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")
#---------------------------------------------------------#
# 在这里将图像转换成RGB图像，防止灰度图在预测时报错。
#---------------------------------------------------------#
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)
out_boxes, out_scores, out_classes = self.sess.run(
[self.boxes, self.scores, self.classes],
feed_dict={
self.yolo_model.input: image_data,
self.input_image_shape: [image.size[1], image.size[0]],
K.learning_phase(): 0
})
for i, c in enumerate(out_classes):
predicted_class = self.class_names[int(c)]
score = str(out_scores[i])
top, left, bottom, right = out_boxes[i]
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
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_FPS(self, image, test_interval):
#---------------------------------------------------------#
# 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)
#---------------------------------------------------------#
# 将图像输入网络当中进行预测！
#---------------------------------------------------------#
input_image_shape = np.expand_dims(np.array([image.size[1], image.size[0]], dtype='float32'), 0)
out_boxes, out_scores, out_classes = self.get_pred(image_data, input_image_shape)
t1 = time.time()
for _ in range(test_interval):
out_boxes, out_scores, out_classes = self.get_pred(image_data, input_image_shape)
t2 = time.time()
tact_time = (t2 - t1) / test_interval
return tact_time
def get_FPS(self, image, test_interval):
#---------------------------------------------------------#
# 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, nw, nh = resize_image(
image, (self.input_shape[1], self.input_shape[0]))
#---------------------------------------------------------#
# 添加上batch_size维度
#---------------------------------------------------------#
image_data = np.expand_dims(
np.transpose(preprocess_input(np.array(image_data, np.float32)),
(2, 0, 1)), 0)
with torch.no_grad():
images = torch.from_numpy(image_data)
if self.cuda:
images = images.cuda()
#---------------------------------------------------#
# 图片传入网络进行预测
#---------------------------------------------------#
pr = self.net(images)[0]
#---------------------------------------------------#
# 取出每一个像素点的种类
#---------------------------------------------------#
pr = F.softmax(pr.permute(1, 2, 0),
dim=-1).cpu().numpy().argmax(axis=-1)
#--------------------------------------#
# 将灰条部分截取掉
#--------------------------------------#
pr = pr[int((self.input_shape[0] - nh) // 2) : int((self.input_shape[0] - nh) // 2 + nh), \
int((self.input_shape[1] - nw) // 2) : int((self.input_shape[1] - nw) // 2 + nw)]
t1 = time.time()
for _ in range(test_interval):
with torch.no_grad():
#---------------------------------------------------#
# 图片传入网络进行预测
#---------------------------------------------------#
pr = self.net(images)[0]
#---------------------------------------------------#
# 取出每一个像素点的种类
#---------------------------------------------------#
pr = F.softmax(pr.permute(1, 2, 0),
dim=-1).cpu().numpy().argmax(axis=-1)
#--------------------------------------#
# 将灰条部分截取掉
#--------------------------------------#
pr = pr[int((self.input_shape[0] - nh) // 2) : int((self.input_shape[0] - nh) // 2 + nh), \
int((self.input_shape[1] - nw) // 2) : int((self.input_shape[1] - nw) // 2 + nw)]
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.m2det.predict(image_data)
#-----------------------------------------------------------#
# 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
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.centernet.predict(image_data)
#--------------------------------------------------------------------------------------------#
# 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.centernet.predict(image_data)
#--------------------------------------------------------------------------------------------#
# 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
def detect_heatmap(self, image, heatmap_save_path):
import cv2
import matplotlib.pyplot as plt
def sigmoid(x):
y = 1.0 / (1.0 + np.exp(-x))
return y
#---------------------------------------------------------#
# 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.yolo_model.predict(image_data)
plt.imshow(image, alpha=1)
plt.axis('off')
mask = np.zeros((image.size[1], image.size[0]))
for sub_output in output:
b, h, w, c = np.shape(sub_output)
sub_output = np.reshape(sub_output, [b, h, w, 3, -1])[0]
score = np.max(sigmoid(sub_output[..., 4]), -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")
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():
images = torch.from_numpy(image_data)
if self.cuda:
images = images.cuda()
#---------------------------------------------------------#
# 传入网络当中进行预测
#---------------------------------------------------------#
_, regression, classification, anchors = self.net(images)
#-----------------------------------------------------------#
# 将预测结果进行解码
#-----------------------------------------------------------#
outputs = decodebox(regression, anchors, self.input_shape)
results = non_max_suppression(torch.cat([outputs, classification], axis=-1), self.input_shape,
image_shape, self.letterbox_image, conf_thres = self.confidence, nms_thres = self.nms_iou)
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
def get_FPS(self, image, test_interval):
#---------------------------------------------------------#
# 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
#---------------------------------------------------------#
image_data, nw, nh = resize_image(
image, (self.input_shape[1], self.input_shape[0]))
#---------------------------------------------------------#
# 归一化+添加上batch_size维度
#---------------------------------------------------------#
image_data = np.expand_dims(
preprocess_input(np.array(image_data, np.float32)), 0)
#---------------------------------------------------#
# 图片传入网络进行预测
#---------------------------------------------------#
pr = self.model.predict(image_data)[0]
#--------------------------------------#
# 将灰条部分截取掉
#--------------------------------------#
pr = pr[int((self.input_shape[0] - nh) // 2) : int((self.input_shape[0] - nh) // 2 + nh), \
int((self.input_shape[1] - nw) // 2) : int((self.input_shape[1] - nw) // 2 + nw)]
#---------------------------------------------------#
# 取出每一个像素点的种类
#---------------------------------------------------#
pr = pr.argmax(axis=-1).reshape(
[self.input_shape[0], self.input_shape[1]])
t1 = time.time()
for _ in range(test_interval):
#---------------------------------------------------#
# 图片传入网络进行预测
#---------------------------------------------------#
pr = self.model.predict(image_data)[0]
#--------------------------------------#
# 将灰条部分截取掉
#--------------------------------------#
pr = pr[int((self.input_shape[0] - nh) // 2) : int((self.input_shape[0] - nh) // 2 + nh), \
int((self.input_shape[1] - nw) // 2) : int((self.input_shape[1] - nw) // 2 + nw)]
#---------------------------------------------------#
# 取出每一个像素点的种类
#---------------------------------------------------#
pr = pr.argmax(axis=-1).reshape(
[self.input_shape[0], self.input_shape[1]])
t2 = time.time()
tact_time = (t2 - t1) / test_interval
return tact_time
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 = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0)
with torch.no_grad():
images = torch.from_numpy(image_data)
if self.cuda:
images = images.cuda()
#---------------------------------------------------------#
# 传入网络当中进行预测
#---------------------------------------------------------#
_, regression, classification, anchors = self.net(images)
#-----------------------------------------------------------#
# 将预测结果进行解码
#-----------------------------------------------------------#
outputs = decodebox(regression, anchors, self.input_shape)
results = non_max_suppression(torch.cat([outputs, classification], axis=-1), self.input_shape,
image_shape, self.letterbox_image, conf_thres = self.confidence, nms_thres = self.nms_iou)
t1 = time.time()
for _ in range(test_interval):
with torch.no_grad():
#---------------------------------------------------------#
# 传入网络当中进行预测
#---------------------------------------------------------#
_, regression, classification, anchors = self.net(images)
#-----------------------------------------------------------#
# 将预测结果进行解码
#-----------------------------------------------------------#
outputs = decodebox(regression, anchors, self.input_shape)
results = non_max_suppression(torch.cat([outputs, classification], axis=-1), self.input_shape,
image_shape, self.letterbox_image, conf_thres = self.confidence, nms_thres = self.nms_iou)
t2 = time.time()
tact_time = (t2 - t1) / test_interval
return tact_time
def get_miou_png(self, image):
#---------------------------------------------------------#
# 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)
orininal_h = np.array(image).shape[0]
orininal_w = np.array(image).shape[1]
#---------------------------------------------------------#
# 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, nw, nh = resize_image(
image, (self.input_shape[1], self.input_shape[0]))
#---------------------------------------------------------#
# 添加上batch_size维度
#---------------------------------------------------------#
image_data = np.expand_dims(
np.transpose(preprocess_input(np.array(image_data, np.float32)),
(2, 0, 1)), 0)
with torch.no_grad():
images = torch.from_numpy(image_data)
if self.cuda:
images = images.cuda()
#---------------------------------------------------#
# 图片传入网络进行预测
#---------------------------------------------------#
pr = self.net(images)[0]
#---------------------------------------------------#
# 取出每一个像素点的种类
#---------------------------------------------------#
pr = F.softmax(pr.permute(1, 2, 0), dim=-1).cpu().numpy()
#--------------------------------------#
# 将灰条部分截取掉
#--------------------------------------#
pr = pr[int((self.input_shape[0] - nh) // 2) : int((self.input_shape[0] - nh) // 2 + nh), \
int((self.input_shape[1] - nw) // 2) : int((self.input_shape[1] - nw) // 2 + nw)]
#---------------------------------------------------#
# 进行图片的resize
#---------------------------------------------------#
pr = cv2.resize(pr, (orininal_w, orininal_h),
interpolation=cv2.INTER_LINEAR)
#---------------------------------------------------#
# 取出每一个像素点的种类
#---------------------------------------------------#
pr = pr.argmax(axis=-1)
image = Image.fromarray(np.uint8(pr))
return image
def detect_heatmap(self, image, heatmap_save_path):
import cv2
import matplotlib.pyplot as plt
def sigmoid(x):
y = 1.0 / (1.0 + np.exp(-x))
return y
#---------------------------------------------------------#
# 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
#---------------------------------------------------------#
image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0)
with torch.no_grad():
images = torch.from_numpy(image_data)
if self.cuda:
images = images.cuda()
#---------------------------------------------------------#
# 将图像输入网络当中进行预测！
#---------------------------------------------------------#
outputs = self.net(images)
plt.imshow(image, alpha=1)
plt.axis('off')
mask = np.zeros((image.size[1], image.size[0]))
for sub_output in outputs:
sub_output = sub_output.cpu().numpy()
b, c, h, w = np.shape(sub_output)
sub_output = np.transpose(np.reshape(sub_output, [b, 3, -1, h, w]), [0, 3, 4, 1, 2])[0]
score = np.max(sigmoid(sub_output[..., 4]), -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_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, image_metas, windows = resize_image([np.array(image)],
self.config)
#---------------------------------------------------------#
# 根据当前输入图像的大小，生成先验框
#---------------------------------------------------------#
anchors = np.expand_dims(get_anchors(self.config, image_data[0].shape),
0)
#---------------------------------------------------------#
# 将图像输入网络当中进行预测！
#---------------------------------------------------------#
detections, _, _, mrcnn_mask, _, _, _ = self.model.predict(
[image_data, image_metas, anchors], verbose=0)
#---------------------------------------------------#
# 上面获得的预测结果是相对于padding后的图片的
# 我们需要将预测结果转换到原图上
#---------------------------------------------------#
box_thre, class_thre, class_ids, masks_arg, masks_sigmoid = postprocess(
detections[0], mrcnn_mask[0], image_shape, image_data[0].shape,
windows[0])
t1 = time.time()
for _ in range(test_interval):
#---------------------------------------------------------#
# 将图像输入网络当中进行预测！
#---------------------------------------------------------#
detections, _, _, mrcnn_mask, _, _, _ = self.model.predict(
[image_data, image_metas, anchors], verbose=0)
#---------------------------------------------------#
# 上面获得的预测结果是相对于padding后的图片的
# 我们需要将预测结果转换到原图上
#---------------------------------------------------#
box_thre, class_thre, class_ids, masks_arg, masks_sigmoid = postprocess(
detections[0], mrcnn_mask[0], image_shape, image_data[0].shape,
windows[0])
t2 = time.time()
tact_time = (t2 - t1) / test_interval
return tact_time
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
#---------------------------------------------------------#
image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0)
with torch.no_grad():
images = torch.from_numpy(image_data)
if self.cuda:
images = images.cuda()
#---------------------------------------------------------#
# 将图像输入网络当中进行预测！
#---------------------------------------------------------#
outputs = self.net(images)
outputs = self.bbox_util.decode_box(outputs)
#---------------------------------------------------------#
# 将预测框进行堆叠，然后进行非极大抑制
#---------------------------------------------------------#
results = self.bbox_util.non_max_suppression(torch.cat(outputs, 1), self.num_classes, self.input_shape,
image_shape, self.letterbox_image, conf_thres=self.confidence, nms_thres=self.nms_iou)
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)
outputs = self.bbox_util.decode_box(outputs)
#---------------------------------------------------------#
# 将预测框进行堆叠，然后进行非极大抑制
#---------------------------------------------------------#
results = self.bbox_util.non_max_suppression(torch.cat(outputs, 1), self.num_classes, self.input_shape,
image_shape, self.letterbox_image, conf_thres=self.confidence, nms_thres=self.nms_iou)
t2 = time.time()
tact_time = (t2 - t1) / test_interval
return tact_time
def detect_image(self, image_id, image, results, clsid2catid):
#---------------------------------------------------------#
# 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)
#---------------------------------------------------------#
# 将图像输入网络当中进行预测！
#---------------------------------------------------------#
input_image_shape = np.expand_dims(
np.array([image.size[1], image.size[0]], dtype='float32'), 0)
out_boxes, out_scores, out_classes = self.yolo_model.predict(
[image_data, input_image_shape])
for i, c in enumerate(out_classes):
result = {}
top, left, bottom, right = out_boxes[i]
result["image_id"] = int(image_id)
result["category_id"] = clsid2catid[c]
result["bbox"] = [
float(left),
float(top),
float(right - left),
float(bottom - top)
]
result["score"] = float(out_scores[i])
results.append(result)
return results
def detect_image(self, image):
#---------------------------------------------------------#
# 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)
#---------------------------------------------------#
# 对图片进行不失真的resize
#---------------------------------------------------#
image_data = letterbox_image(
image, [self.input_shape[1], self.input_shape[0]])
#---------------------------------------------------------#
# 归一化+添加上batch_size维度+转置
#---------------------------------------------------------#
image_data = np.transpose(
np.expand_dims(preprocess_input(np.array(image_data, np.float32)),
0), (0, 3, 1, 2))
with torch.no_grad():
photo = torch.from_numpy(image_data)
if self.cuda:
photo = photo.cuda()
#---------------------------------------------------#
# 图片传入网络进行预测
#---------------------------------------------------#
preds = torch.softmax(self.model(photo)[0], dim=-1).cpu().numpy()
#---------------------------------------------------#
# 获得所属种类
#---------------------------------------------------#
class_name = self.class_names[np.argmax(preds)]
probability = np.max(preds)
#---------------------------------------------------#
# 绘图并写字
#---------------------------------------------------#
plt.subplot(1, 1, 1)
plt.imshow(np.array(image))
plt.title('Class:%s Probability:%.3f' % (class_name, probability))
plt.show()
return class_name
def get_miou_png(self, image):
#---------------------------------------------------------#
# 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)
orininal_h = np.array(image).shape[0]
orininal_w = np.array(image).shape[1]
#---------------------------------------------------------#
# 给图像增加灰条，实现不失真的resize
#---------------------------------------------------------#
image_data, nw, nh = resize_image(
image, (self.input_shape[1], self.input_shape[0]))
#---------------------------------------------------------#
# 归一化+添加上batch_size维度
#---------------------------------------------------------#
image_data = np.expand_dims(
preprocess_input(np.array(image_data, np.float32)), 0)
#--------------------------------------#
# 图片传入网络进行预测
#--------------------------------------#
pr = self.model.predict(image_data)[0]
#--------------------------------------#
# 将灰条部分截取掉
#--------------------------------------#
pr = pr[int((self.input_shape[0] - nh) // 2) : int((self.input_shape[0] - nh) // 2 + nh), \
int((self.input_shape[1] - nw) // 2) : int((self.input_shape[1] - nw) // 2 + nw)]
#--------------------------------------#
# 进行图片的resize
#--------------------------------------#
pr = cv2.resize(pr, (orininal_w, orininal_h),
interpolation=cv2.INTER_LINEAR)
#---------------------------------------------------#
# 取出每一个像素点的种类
#---------------------------------------------------#
pr = pr.argmax(axis=-1)
image = Image.fromarray(np.uint8(pr))
return image
def get_map_out(self, image):
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, image_metas, windows = resize_image([np.array(image)],
self.config)
#---------------------------------------------------------#
# 根据当前输入图像的大小，生成先验框
#---------------------------------------------------------#
anchors = np.expand_dims(get_anchors(self.config, image_data[0].shape),
0)
#---------------------------------------------------------#
# 将图像输入网络当中进行预测！
#---------------------------------------------------------#
detections, _, _, mrcnn_mask, _, _, _ = self.model.predict(
[image_data, image_metas, anchors], verbose=0)
#---------------------------------------------------#
# 上面获得的预测结果是相对于padding后的图片的
# 我们需要将预测结果转换到原图上
#---------------------------------------------------#
box_thre, class_thre, class_ids, masks_arg, masks_sigmoid = postprocess(
detections[0], mrcnn_mask[0], image_shape, image_data[0].shape,
windows[0])
outboxes = None
if box_thre is not None:
outboxes = np.zeros_like(box_thre)
outboxes[:, [0, 2]] = box_thre[:, [1, 3]]
outboxes[:, [1, 3]] = box_thre[:, [0, 2]]
return outboxes, class_thre, class_ids, masks_arg, masks_sigmoid
def get_random_data_with_Mosaic(self,
annotation_line,
input_shape,
max_boxes=100,
hue=.1,
sat=1.5,
val=1.5):
h, w = input_shape
min_offset_x = self.rand(0.25, 0.75)
min_offset_y = self.rand(0.25, 0.75)
nws = [
int(w * self.rand(0.4, 1)),
int(w * self.rand(0.4, 1)),
int(w * self.rand(0.4, 1)),
int(w * self.rand(0.4, 1))
]
nhs = [
int(h * self.rand(0.4, 1)),
int(h * self.rand(0.4, 1)),
int(h * self.rand(0.4, 1)),
int(h * self.rand(0.4, 1))
]
place_x = [
int(w * min_offset_x) - nws[0],
int(w * min_offset_x) - nws[1],
int(w * min_offset_x),
int(w * min_offset_x)
]
place_y = [
int(h * min_offset_y) - nhs[0],
int(h * min_offset_y),
int(h * min_offset_y),
int(h * min_offset_y) - nhs[3]
]
image_datas = []
box_datas = []
index = 0
for line in annotation_line:
# 每一行进行分割
line_content = line.split()
# 打开图片
image = Image.open(line_content[0])
image = cvtColor(image)
# 图片的大小
iw, ih = image.size
# 保存框的位置
box = np.array([
np.array(list(map(int, box.split(','))))
for box in line_content[1:]
])
# 是否翻转图片
flip = self.rand() < .5
if flip and len(box) > 0:
image = image.transpose(Image.FLIP_LEFT_RIGHT)
box[:, [0, 2]] = iw - box[:, [2, 0]]
nw = nws[index]
nh = nhs[index]
image = image.resize((nw, nh), Image.BICUBIC)
# 将图片进行放置，分别对应四张分割图片的位置
dx = place_x[index]
dy = place_y[index]
new_image = Image.new('RGB', (w, h), (128, 128, 128))
new_image.paste(image, (dx, dy))
image_data = np.array(new_image)
index = index + 1
box_data = []
# 对box进行重新处理
if len(box) > 0:
np.random.shuffle(box)
box[:, [0, 2]] = box[:, [0, 2]] * nw / iw + dx
box[:, [1, 3]] = box[:, [1, 3]] * nh / ih + dy
box[:, 0:2][box[:, 0:2] < 0] = 0
box[:, 2][box[:, 2] > w] = w
box[:, 3][box[:, 3] > h] = h
box_w = box[:, 2] - box[:, 0]
box_h = box[:, 3] - box[:, 1]
box = box[np.logical_and(box_w > 1, box_h > 1)]
box_data = np.zeros((len(box), 5))
box_data[:len(box)] = box
image_datas.append(image_data)
box_datas.append(box_data)
# 将图片分割，放在一起
cutx = int(w * min_offset_x)
cuty = int(h * min_offset_y)
new_image = np.zeros([h, w, 3])
new_image[:cuty, :cutx, :] = image_datas[0][:cuty, :cutx, :]
new_image[cuty:, :cutx, :] = image_datas[1][cuty:, :cutx, :]
new_image[cuty:, cutx:, :] = image_datas[2][cuty:, cutx:, :]
new_image[:cuty, cutx:, :] = image_datas[3][:cuty, cutx:, :]
# 进行色域变换
hue = self.rand(-hue, hue)
sat = self.rand(1, sat) if self.rand() < .5 else 1 / self.rand(1, sat)
val = self.rand(1, val) if self.rand() < .5 else 1 / self.rand(1, val)
x = cv2.cvtColor(np.array(new_image / 255, np.float32),
cv2.COLOR_RGB2HSV)
x[..., 0] += hue * 360
x[..., 0][x[..., 0] > 1] -= 1
x[..., 0][x[..., 0] < 0] += 1
x[..., 1] *= sat
x[..., 2] *= val
x[x[:, :, 0] > 360, 0] = 360
x[:, :, 1:][x[:, :, 1:] > 1] = 1
x[x < 0] = 0
new_image = cv2.cvtColor(x, cv2.COLOR_HSV2RGB) * 255
# 对框进行进一步的处理
new_boxes = self.merge_bboxes(box_datas, cutx, cuty)
return new_image, new_boxes
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
def detect_image(self, image):
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)
image_origin = np.array(image, np.uint8)
#---------------------------------------------------------#
# 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, image_metas, windows = resize_image([np.array(image)],
self.config)
#---------------------------------------------------------#
# 根据当前输入图像的大小，生成先验框
#---------------------------------------------------------#
anchors = np.expand_dims(get_anchors(self.config, image_data[0].shape),
0)
#---------------------------------------------------------#
# 将图像输入网络当中进行预测！
#---------------------------------------------------------#
detections, _, _, mrcnn_mask, _, _, _ = self.model.predict(
[image_data, image_metas, anchors], verbose=0)
#---------------------------------------------------#
# 上面获得的预测结果是相对于padding后的图片的
# 我们需要将预测结果转换到原图上
#---------------------------------------------------#
box_thre, class_thre, class_ids, masks_arg, masks_sigmoid = postprocess(
detections[0], mrcnn_mask[0], image_shape, image_data[0].shape,
windows[0])
if box_thre is None:
return image
#----------------------------------------------------------------------#
# masks_class [image_shape[0], image_shape[1]]
# 根据每个像素点所属的实例和是否满足门限需求，判断每个像素点的种类
#----------------------------------------------------------------------#
masks_class = masks_sigmoid * (class_ids[None, None, :] + 1)
masks_class = np.reshape(masks_class,
[-1, np.shape(masks_sigmoid)[-1]])
masks_class = np.reshape(
masks_class[np.arange(np.shape(masks_class)[0]),
np.reshape(masks_arg, [-1])],
[image_shape[0], image_shape[1]])
#---------------------------------------------------------#
# 设置字体与边框厚度
#---------------------------------------------------------#
scale = 0.6
thickness = int(
max((image.size[0] + image.size[1]) // self.IMAGE_MAX_DIM, 1))
font = cv2.FONT_HERSHEY_DUPLEX
color_masks = self.colors[masks_class].astype('uint8')
image_fused = cv2.addWeighted(color_masks,
0.4,
image_origin,
0.6,
gamma=0)
for i in range(np.shape(class_ids)[0]):
top, left, bottom, right = np.array(box_thre[i, :], np.int32)
#---------------------------------------------------------#
# 获取颜色并绘制预测框
#---------------------------------------------------------#
color = self.colors[class_ids[i] + 1].tolist()
cv2.rectangle(image_fused, (left, top), (right, bottom), color,
thickness)
#---------------------------------------------------------#
# 获得这个框的种类并写在图片上
#---------------------------------------------------------#
class_name = self.class_names[class_ids[i]]
print(class_name, top, left, bottom, right)
text_str = f'{class_name}: {class_thre[i]:.2f}'
text_w, text_h = cv2.getTextSize(text_str, font, scale, 1)[0]
cv2.rectangle(image_fused, (left, top),
(left + text_w, top + text_h + 5), color, -1)
cv2.putText(image_fused, text_str, (left, top + 15), font, scale,
(255, 255, 255), 1, cv2.LINE_AA)
image = Image.fromarray(np.uint8(image_fused))
return image
def detect_image(self, image):
#---------------------------------------------------#
# 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
#---------------------------------------------------------#
image_data = np.expand_dims(
np.transpose(
preprocess_input(np.array(image_data, dtype='float32')),
(2, 0, 1)), 0)
with torch.no_grad():
images = torch.from_numpy(image_data)
if self.cuda:
images = images.cuda()
#---------------------------------------------------------#
# 将图像输入网络当中进行预测！
#---------------------------------------------------------#
outputs = self.net(images)
outputs = self.bbox_util.decode_box(outputs)
#---------------------------------------------------------#
# 将预测框进行堆叠，然后进行非极大抑制
#---------------------------------------------------------#
results = self.bbox_util.non_max_suppression(
torch.cat(outputs, 1),
self.num_classes,
self.input_shape,
image_shape,
self.letterbox_image,
conf_thres=self.confidence,
nms_thres=self.nms_iou)
if results[0] is None:
return image
top_label = np.array(results[0][:, 6], dtype='int32')
top_conf = results[0][:, 4] * 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 * image.size[1] +
0.5).astype('int32'))
thickness = int(
max((image.size[0] + image.size[1]) // np.mean(self.input_shape),
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 detect_image(self, image):
#---------------------------------------------------------#
# 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)
#---------------------------------------------------#
# 对输入图像进行一个备份，后面用于绘图
#---------------------------------------------------#
old_img = copy.deepcopy(image)
orininal_h = np.array(image).shape[0]
orininal_w = np.array(image).shape[1]
#---------------------------------------------------------#
# 给图像增加灰条，实现不失真的resize
#---------------------------------------------------------#
image_data, nw, nh = resize_image(
image, (self.input_shape[1], self.input_shape[0]))
#---------------------------------------------------------#
# 归一化+添加上batch_size维度
#---------------------------------------------------------#
image_data = np.expand_dims(
preprocess_input(np.array(image_data, np.float32)), 0)
#---------------------------------------------------#
# 图片传入网络进行预测
#---------------------------------------------------#
pr = self.model.predict(image_data)[0]
#---------------------------------------------------#
# 将灰条部分截取掉
#---------------------------------------------------#
pr = pr[int((self.input_shape[0] - nh) // 2) : int((self.input_shape[0] - nh) // 2 + nh), \
int((self.input_shape[1] - nw) // 2) : int((self.input_shape[1] - nw) // 2 + nw)]
#---------------------------------------------------#
# 进行图片的resize
#---------------------------------------------------#
pr = cv2.resize(pr, (orininal_w, orininal_h),
interpolation=cv2.INTER_LINEAR)
#---------------------------------------------------#
# 取出每一个像素点的种类
#---------------------------------------------------#
pr = pr.argmax(axis=-1)
if self.mix_type == 0:
# seg_img = np.zeros((np.shape(pr)[0], np.shape(pr)[1], 3))
# for c in range(self.num_classes):
# seg_img[:, :, 0] += ((pr[:, :] == c ) * self.colors[c][0]).astype('uint8')
# seg_img[:, :, 1] += ((pr[:, :] == c ) * self.colors[c][1]).astype('uint8')
# seg_img[:, :, 2] += ((pr[:, :] == c ) * self.colors[c][2]).astype('uint8')
seg_img = np.reshape(
np.array(self.colors, np.uint8)[np.reshape(pr, [-1])],
[orininal_h, orininal_w, -1])
#------------------------------------------------#
# 将新图片转换成Image的形式
#------------------------------------------------#
image = Image.fromarray(np.uint8(seg_img))
#------------------------------------------------#
# 将新图与原图及进行混合
#------------------------------------------------#
image = Image.blend(old_img, image, 0.7)
elif self.mix_type == 1:
# seg_img = np.zeros((np.shape(pr)[0], np.shape(pr)[1], 3))
# for c in range(self.num_classes):
# seg_img[:, :, 0] += ((pr[:, :] == c ) * self.colors[c][0]).astype('uint8')
# seg_img[:, :, 1] += ((pr[:, :] == c ) * self.colors[c][1]).astype('uint8')
# seg_img[:, :, 2] += ((pr[:, :] == c ) * self.colors[c][2]).astype('uint8')
seg_img = np.reshape(
np.array(self.colors, np.uint8)[np.reshape(pr, [-1])],
[orininal_h, orininal_w, -1])
#------------------------------------------------#
# 将新图片转换成Image的形式
#------------------------------------------------#
image = Image.fromarray(np.uint8(seg_img))
elif self.mix_type == 2:
seg_img = (np.expand_dims(pr != 0, -1) *
np.array(old_img, np.float32)).astype('uint8')
#------------------------------------------------#
# 将新图片转换成Image的形式
#------------------------------------------------#
image = Image.fromarray(np.uint8(seg_img))
return image
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.centernet.predict(image_data)
#--------------------------------------------------------------------------------------------#
# 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 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 detect_image(self, image, crop = False, count = False):
#---------------------------------------------------------#
# 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)
#---------------------------------------------------------#
# 将图像输入网络当中进行预测！
#---------------------------------------------------------#
out_boxes, out_scores, out_classes = self.sess.run(
[self.boxes, self.scores, self.classes],
feed_dict={
self.yolo_model.input: image_data,
self.input_image_shape: [image.size[1], image.size[0]],
K.learning_phase(): 0})
print('Found {} boxes for {}'.format(len(out_boxes), 'img'))
#---------------------------------------------------------#
# 设置字体与边框厚度
#---------------------------------------------------------#
font = ImageFont.truetype(font='model_data/simhei.ttf', size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))
thickness = int(max((image.size[0] + image.size[1]) // np.mean(self.input_shape), 1))
#---------------------------------------------------------#
# 计数
#---------------------------------------------------------#
if count:
print("top_label:", out_classes)
classes_nums = np.zeros([self.num_classes])
for i in range(self.num_classes):
num = np.sum(out_classes == 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(out_boxes)):
top, left, bottom, right = out_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(out_classes)):
predicted_class = self.class_names[int(c)]
box = out_boxes[i]
score = out_scores[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_random_data(self,
annotation_line,
input_shape,
jitter=.3,
hue=.1,
sat=1.5,
val=1.5,
random=True):
line = annotation_line.split()
#------------------------------#
# 读取图像并转换成RGB图像
#------------------------------#
image = Image.open(line[0])
image = cvtColor(image)
#------------------------------#
# 获得图像的高宽与目标高宽
#------------------------------#
iw, ih = image.size
h, w = input_shape
#------------------------------#
# 获得预测框
#------------------------------#
box = np.array(
[np.array(list(map(int, box.split(',')))) for box in line[1:]])
if not random:
scale = min(w / iw, h / ih)
nw = int(iw * scale)
nh = int(ih * scale)
dx = (w - nw) // 2
dy = (h - nh) // 2
#---------------------------------#
# 将图像多余的部分加上灰条
#---------------------------------#
image = image.resize((nw, nh), Image.BICUBIC)
new_image = Image.new('RGB', (w, h), (128, 128, 128))
new_image.paste(image, (dx, dy))
image_data = np.array(new_image, np.float32)
#---------------------------------#
# 对真实框进行调整
#---------------------------------#
if len(box) > 0:
np.random.shuffle(box)
box[:, [0, 2]] = box[:, [0, 2]] * nw / iw + dx
box[:, [1, 3]] = box[:, [1, 3]] * nh / ih + dy
box[:, 0:2][box[:, 0:2] < 0] = 0
box[:, 2][box[:, 2] > w] = w
box[:, 3][box[:, 3] > h] = h
box_w = box[:, 2] - box[:, 0]
box_h = box[:, 3] - box[:, 1]
box = box[np.logical_and(box_w > 1,
box_h > 1)] # discard invalid box
return image_data, box
#------------------------------------------#
# 对图像进行缩放并且进行长和宽的扭曲
#------------------------------------------#
new_ar = w / h * self.rand(1 - jitter, 1 + jitter) / self.rand(
1 - jitter, 1 + jitter)
scale = self.rand(.25, 2)
if new_ar < 1:
nh = int(scale * h)
nw = int(nh * new_ar)
else:
nw = int(scale * w)
nh = int(nw / new_ar)
image = image.resize((nw, nh), Image.BICUBIC)
#------------------------------------------#
# 将图像多余的部分加上灰条
#------------------------------------------#
dx = int(self.rand(0, w - nw))
dy = int(self.rand(0, h - nh))
new_image = Image.new('RGB', (w, h), (128, 128, 128))
new_image.paste(image, (dx, dy))
image = new_image
#------------------------------------------#
# 翻转图像
#------------------------------------------#
flip = self.rand() < .5
if flip: image = image.transpose(Image.FLIP_LEFT_RIGHT)
#------------------------------------------#
# 色域扭曲
#------------------------------------------#
hue = self.rand(-hue, hue)
sat = self.rand(1, sat) if self.rand() < .5 else 1 / self.rand(1, sat)
val = self.rand(1, val) if self.rand() < .5 else 1 / self.rand(1, val)
x = cv2.cvtColor(np.array(image, np.float32) / 255, cv2.COLOR_RGB2HSV)
x[..., 0] += hue * 360
x[..., 0][x[..., 0] > 1] -= 1
x[..., 0][x[..., 0] < 0] += 1
x[..., 1] *= sat
x[..., 2] *= val
x[x[:, :, 0] > 360, 0] = 360
x[:, :, 1:][x[:, :, 1:] > 1] = 1
x[x < 0] = 0
image_data = cv2.cvtColor(x, cv2.COLOR_HSV2RGB) * 255
#---------------------------------#
# 对真实框进行调整
#---------------------------------#
if len(box) > 0:
np.random.shuffle(box)
box[:, [0, 2]] = box[:, [0, 2]] * nw / iw + dx
box[:, [1, 3]] = box[:, [1, 3]] * nh / ih + dy
if flip: box[:, [0, 2]] = w - box[:, [2, 0]]
box[:, 0:2][box[:, 0:2] < 0] = 0
box[:, 2][box[:, 2] > w] = w
box[:, 3][box[:, 3] > h] = h
box_w = box[:, 2] - box[:, 0]
box_h = box[:, 3] - box[:, 1]
box = box[np.logical_and(box_w > 1, box_h > 1)]
return image_data, box
def get_random_data(self, annotation_line, input_shape, jitter=.3, hue=.1, sat=0.7, val=0.4, random=True):
line = annotation_line.split()
#------------------------------#
# 读取图像并转换成RGB图像
#------------------------------#
image = Image.open(line[0])
image = cvtColor(image)
#------------------------------#
# 获得图像的高宽与目标高宽
#------------------------------#
iw, ih = image.size
h, w = input_shape
#------------------------------#
# 获得预测框
#------------------------------#
box = np.array([np.array(list(map(int,box.split(',')))) for box in line[1:]])
if not random:
scale = min(w/iw, h/ih)
nw = int(iw*scale)
nh = int(ih*scale)
dx = (w-nw)//2
dy = (h-nh)//2
#---------------------------------#
# 将图像多余的部分加上灰条
#---------------------------------#
image = image.resize((nw,nh), Image.BICUBIC)
new_image = Image.new('RGB', (w,h), (128,128,128))
new_image.paste(image, (dx, dy))
image_data = np.array(new_image, np.float32)
#---------------------------------#
# 对真实框进行调整
#---------------------------------#
if len(box)>0:
np.random.shuffle(box)
box[:, [0,2]] = box[:, [0,2]]*nw/iw + dx
box[:, [1,3]] = box[:, [1,3]]*nh/ih + dy
box[:, 0:2][box[:, 0:2]<0] = 0
box[:, 2][box[:, 2]>w] = w
box[:, 3][box[:, 3]>h] = h
box_w = box[:, 2] - box[:, 0]
box_h = box[:, 3] - box[:, 1]
box = box[np.logical_and(box_w>1, box_h>1)] # discard invalid box
return image_data, box
#------------------------------------------#
# 对图像进行缩放并且进行长和宽的扭曲
#------------------------------------------#
new_ar = iw/ih * self.rand(1-jitter,1+jitter) / self.rand(1-jitter,1+jitter)
scale = self.rand(.25, 2)
if new_ar < 1:
nh = int(scale*h)
nw = int(nh*new_ar)
else:
nw = int(scale*w)
nh = int(nw/new_ar)
image = image.resize((nw,nh), Image.BICUBIC)
#------------------------------------------#
# 将图像多余的部分加上灰条
#------------------------------------------#
dx = int(self.rand(0, w-nw))
dy = int(self.rand(0, h-nh))
new_image = Image.new('RGB', (w,h), (128,128,128))
new_image.paste(image, (dx, dy))
image = new_image
#------------------------------------------#
# 翻转图像
#------------------------------------------#
flip = self.rand()<.5
if flip: image = image.transpose(Image.FLIP_LEFT_RIGHT)
image_data = np.array(image, np.uint8)
#---------------------------------#
# 对图像进行色域变换
# 计算色域变换的参数
#---------------------------------#
r = np.random.uniform(-1, 1, 3) * [hue, sat, val] + 1
#---------------------------------#
# 将图像转到HSV上
#---------------------------------#
hue, sat, val = cv2.split(cv2.cvtColor(image_data, cv2.COLOR_RGB2HSV))
dtype = image_data.dtype
#---------------------------------#
# 应用变换
#---------------------------------#
x = np.arange(0, 256, dtype=r.dtype)
lut_hue = ((x * r[0]) % 180).astype(dtype)
lut_sat = np.clip(x * r[1], 0, 255).astype(dtype)
lut_val = np.clip(x * r[2], 0, 255).astype(dtype)
image_data = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val)))
image_data = cv2.cvtColor(image_data, cv2.COLOR_HSV2RGB)
#---------------------------------#
# 对真实框进行调整
#---------------------------------#
if len(box)>0:
np.random.shuffle(box)
box[:, [0,2]] = box[:, [0,2]]*nw/iw + dx
box[:, [1,3]] = box[:, [1,3]]*nh/ih + dy
if flip: box[:, [0,2]] = w - box[:, [2,0]]
box[:, 0:2][box[:, 0:2]<0] = 0
box[:, 2][box[:, 2]>w] = w
box[:, 3][box[:, 3]>h] = h
box_w = box[:, 2] - box[:, 0]
box_h = box[:, 3] - box[:, 1]
box = box[np.logical_and(box_w>1, box_h>1)]
return image_data, box
def get_random_data_with_Mosaic(self,
annotation_line,
input_shape,
jitter=0.3,
hue=.1,
sat=0.7,
val=0.4):
h, w = input_shape
min_offset_x = self.rand(0.3, 0.7)
min_offset_y = self.rand(0.3, 0.7)
image_datas = []
box_datas = []
index = 0
for line in annotation_line:
#---------------------------------#
# 每一行进行分割
#---------------------------------#
line_content = line.split()
#---------------------------------#
# 打开图片
#---------------------------------#
image = Image.open(line_content[0])
image = cvtColor(image)
#---------------------------------#
# 图片的大小
#---------------------------------#
iw, ih = image.size
#---------------------------------#
# 保存框的位置
#---------------------------------#
box = np.array([
np.array(list(map(int, box.split(','))))
for box in line_content[1:]
])
#---------------------------------#
# 是否翻转图片
#---------------------------------#
flip = self.rand() < .5
if flip and len(box) > 0:
image = image.transpose(Image.FLIP_LEFT_RIGHT)
box[:, [0, 2]] = iw - box[:, [2, 0]]
#------------------------------------------#
# 对图像进行缩放并且进行长和宽的扭曲
#------------------------------------------#
new_ar = iw / ih * self.rand(1 - jitter, 1 + jitter) / self.rand(
1 - jitter, 1 + jitter)
scale = self.rand(.4, 1)
if new_ar < 1:
nh = int(scale * h)
nw = int(nh * new_ar)
else:
nw = int(scale * w)
nh = int(nw / new_ar)
image = image.resize((nw, nh), Image.BICUBIC)
#-----------------------------------------------#
# 将图片进行放置，分别对应四张分割图片的位置
#-----------------------------------------------#
if index == 0:
dx = int(w * min_offset_x) - nw
dy = int(h * min_offset_y) - nh
elif index == 1:
dx = int(w * min_offset_x) - nw
dy = int(h * min_offset_y)
elif index == 2:
dx = int(w * min_offset_x)
dy = int(h * min_offset_y)
elif index == 3:
dx = int(w * min_offset_x)
dy = int(h * min_offset_y) - nh
new_image = Image.new('RGB', (w, h), (128, 128, 128))
new_image.paste(image, (dx, dy))
image_data = np.array(new_image)
index = index + 1
box_data = []
#---------------------------------#
# 对box进行重新处理
#---------------------------------#
if len(box) > 0:
np.random.shuffle(box)
box[:, [0, 2]] = box[:, [0, 2]] * nw / iw + dx
box[:, [1, 3]] = box[:, [1, 3]] * nh / ih + dy
box[:, 0:2][box[:, 0:2] < 0] = 0
box[:, 2][box[:, 2] > w] = w
box[:, 3][box[:, 3] > h] = h
box_w = box[:, 2] - box[:, 0]
box_h = box[:, 3] - box[:, 1]
box = box[np.logical_and(box_w > 1, box_h > 1)]
box_data = np.zeros((len(box), 5))
box_data[:len(box)] = box
image_datas.append(image_data)
box_datas.append(box_data)
#---------------------------------#
# 将图片分割，放在一起
#---------------------------------#
cutx = int(w * min_offset_x)
cuty = int(h * min_offset_y)
new_image = np.zeros([h, w, 3])
new_image[:cuty, :cutx, :] = image_datas[0][:cuty, :cutx, :]
new_image[cuty:, :cutx, :] = image_datas[1][cuty:, :cutx, :]
new_image[cuty:, cutx:, :] = image_datas[2][cuty:, cutx:, :]
new_image[:cuty, cutx:, :] = image_datas[3][:cuty, cutx:, :]
new_image = np.array(new_image, np.uint8)
#---------------------------------#
# 对图像进行色域变换
# 计算色域变换的参数
#---------------------------------#
r = np.random.uniform(-1, 1, 3) * [hue, sat, val] + 1
#---------------------------------#
# 将图像转到HSV上
#---------------------------------#
hue, sat, val = cv2.split(cv2.cvtColor(new_image, cv2.COLOR_RGB2HSV))
dtype = new_image.dtype
#---------------------------------#
# 应用变换
#---------------------------------#
x = np.arange(0, 256, dtype=r.dtype)
lut_hue = ((x * r[0]) % 180).astype(dtype)
lut_sat = np.clip(x * r[1], 0, 255).astype(dtype)
lut_val = np.clip(x * r[2], 0, 255).astype(dtype)
new_image = cv2.merge(
(cv2.LUT(hue, lut_hue), cv2.LUT(sat,
lut_sat), cv2.LUT(val, lut_val)))
new_image = cv2.cvtColor(new_image, cv2.COLOR_HSV2RGB)
#---------------------------------#
# 对框进行进一步的处理
#---------------------------------#
new_boxes = self.merge_bboxes(box_datas, cutx, cuty)
return new_image, new_boxes
