s %s %s\n" % (
predicted_class, score[:6], str(int(left)), str(int(top)), str(int(right)), str(int(bottom))))
f.close()
return
