def detect_image(self,image_id,image):
self.confidence = 0.05
f = open("./input/detection-results/"+image_id+".txt","w")
image_shape = np.array(np.shape(image)[0:2])
crop_img = np.array(letterbox_image(image, (self.model_image_size[0],self.model_image_size[1])))
photo = np.array(crop_img,dtype = np.float32)
photo /= 255.0
photo = np.transpose(photo, (2, 0, 1))
photo = photo.astype(np.float32)
images = []
images.append(photo)
images = np.asarray(images)
with torch.no_grad():
images = torch.from_numpy(images)
if self.cuda:
images = images.cuda()
outputs = self.net(images)
output_list = []
for i in range(2):
output_list.append(self.yolo_decodes[i](outputs[i]))
output = torch.cat(output_list, 1)
batch_detections = non_max_suppression(output, len(self.class_names),
conf_thres=self.confidence,
nms_thres=0.3)
try:
batch_detections = batch_detections[0].cpu().numpy()
except:
return image
top_index = batch_detections[:,4]*batch_detections[:,5] > self.confidence
top_conf = batch_detections[top_index,4]*batch_detections[top_index,5]
top_label = np.array(batch_detections[top_index,-1],np.int32)
top_bboxes = np.array(batch_detections[top_index,:4])
top_xmin, top_ymin, top_xmax, top_ymax = np.expand_dims(top_bboxes[:,0],-1),np.expand_dims(top_bboxes[:,1],-1),np.expand_dims(top_bboxes[:,2],-1),np.expand_dims(top_bboxes[:,3],-1)
# 去掉灰条
boxes = yolo_correct_boxes(top_ymin,top_xmin,top_ymax,top_xmax,np.array([self.model_image_size[0],self.model_image_size[1]]),image_shape)
for i, c in enumerate(top_label):
predicted_class = self.class_names[c]
score = str(top_conf[i])
top, left, bottom, right = boxes[i]
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 predict(self,image):
image_shape = np.array(np.shape(image)[0:2])
crop_img = np.array(letterbox_image(image, (self.input_shape[0], self.input_shape[1])))
photo = np.array(crop_img, dtype=np.float32)
photo /= 255.0
photo = np.transpose(photo, (2, 0, 1))
photo = photo.astype(np.float32)
images = []
images.append(photo)
images = np.asarray(images)
with torch.no_grad():
images = torch.from_numpy(images)
if self.cuda:
images = images.cuda()
outputs = self.net(images)
output_list = []
for i in range(3):
output_list.append(self.yolo_decodes[i](outputs[i]))
output = torch.cat(output_list, 1)
batch_detections = non_max_suppression(output, len(self.class_names),
conf_thres=self.confidence,
nms_thres=0.3)
batch_detections = batch_detections[0].cpu().numpy()
top_index = batch_detections[:, 4] * batch_detections[:, 5] > self.confidence
top_conf = batch_detections[top_index, 4] * batch_detections[top_index, 5]
top_label = np.array(batch_detections[top_index, -1], np.int32)
top_bboxes = np.array(batch_detections[top_index, :4])
top_xmin, top_ymin, top_xmax, top_ymax = np.expand_dims(top_bboxes[:, 0], -1), \
np.expand_dims(top_bboxes[:, 1], -1), \
np.expand_dims(top_bboxes[:, 2], -1), \
np.expand_dims(top_bboxes[:, 3], -1)
# 去掉灰条
boxes = yolo_correct_boxes(top_ymin, top_xmin, top_ymax, top_xmax,
np.array([self.input_shape[0], self.input_shape[1]]), image_shape)
return boxes,top_label,top_conf
def detect_image(self, image):
image_shape = np.array(np.shape(image)[0:2])
crop_img = np.array(letterbox_image(image, (self.model_image_size[0],self.model_image_size[1])))
photo = np.array(crop_img,dtype = np.float32)
photo /= 255.0
photo = np.transpose(photo, (2, 0, 1))
photo = photo.astype(np.float32)
images = []
images.append(photo)
images = np.asarray(images)
with torch.no_grad():
images = torch.from_numpy(images)
if self.cuda:
images = images.cuda()
outputs = self.net(images)
output_list = []
for i in range(3):
output_list.append(self.yolo_decodes[i](outputs[i]))
output = torch.cat(output_list, 1)
batch_detections = non_max_suppression(output, len(self.class_names),
conf_thres=self.confidence,
nms_thres=0.3)
try:
batch_detections = batch_detections[0].cpu().numpy()
except:
return image
top_index = batch_detections[:,4]*batch_detections[:,5] > self.confidence
top_conf = batch_detections[top_index,4]*batch_detections[top_index,5]
top_label = np.array(batch_detections[top_index,-1],np.int32)
top_bboxes = np.array(batch_detections[top_index,:4])
top_xmin, top_ymin, top_xmax, top_ymax = np.expand_dims(top_bboxes[:,0],-1),np.expand_dims(top_bboxes[:,1],-1),np.expand_dims(top_bboxes[:,2],-1),np.expand_dims(top_bboxes[:,3],-1)
# 去掉灰条
boxes = yolo_correct_boxes(top_ymin,top_xmin,top_ymax,top_xmax,np.array([self.model_image_size[0],self.model_image_size[1]]),image_shape)
font = ImageFont.truetype(font='model_data/simhei.ttf',size=np.floor(3e-2 * np.shape(image)[1] + 0.5).astype('int32'))
thickness = (np.shape(image)[0] + np.shape(image)[1]) // self.model_image_size[0]
for i, c in enumerate(top_label):
predicted_class = self.class_names[c]
score = top_conf[i]
top, left, bottom, right = boxes[i]
top = top - 5
left = left - 5
bottom = bottom + 5
right = right + 5
top = max(0, np.floor(top + 0.5).astype('int32'))
left = max(0, np.floor(left + 0.5).astype('int32'))
bottom = min(np.shape(image)[0], np.floor(bottom + 0.5).astype('int32'))
right = min(np.shape(image)[1], np.floor(right + 0.5).astype('int32'))
# 画框框
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
label = label.encode('utf-8')
print(label)
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[self.class_names.index(predicted_class)])
draw.rectangle(
[tuple(text_origin), tuple(text_origin + label_size)],
fill=self.colors[self.class_names.index(predicted_class)])
draw.text(text_origin, str(label,'UTF-8'), fill=(0, 0, 0), font=font)
del draw
return image
def detect_image(self, image, aligned_depth_frame=None, color_intrin_part=None, mode=1):
image_shape = np.array(np.shape(image)[0:2])
crop_img = np.array(letterbox_image(image, (self.model_image_size[0], self.model_image_size[1])))
photo = np.array(crop_img, dtype=np.float32)
photo /= 255.0
photo = np.transpose(photo, (2, 0, 1))
photo = photo.astype(np.float32)
images = []
images.append(photo)
images = np.asarray(images)
with torch.no_grad():
images = torch.from_numpy(images)
if self.cuda:
images = images.cuda()
outputs = self.net(images)
output_list = []
for i in range(3):
output_list.append(self.yolo_decodes[i](outputs[i]))
output = torch.cat(output_list, 1)
batch_detections = non_max_suppression(output, len(self.class_names),
conf_thres=self.confidence,
nms_thres=0.3)
try:
batch_detections = batch_detections[0].cpu().numpy()
except:
return image
top_index = batch_detections[:, 4] * batch_detections[:, 5] > self.confidence
top_conf = batch_detections[top_index, 4] * batch_detections[top_index, 5]
top_label = np.array(batch_detections[top_index, -1], np.int32)
top_bboxes = np.array(batch_detections[top_index, :4])
top_xmin, top_ymin, top_xmax, top_ymax = np.expand_dims(top_bboxes[:, 0], -1), np.expand_dims(top_bboxes[:, 1],
-1), np.expand_dims(
top_bboxes[:, 2], -1), np.expand_dims(top_bboxes[:, 3], -1)
# 去掉灰条
boxes = yolo_correct_boxes(top_ymin, top_xmin, top_ymax, top_xmax,
np.array([self.model_image_size[0], self.model_image_size[1]]), image_shape)
font = ImageFont.truetype(font='model_data/simhei.ttf',
size=np.floor(3e-2 * np.shape(image)[1] + 0.5).astype('int32'))
thickness = (np.shape(image)[0] + np.shape(image)[1]) // self.model_image_size[0]
for i, c in enumerate(top_label):
predicted_class = self.class_names[c]
score = top_conf[i]
top, left, bottom, right = boxes[i]
top = top - 5
left = left - 5
bottom = bottom + 5
right = right + 5
top = max(0, np.floor(top + 0.5).astype('int32'))
left = max(0, np.floor(left + 0.5).astype('int32'))
bottom = min(np.shape(image)[0], np.floor(bottom + 0.5).astype('int32'))
right = min(np.shape(image)[1], np.floor(right + 0.5).astype('int32'))
# print(np.shape(image)[0], np.shape(image)[1])
# print("left:{}, top:{}, right:{}, bottom:{}".format(left, top, right, bottom))
fail = 0
if (aligned_depth_frame and color_intrin_part):
ppx = color_intrin_part[0]
ppy = color_intrin_part[1]
fx = color_intrin_part[2]
fy = color_intrin_part[3]
width = aligned_depth_frame.width
height = aligned_depth_frame.height
# ----------------------------------------------------------------------------------------------------------------
# 1、取中心点像素深度
# ----------------------------------------------------------------------------------------------------------------
if mode == 1:
center_x = int(round((left + right) / 2))
center_y = int(round((top + bottom) / 2))
# print("center:", center_x, center_y)
# print("depth size:", width, height)
center_x = min(max(1, center_x), width - 1)
center_y = min(max(1, center_y), height - 1)
# print("center_after:", center_x, center_y)
# center_x = min(max(0,center_x),width)
# center_y = min(max(0,center_y),height)
target_xy_pixel = [center_x, center_y]
target_depth = aligned_depth_frame.get_distance(target_xy_pixel[0], target_xy_pixel[1])
strDistance = "\n%.2f m" % target_depth
target_xy_true = [(target_xy_pixel[0] - ppx) * target_depth / fx,
(target_xy_pixel[1] - ppy) * target_depth / fy]
# # ----------------------------------------------------------------------------------------------------------------
# # 2、取box里面所有像素深度值后平均
# # ----------------------------------------------------------------------------------------------------------------
# elif mode == 2:
# depth = 0
# cnt = 0
# depth_matrix = np.zeros((width, height))
# for x in range(left, right):
# for y in range(top, bottom):
# depth_matrix[x][y] = aligned_depth_frame.get_distance(x, y)
# # print("x:{}, y:{}".format(x,y),depth_matrix[x][y])
# depth += depth_matrix[x][y]
# cnt += 1
# target_depth = depth / cnt
# minn = 1000000
# pseudo_x = 0
# pseudo_y = 0
# for x in range(left, right):
# for y in range(top, bottom):
# if minn > abs(depth_matrix[x][y] - target_depth):
# minn = abs(depth_matrix[x][y] - target_depth)
# pseudo_x = x
# pseudo_y = y
# target_xy_pixel = [pseudo_x, pseudo_y]
# strDistance = " depth: %.2f m" % target_depth
# target_xy_true = [(pseudo_x - ppx) * target_depth / fx,
# (pseudo_y - ppy) * target_depth / fy]
#
# # ----------------------------------------------------------------------------------------------------------------
# # 3、去前后百分之十的极值后再平均
# # ----------------------------------------------------------------------------------------------------------------
# elif mode == 3:
# depth = 0
# cnt = 0
# depth_matrix = np.zeros((width, height))
# for x in range(left, right):
# for y in range(top, bottom):
# depth_matrix[x][y] = aligned_depth_frame.get_distance(x, y)
#
# depth_matrix_flat = depth_matrix[left:right, top:bottom].reshape((right - left) * (bottom - top), )
# matrix_flat_len = depth_matrix_flat.shape[0]
# drop_len = int(matrix_flat_len * 0.1)
# depth_matrix_flat.sort()
# depth_matrix_flat = depth_matrix_flat[drop_len:-drop_len]
# depth = depth_matrix_flat.sum()
#
# target_depth = depth / (matrix_flat_len - 2 * drop_len)
# minn = 1000000
# pseudo_x = 0
# pseudo_y = 0
# for x in range(left, right):
# for y in range(top, bottom):
# if minn > abs(depth_matrix[x][y] - target_depth):
# minn = abs(depth_matrix[x][y] - target_depth)
# pseudo_x = x
# pseudo_y = y
# target_xy_pixel = [pseudo_x, pseudo_y]
# strDistance = " depth: %.2f m" % target_depth
# target_xy_true = [(pseudo_x - ppx) * target_depth / fx,
# (pseudo_y - ppy) * target_depth / fy]
#
# # ----------------------------------------------------------------------------------------------------------------
# # 4、去掉深度缺失的像素（深度为0）后再平均
# # ----------------------------------------------------------------------------------------------------------------
# elif mode == 4:
# depth = 0
# cnt = 0
# depth_matrix = np.zeros((width, height))
# for x in range(left, right):
# for y in range(top, bottom):
# depth_matrix[x][y] = aligned_depth_frame.get_distance(x, y)
# if depth_matrix[x][y] > 0:
# depth += depth_matrix[x][y]
# cnt += 1
# if cnt == 0:
# print("该目标框内所有像素均检测缺失，无法计算深度")
# fail = 1
# else:
# target_depth = depth / cnt
# minn = 1000000
# pseudo_x = 0
# pseudo_y = 0
# for x in range(left, right):
# for y in range(top, bottom):
# if minn > abs(depth_matrix[x][y] - target_depth):
# minn = abs(depth_matrix[x][y] - target_depth)
# pseudo_x = x
# pseudo_y = y
# target_xy_pixel = [pseudo_x, pseudo_y]
# strDistance = " depth: %.2f m" % target_depth
# target_xy_true = [(pseudo_x - ppx) * target_depth / fx,
# (pseudo_y - ppy) * target_depth / fy]
else:
strDistance = "\n 0 m"
# 画框框----------------------------------------------------------------------------------------------------
if fail == 0:
label = '{} {:.2f}'.format(predicted_class, score)
label = label + strDistance
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
label = label.encode('utf-8')
# print(label)
print('检测出目标：{} ；实际坐标为（m)：（{:.3f}, {:.3f}, {:.3f}) \n中心点像素坐标(pixel):({}, {}) ；中心点相机坐标（m):（{}，{}）；深度: {} m\n'.format(predicted_class,
target_xy_true[0],target_xy_true[1],target_depth,
target_xy_pixel[0],
target_xy_pixel[1],
target_xy_true[0],
target_xy_true[1],
target_depth))
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[self.class_names.index(predicted_class)])
draw.rectangle(
[tuple(text_origin), tuple(text_origin + label_size)],
fill=self.colors[self.class_names.index(predicted_class)])
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])
#---------------------------------------------------------#
# 给图像增加灰条，实现不失真的resize
#---------------------------------------------------------#
crop_img = np.array(
letterbox_image(
image, (self.model_image_size[1], self.model_image_size[0])))
photo = np.array(crop_img, dtype=np.float32) / 255.0
photo = np.transpose(photo, (2, 0, 1))
#---------------------------------------------------------#
# 添加上batch_size维度
#---------------------------------------------------------#
images = [photo]
with torch.no_grad():
images = torch.from_numpy(np.asarray(images))
if self.cuda:
images = images.cuda()
outputs = self.net(images)
output_list = []
for i in range(3):
output_list.append(self.yolo_decodes[i](outputs[i]))
output = torch.cat(output_list, 1)
batch_detections = non_max_suppression(output,
len(self.class_names),
conf_thres=self.confidence,
nms_thres=self.iou)
try:
batch_detections = batch_detections[0].cpu().numpy()
top_index = batch_detections[:,
4] * batch_detections[:,
5] > self.confidence
top_conf = batch_detections[top_index,
4] * batch_detections[top_index, 5]
top_label = np.array(batch_detections[top_index, -1], np.int32)
top_bboxes = np.array(batch_detections[top_index, :4])
top_xmin, top_ymin, top_xmax, top_ymax = np.expand_dims(
top_bboxes[:, 0],
-1), np.expand_dims(top_bboxes[:, 1], -1), np.expand_dims(
top_bboxes[:, 2],
-1), np.expand_dims(top_bboxes[:, 3], -1)
# 去掉灰条
boxes = yolo_correct_boxes(
top_ymin, top_xmin, top_ymax, top_xmax,
np.array(
[self.model_image_size[0], self.model_image_size[1]]),
image_shape)
except:
pass
t1 = time.time()
for _ in range(test_interval):
with torch.no_grad():
outputs = self.net(images)
output_list = []
for i in range(3):
output_list.append(self.yolo_decodes[i](outputs[i]))
output = torch.cat(output_list, 1)
batch_detections = non_max_suppression(
output,
len(self.class_names),
conf_thres=self.confidence,
nms_thres=self.iou)
try:
batch_detections = batch_detections[0].cpu().numpy()
top_index = batch_detections[:,
4] * batch_detections[:,
5] > self.confidence
top_conf = batch_detections[
top_index, 4] * batch_detections[top_index, 5]
top_label = np.array(batch_detections[top_index, -1],
np.int32)
top_bboxes = np.array(batch_detections[top_index, :4])
top_xmin, top_ymin, top_xmax, top_ymax = np.expand_dims(
top_bboxes[:, 0], -1), np.expand_dims(
top_bboxes[:, 1], -1), np.expand_dims(
top_bboxes[:, 2],
-1), np.expand_dims(top_bboxes[:, 3], -1)
# 去掉灰条
boxes = yolo_correct_boxes(
top_ymin, top_xmin, top_ymax, top_xmax,
np.array([
self.model_image_size[0], self.model_image_size[1]
]), image_shape)
except:
pass
t2 = time.time()
tact_time = (t2 - t1) / test_interval
return tact_time
def detect_image(self, image_id, image):
self.confidence = 0.01
self.iou = 0.5
f = open("./input/detection-results/" + image_id + ".txt", "w")
image_shape = np.array(np.shape(image)[0:2])
#---------------------------------------------------------#
# 给图像增加灰条，实现不失真的resize
# 也可以直接resize进行识别
#---------------------------------------------------------#
if self.letterbox_image:
crop_img = np.array(
letterbox_image(
image,
(self.model_image_size[1], self.model_image_size[0])))
else:
crop_img = image.convert('RGB')
crop_img = crop_img.resize(
(self.model_image_size[1], self.model_image_size[0]),
Image.BICUBIC)
photo = np.array(crop_img, dtype=np.float32) / 255.0
photo = np.transpose(photo, (2, 0, 1))
#---------------------------------------------------------#
# 添加上batch_size维度
#---------------------------------------------------------#
images = [photo]
with torch.no_grad():
images = torch.from_numpy(np.asarray(images))
if self.cuda:
images = images.cuda()
#---------------------------------------------------------#
# 将图像输入网络当中进行预测！
#---------------------------------------------------------#
outputs = self.net(images)
output_list = []
for i in range(3):
output_list.append(self.yolo_decodes[i](outputs[i]))
#---------------------------------------------------------#
# 将预测框进行堆叠，然后进行非极大抑制
#---------------------------------------------------------#
output = torch.cat(output_list, 1)
batch_detections = non_max_suppression(output,
self.num_classes,
conf_thres=self.confidence,
nms_thres=self.iou)
#---------------------------------------------------------#
# 如果没有检测出物体，返回
#---------------------------------------------------------#
try:
batch_detections = batch_detections[0].cpu().numpy()
except:
return
#---------------------------------------------------------#
# 对预测框进行得分筛选
#---------------------------------------------------------#
top_index = batch_detections[:,
4] * batch_detections[:,
5] > self.confidence
top_conf = batch_detections[top_index,
4] * batch_detections[top_index, 5]
top_label = np.array(batch_detections[top_index, -1], np.int32)
top_bboxes = np.array(batch_detections[top_index, :4])
top_xmin, top_ymin, top_xmax, top_ymax = np.expand_dims(
top_bboxes[:, 0],
-1), np.expand_dims(top_bboxes[:, 1], -1), np.expand_dims(
top_bboxes[:, 2],
-1), np.expand_dims(top_bboxes[:, 3], -1)
#-----------------------------------------------------------------#
# 在图像传入网络预测前会进行letterbox_image给图像周围添加灰条
# 因此生成的top_bboxes是相对于有灰条的图像的
# 我们需要对其进行修改，去除灰条的部分。
#-----------------------------------------------------------------#
if self.letterbox_image:
boxes = yolo_correct_boxes(
top_ymin, top_xmin, top_ymax, top_xmax,
np.array(
[self.model_image_size[0], self.model_image_size[1]]),
image_shape)
else:
top_xmin = top_xmin / self.model_image_size[1] * image_shape[1]
top_ymin = top_ymin / self.model_image_size[0] * image_shape[0]
top_xmax = top_xmax / self.model_image_size[1] * image_shape[1]
top_ymax = top_ymax / self.model_image_size[0] * image_shape[0]
boxes = np.concatenate(
[top_ymin, top_xmin, top_ymax, top_xmax], axis=-1)
for i, c in enumerate(top_label):
predicted_class = self.class_names[c]
score = str(top_conf[i])
top, left, bottom, right = boxes[i]
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])
#---------------------------------------------------------#
# 给图像增加灰条，实现不失真的resize
#---------------------------------------------------------#
crop_img = np.array(
letterbox_image(
image, (self.model_image_size[1], self.model_image_size[0])))
photo = np.array(crop_img, dtype=np.float32) / 255.0
photo = np.transpose(photo, (2, 0, 1))
#---------------------------------------------------------#
# 添加上batch_size维度
#---------------------------------------------------------#
images = [photo]
with torch.no_grad():
images = torch.from_numpy(np.asarray(images))
if self.cuda:
images = images.cuda()
#---------------------------------------------------------#
# 将图像输入网络当中进行预测！
#---------------------------------------------------------#
outputs = self.net(images)
output_list = []
for i in range(3):
output_list.append(self.yolo_decodes[i](outputs[i]))
#---------------------------------------------------------#
# 将预测框进行堆叠，然后进行非极大抑制
#---------------------------------------------------------#
output = torch.cat(output_list, 1)
batch_detections = non_max_suppression(output,
len(self.class_names),
conf_thres=self.confidence,
nms_thres=self.iou)
#---------------------------------------------------------#
# 如果没有检测出物体，返回原图
#---------------------------------------------------------#
try:
batch_detections = batch_detections[0].cpu().numpy()
except:
return image
#---------------------------------------------------------#
# 对预测框进行得分筛选
#---------------------------------------------------------#
top_index = batch_detections[:,
4] * batch_detections[:,
5] > self.confidence
top_conf = batch_detections[top_index,
4] * batch_detections[top_index, 5]
top_label = np.array(batch_detections[top_index, -1], np.int32)
top_bboxes = np.array(batch_detections[top_index, :4])
top_xmin, top_ymin, top_xmax, top_ymax = np.expand_dims(
top_bboxes[:, 0],
-1), np.expand_dims(top_bboxes[:, 1], -1), np.expand_dims(
top_bboxes[:, 2],
-1), np.expand_dims(top_bboxes[:, 3], -1)
#-----------------------------------------------------------------#
# 在图像传入网络预测前会进行letterbox_image给图像周围添加灰条
# 因此生成的top_bboxes是相对于有灰条的图像的
# 我们需要对其进行修改，去除灰条的部分。
#-----------------------------------------------------------------#
boxes = yolo_correct_boxes(
top_ymin, top_xmin, top_ymax, top_xmax,
np.array([self.model_image_size[0], self.model_image_size[1]]),
image_shape)
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.model_image_size[0], 1)
for i, c in enumerate(top_label):
predicted_class = self.class_names[c]
score = top_conf[i]
top, left, bottom, right = boxes[i]
top = top - 5
left = left - 5
bottom = bottom + 5
right = right + 5
top = max(0, np.floor(top + 0.5).astype('int32'))
left = max(0, np.floor(left + 0.5).astype('int32'))
bottom = min(
np.shape(image)[0],
np.floor(bottom + 0.5).astype('int32'))
right = min(
np.shape(image)[1],
np.floor(right + 0.5).astype('int32'))
# 画框框
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
label = label.encode('utf-8')
print(label, top, left, bottom, right)
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
for i in range(thickness):
draw.rectangle([left + i, top + i, right - i, bottom - i],
outline=self.colors[self.class_names.index(
predicted_class)])
draw.rectangle(
[tuple(text_origin),
tuple(text_origin + label_size)],
fill=self.colors[self.class_names.index(predicted_class)])
draw.text(text_origin,
str(label, 'UTF-8'),
fill=(0, 0, 0),
font=font)
del draw
return image
def detect_image(self, image):
image_shape = np.array(np.shape(image)[0:2])
crop_img = np.array(
letterbox_image(
image, (self.model_image_size[0], self.model_image_size[1])))
photo = np.array(crop_img, dtype=np.float32)
photo /= 255.0
photo = np.transpose(photo, (2, 0, 1))
photo = photo.astype(np.float32)
images = []
images.append(photo)
images = np.asarray(images)
with torch.no_grad():
images = torch.from_numpy(images)
if self.cuda:
images = images.cuda()
outputs = self.net(images)
output_list = []
for i in range(3):
output_list.append(self.yolo_decodes[i](outputs[i]))
output = torch.cat(output_list, 1)
batch_detections = non_max_suppression(output,
len(self.class_names),
conf_thres=self.confidence,
nms_thres=0.3)
try:
batch_detections = batch_detections[0].cpu().numpy()
except:
return image
top_index = batch_detections[:,
4] * batch_detections[:,
5] > self.confidence
top_conf = batch_detections[top_index, 4] * batch_detections[top_index,
5]
top_label = np.array(batch_detections[top_index, -1], np.int32)
top_bboxes = np.array(batch_detections[top_index, :4])
top_xmin, top_ymin, top_xmax, top_ymax = np.expand_dims(
top_bboxes[:, 0],
-1), np.expand_dims(top_bboxes[:, 1], -1), np.expand_dims(
top_bboxes[:, 2], -1), np.expand_dims(top_bboxes[:, 3], -1)
# 去掉灰条
boxes = yolo_correct_boxes(
top_ymin, top_xmin, top_ymax, top_xmax,
np.array([self.model_image_size[0], self.model_image_size[1]]),
image_shape)
font = ImageFont.truetype(font='model_data/simhei.ttf',
size=np.floor(3e-2 * np.shape(image)[1] +
0.5).astype('int32'))
thickness = (np.shape(image)[0] +
np.shape(image)[1]) // self.model_image_size[0]
for i, c in enumerate(top_label):
predicted_class = self.class_names[c]
score = top_conf[i]
top, left, bottom, right = boxes[i]
# top = top - 250
# left = left - 250
# bottom = bottom + 250
# right = right + 250
top = top - 5
left = left - 5
bottom = bottom + 5
right = right + 5
# 从左上角开始 剪切 200*200的图片
img2 = image.crop((left, top, right, bottom))
img2.save("lena2.jpg")
top = max(0, np.floor(top).astype('int32'))
left = max(0, np.floor(left).astype('int32'))
bottom = min(np.shape(image)[0], np.floor(bottom).astype('int32'))
right = min(np.shape(image)[1], np.floor(right).astype('int32'))
# 画框框
# 画框框
if predicted_class == 'person':
predicted_class_ch = "Ren"
elif predicted_class == 'chair':
predicted_class_ch = "椅子"
elif predicted_class == 'clock':
predicted_class_ch = "钟"
elif predicted_class == 'tie':
predicted_class_ch = "厂牌吗？？"
elif predicted_class == 'cell phone':
predicted_class_ch = "手机"
elif predicted_class == 'laptop':
predicted_class_ch = "笔记本电脑"
elif predicted_class == 'QR':
predicted_class_ch = "2维码"
else:
predicted_class_ch = "单号"
label = '{} {} {:.2f} {}'.format(predicted_class_ch, '置信度', score,
'%')
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
label = label.encode('utf-8')
print(label)
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 3])
for i in range(thickness):
draw.rectangle( #边框
[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])Y1909170500-F1-1568720302878.jpg 不行
# 绘制文本E:\发货单\截图20200727212747.png
# font = ImageFont.truetype("consola.ttf", 40, encoding="unic") # 设置字体
# draw.text((100, 50), u'Hello World', 'fuchsia', font)
#draw.text(text_origin, str(label, 'UTF-8'), fill=(0, 0, 0), font=font)
#del draw
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 generate_box(self, image):
image_shape = np.array(np.shape(image)[0:2])
crop_img = np.array(letterbox_image(image, (self.model_image_size[0], self.model_image_size[1])))
photo = np.array(crop_img, dtype=np.float32)
photo /= 255.0
photo = np.transpose(photo, (2, 0, 1))
photo = photo.astype(np.float32)
images = []
images.append(photo)
images = np.asarray(images)
images = torch.from_numpy(images)
if self.cuda:
images = images.cuda()
with torch.no_grad():
outputs = self.net(images)
output_list = []
for i in range(3):
output_list.append(self.yolo_decodes[i](outputs[i]))
output = torch.cat(output_list, 1)
batch_detections = non_max_suppression(output, self.config["yolo"]["classes"],
conf_thres=self.confidence,
nms_thres=0.3)
try:
batch_detections = batch_detections[0].cpu().numpy()
except:
boxlist = []
return boxlist
top_index = batch_detections[:, 4] * batch_detections[:, 5] > self.confidence
top_conf = batch_detections[top_index, 4] * batch_detections[top_index, 5]
top_label = np.array(batch_detections[top_index, -1], np.int32)
top_bboxes = np.array(batch_detections[top_index, :4])
top_xmin, top_ymin, top_xmax, top_ymax = np.expand_dims(top_bboxes[:, 0], -1), np.expand_dims(top_bboxes[:, 1],
-1), np.expand_dims(
top_bboxes[:, 2], -1), np.expand_dims(top_bboxes[:, 3], -1)
# 去掉灰条
boxes = yolo_correct_boxes(top_ymin, top_xmin, top_ymax, top_xmax,
np.array([self.model_image_size[0], self.model_image_size[1]]), image_shape)
font = ImageFont.truetype(font='model_data/simhei.ttf',
size=np.floor(3e-2 * np.shape(image)[1] + 0.5).astype('int32'))
thickness = (np.shape(image)[0] + np.shape(image)[1]) // self.model_image_size[0]
boxlist = []
for i, c in enumerate(top_label):
top, left, bottom, right = boxes[i]
top = top - 5
left = left - 5
bottom = bottom + 5
right = right + 5
top = max(0, np.floor(top + 0.5).astype('int32'))
left = max(0, np.floor(left + 0.5).astype('int32'))
bottom = min(np.shape(image)[0], np.floor(bottom + 0.5).astype('int32'))
right = min(np.shape(image)[1], np.floor(right + 0.5).astype('int32'))
box_str = str(left) + ',' + str(top) + ',' + str(right) + ',' + str(bottom)
boxlist.append(box_str)
return boxlist
def detect_image(self, image):
image_shape = np.array(np.shape(image)[0:2])
crop_img = np.array(
letterbox_image(
image, (self.model_image_size[0], self.model_image_size[1])))
photo = np.array(crop_img, dtype=np.float32)
photo /= 255.0 # 归一化
photo = np.transpose(photo, (2, 0, 1))
photo = photo.astype(np.float32)
images = []
images.append(photo)
images = np.asarray(images)
with torch.no_grad():
images = torch.from_numpy(images)
if self.cuda:
images = images.cuda()
outputs = self.net(images)
output_list = []
for i in range(3):
output_list.append(self.yolo_decodes[i](outputs[i]))
output = torch.cat(output_list, 1)
batch_detections = non_max_suppression(output,
len(self.class_names),
conf_thres=self.confidence,
nms_thres=0.3)
try:
batch_detections = batch_detections[0].cpu().numpy()
except:
return []
top_index = batch_detections[:,
4] * batch_detections[:,
5] > self.confidence
top_conf = batch_detections[top_index, 4] * batch_detections[top_index,
5]
top_label = np.array(batch_detections[top_index, -1], np.int32)
top_bboxes = np.array(batch_detections[top_index, :4])
top_xmin, top_ymin, top_xmax, top_ymax = np.expand_dims(
top_bboxes[:, 0],
-1), np.expand_dims(top_bboxes[:, 1], -1), np.expand_dims(
top_bboxes[:, 2], -1), np.expand_dims(top_bboxes[:, 3], -1)
boxes = yolo_correct_boxes(
top_ymin, top_xmin, top_ymax, top_xmax,
np.array([self.model_image_size[0], self.model_image_size[1]]),
image_shape)
l = []
for i, c in enumerate(top_label):
predicted_class = self.class_names[c]
score = top_conf[i]
top, left, bottom, right = boxes[i]
top = top - 5
left = left - 5
bottom = bottom + 5
right = right + 5
top = max(0, np.floor(top + 0.5).astype('int32'))
left = max(0, np.floor(left + 0.5).astype('int32'))
bottom = min(
np.shape(image)[0],
np.floor(bottom + 0.5).astype('int32'))
right = min(
np.shape(image)[1],
np.floor(right + 0.5).astype('int32'))
l.append([left, top, right, bottom, score, predicted_class])
return l
def detect_image(self, image):
# embed()
image_shape = np.array(np.shape(image)[0:2])
num_class = len(self.class_names) # 有80类
# embed()
#---------------------------------------------------------#
# 给图像增加灰条（什么是灰条），实现不失真的resize
#---------------------------------------------------------#
# 复制image return new_image
crop_img = np.array(
letterbox_image(
image, (self.model_image_size[1], self.model_image_size[0])))
photo = np.array(crop_img, dtype=np.float32) / 255.0 # 归一化？
photo = np.transpose(
photo, (2, 0, 1)
) # 转置：将Image.open(img)得到的[H,W,C]格式转换permute为pytorch可以处理的[C,H,W]格式
#---------------------------------------------------------#
# 添加上batch_size维度
#---------------------------------------------------------#
images = [photo] # 将photo变为list类型
with torch.no_grad(
): # disabled gradient calculation,reduce memory consumption for computations
images = torch.from_numpy(
np.asarray(images)
) # Creates a Tensor from a numpy.ndarray，此时images的shape为[1, 3, 416, 416]
if self.cuda:
images = images.cuda()
#---------------------------------------------------------#
# 将图像输入网络当中进行预测！
#---------------------------------------------------------#
# embed()
# 从这里开始处理
# 特征提取
# 输出outputs为tuple,len=3,每个tensor的shape分别为 第一个特征层[1, 255, 13, 13],第二个特征层[1, 255, 26, 26],第三个特征层[1, 255, 52, 52]
outputs = self.net(images)
# embed()
output_list = []
for i in range(3): # 为什么是3
# 有三个特征层，每个特征层对应自己的decode解码器
output_list.append(self.yolo_decodes[i](
outputs[i])) # 在这里打几个断点看看
#---------------------------------------------------------#
# 将预测框进行堆叠，然后进行非极大抑制
#---------------------------------------------------------#
# torch.cat()对矩阵按行进行拼接得到向量
output = torch.cat(output_list, 1) # 这里也打几个断点
# output就是predictions,格式为[batch_size, num_anchors, 85]
batch_detections = non_max_suppression(output,
len(self.class_names),
conf_thres=self.confidence,
nms_thres=self.iou)
# embed()
#---------------------------------------------------------#
# 如果没有检测出物体，返回原图
#---------------------------------------------------------#
try:
batch_detections = batch_detections[0].cpu().numpy()
except:
return image
#---------------------------------------------------------#
# 对预测框进行得分筛选
#---------------------------------------------------------#
# coordinates = []# bboxes的坐标
top_index = batch_detections[:,
4] * batch_detections[:,
5] > self.confidence
top_conf = batch_detections[top_index,
4] * batch_detections[top_index, 5]
top_label = np.array(batch_detections[top_index, -1], np.int32)
top_bboxes = np.array(batch_detections[top_index, :4])
# 得到坐标点
top_xmin, top_ymin, top_xmax, top_ymax = np.expand_dims(
top_bboxes[:, 0],
-1), np.expand_dims(top_bboxes[:, 1], -1), np.expand_dims(
top_bboxes[:, 2],
-1), np.expand_dims(top_bboxes[:, 3], -1)
# coordinates.append((top_xmin,top_xmax,top_ymin,top_ymax))# 把四个坐标点看做一个整体
#-----------------------------------------------------------------#
# 在图像传入网络预测前会进行letterbox_image给图像周围添加灰条
# 因此生成的top_bboxes是相对于有灰条的图像的
# 我们需要对其进行修改，去除灰条的部分。
#-----------------------------------------------------------------#
# boxes存放各目标的坐标
boxes = yolo_correct_boxes(
top_ymin, top_xmin, top_ymax, top_xmax,
np.array([self.model_image_size[0], self.model_image_size[1]]),
image_shape)
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.model_image_size[0], 1)
for i, c in enumerate(top_label):
# embed()
predicted_class = self.class_names[c]
score = top_conf[i]
top, left, bottom, right = boxes[i]
top = top - 5
left = left - 5
bottom = bottom + 5
right = right + 5
# 左上角点的坐标
top = max(0, np.floor(top + 0.5).astype('int32'))
left = max(0, np.floor(left + 0.5).astype('int32'))
# 右下角点的坐标
bottom = min(
np.shape(image)[0],
np.floor(bottom + 0.5).astype('int32'))
right = min(
np.shape(image)[1],
np.floor(right + 0.5).astype('int32'))
# 画框框
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
label = label.encode('utf-8')
print(label, top, left, bottom, right)
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
for i in range(thickness):
draw.rectangle([left + i, top + i, right - i, bottom - i],
outline=self.colors[self.class_names.index(
predicted_class)])
draw.rectangle( # 画框框
[tuple(text_origin),
tuple(text_origin + label_size)],
fill=self.colors[self.class_names.index(predicted_class)])
draw.text(text_origin,
str(label, 'UTF-8'),
fill=(0, 0, 0),
font=font)
del draw
return image, boxes # 将boxes返回
def detect_image(self, image):
image_shape = np.array(np.shape(image)[0:2])
crop_img = np.array(
letterbox_image(image,
(self.model_image_size[1],
self.model_image_size[0]))) # letterbox_image？？？
photo = np.array(crop_img, dtype=np.float32)
photo /= 255.0 # 归一化操作
photo = np.transpose(photo, (2, 0, 1)) # 通道维度调整（pytorch），有利于GPU处理
photo = photo.astype(np.float32)
images = [] # 扩充一个维度
images.append(photo) # 扩充一个维度
images = np.asarray(images)
images = torch.from_numpy(images) # numpy转化为tensor
if self.cuda:
images = images.cuda()
with torch.no_grad():
outputs = self.net(images) # 图片传入网络，得到网络的预测结果
output_list = [] # 三个size的预测结果
for i in range(3): # 经过三次循环对特征层解码（先验框）
output_list.append(self.yolo_decodes[i](
outputs[i])) # yolo_decodes 先验框调整的过程
output = torch.cat(output_list, 1)
batch_detections = non_max_suppression(
output,
self.config["yolo"]["classes"], # 非极大值抑制
conf_thres=self.confidence,
nms_thres=self.iou)
try:
batch_detections = batch_detections[0].cpu().numpy() # 判断图片是否还有框
except:
return image
top_index = batch_detections[:,
4] * batch_detections[:,
5] > self.confidence
top_conf = batch_detections[top_index, 4] * batch_detections[top_index,
5]
top_label = np.array(batch_detections[top_index, -1], np.int32)
top_bboxes = np.array(batch_detections[top_index, :4])
top_xmin, top_ymin, top_xmax, top_ymax = np.expand_dims(
top_bboxes[:, 0],
-1), np.expand_dims(top_bboxes[:, 1], -1), np.expand_dims(
top_bboxes[:, 2], -1), np.expand_dims(top_bboxes[:, 3], -1)
# 去掉灰条（基于原图的坐标绘制框）
boxes = yolo_correct_boxes(
top_ymin, top_xmin, top_ymax, top_xmax,
np.array([self.model_image_size[0], self.model_image_size[1]]),
image_shape)
font = ImageFont.truetype(font='model_data/simhei.ttf',
size=np.floor(3e-2 * np.shape(image)[1] +
0.5).astype('int32')) # 定义字体
thickness = (np.shape(image)[0] + np.shape(image)[1]
) // self.model_image_size[0] # 框的宽度怎么样子的
# 画图的代码
for i, c in enumerate(top_label):
predicted_class = self.class_names[c] # 取出类的名称
score = top_conf[i] # 取出类的得分
top, left, bottom, right = boxes[i] # # 取出类的位置
top = top - 5
left = left - 5
bottom = bottom + 5
right = right + 5
top = max(0, np.floor(top + 0.5).astype('int32'))
left = max(0, np.floor(left + 0.5).astype('int32'))
bottom = min(
np.shape(image)[0],
np.floor(bottom + 0.5).astype('int32'))
right = min(
np.shape(image)[1],
np.floor(right + 0.5).astype('int32'))
# 画框框
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
label = label.encode('utf-8')
print(label)
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( # 绘画矩形
[left + i, top + i, right - i, bottom - i],
outline=self.colors[self.class_names.index(
predicted_class)])
draw.rectangle(
[tuple(text_origin),
tuple(text_origin + label_size)],
fill=self.colors[self.class_names.index(predicted_class)])
draw.text(text_origin,
str(label, 'UTF-8'),
fill=(0, 0, 0),
font=font) # 写字
del draw
return image
def detect_image(self, image):
predict = np.zeros(12)
image_shape = np.array(np.shape(image)[0:2])
crop_img = np.array(
letterbox_image(
image, (self.model_image_size[0], self.model_image_size[1])))
photo = np.array(crop_img, dtype=np.float32)
photo /= 255.0
photo = np.transpose(photo, (2, 0, 1))
photo = photo.astype(np.float32)
images = []
images.append(photo)
images = np.asarray(images)
with torch.no_grad():
images = torch.from_numpy(images)
if self.cuda:
images = images.cuda()
outputs = self.net(images)
output_list = []
for i in range(3):
output_list.append(self.yolo_decodes[i](outputs[i]))
output = torch.cat(output_list, 1)
batch_detections = non_max_suppression(output,
len(self.class_names),
conf_thres=self.confidence,
nms_thres=0.3)
try:
batch_detections = batch_detections[0].cpu().numpy()
except:
return image, predict
top_index = batch_detections[:,
4] * batch_detections[:,
5] > self.confidence
top_conf = batch_detections[top_index, 4] * batch_detections[top_index,
5]
top_label = np.array(batch_detections[top_index, -1], np.int32)
top_bboxes = np.array(batch_detections[top_index, :4])
top_xmin, top_ymin, top_xmax, top_ymax = np.expand_dims(
top_bboxes[:, 0],
-1), np.expand_dims(top_bboxes[:, 1], -1), np.expand_dims(
top_bboxes[:, 2], -1), np.expand_dims(top_bboxes[:, 3], -1)
# 去掉灰条
boxes = yolo_correct_boxes(
top_ymin, top_xmin, top_ymax, top_xmax,
np.array([self.model_image_size[0], self.model_image_size[1]]),
image_shape)
font = ImageFont.truetype(font='model_data/simhei.ttf',
size=np.floor(3e-2 * np.shape(image)[1] +
0.5).astype('int32'))
thickness = (np.shape(image)[0] +
np.shape(image)[1]) // self.model_image_size[0]
predict = self.calsquare(image, boxes, top_label, predict)
return image, predict
def detect_image(self, image):
image_shape = np.array(np.shape(image)[0:2])
crop_img = np.array(
letterbox_image(
image, (self.model_image_size[0], self.model_image_size[1])))
# 更改格式为float32
photo = np.array(crop_img, dtype=np.float32)
# 像素值压缩到0-1之间
photo /= 255.0
# 读进来得图片是H*W*C，输入网络时要求C*H*W，因此在这里变换通道
photo = np.transpose(photo, (2, 0, 1))
# photo = photo.astype(np.float32)
images = []
images.append(photo)
images = np.asarray(images)
with torch.no_grad():
# 把numpy格式的像素数组转为tensor
images = torch.from_numpy(images)
if self.cuda:
# 把数据放到GPU上
images = images.cuda()
# YoloBody得到预测结果
# self.net == self.net.forword(images)
outputs = self.net(images)
output_list = []
for i in range(3):
# 用第i个DecodeBox来处理第i个output
output_list.append(self.yolo_decodes[i](outputs[i]))
# 将13、26、52的output拼接到一起 bs * 10647 * [4+1+num_classes]
output = torch.cat(output_list, 1)
# 使用非极大似然抑制剔除一定区域内的重复框
# bs * n * [(x1,y1,x2,y2)+obj_conf+class_conf+class_pred]
batch_detections = non_max_suppression(output,
len(self.class_names),
conf_thres=self.confidence,
nms_thres=self.iou)
# 整理检测结果
try:
batch_detections = batch_detections[0].cpu().numpy()
except:
return image
# 根据score再筛选一遍，但是在non_max_suppression已经使用score筛选过了为什么还要筛选呢？
top_index = batch_detections[:,
4] * batch_detections[:,
5] > self.confidence
# 根据筛选结果得到符合要求的score、label、bboxes
top_score = batch_detections[top_index,
4] * batch_detections[top_index, 5]
top_label = np.array(batch_detections[top_index, -1], np.int32)
top_bboxes = np.array(batch_detections[top_index, :4])
# 将(x1,y1,x2,y2)分别扩展至n*1维，n为box总数
top_xmin, top_ymin, top_xmax, top_ymax = np.expand_dims(
top_bboxes[:, 0],
-1), np.expand_dims(top_bboxes[:, 1], -1), np.expand_dims(
top_bboxes[:, 2], -1), np.expand_dims(top_bboxes[:, 3], -1)
# 去掉灰条，得到原图上预测框(y1,x1,y2,x2)坐标(top,left,bottom,right)
boxes = yolo_correct_boxes(
top_ymin, top_xmin, top_ymax, top_xmax,
np.array([self.model_image_size[0], self.model_image_size[1]]),
image_shape)
# 绘制检测结果
font = ImageFont.truetype(font='model_data/simhei.ttf',
size=np.floor(3e-2 * np.shape(image)[1] +
0.5).astype('int32'))
# 矩形框四边线条厚度
# thickness = (np.shape(image)[0] + np.shape(image)[1]) // self.model_image_size[0]
thickness = int(
max(np.ceil(np.shape(image)[0] / self.model_image_size[0]),
np.ceil(np.shape(image)[1] / self.model_image_size[0]))) + 1
for i, c in enumerate(top_label):
predicted_class = self.class_names[c]
score = top_score[i]
top, left, bottom, right = boxes[i]
# top = top - 5
# left = left - 5
# bottom = bottom + 5
# right = right + 5
# top = max(0, np.floor(top + 0.5).astype('int32'))
# left = max(0, np.floor(left + 0.5).astype('int32'))
# bottom = min(np.shape(image)[0], np.floor(bottom + 0.5).astype('int32'))
# right = min(np.shape(image)[1], np.floor(right + 0.5).astype('int32'))
top = max(0, np.ceil(top).astype('int32'))
left = max(0, np.ceil(left).astype('int32'))
bottom = min(np.shape(image)[0], np.ceil(bottom).astype('int32'))
right = min(np.shape(image)[1], np.ceil(right).astype('int32'))
# 画框框
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
# 返回使用指定字体对象显示给定字符串所需要的图像尺寸
label_size = draw.textsize(label, font)
# label = label.encode('utf-8')
# print(label)
# 如果顶部有文本框的空间，文本框放置在预测框左上方的外部
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
# 顶部没有文本框的空间，文本框放置在预测框左上方的内部
else:
# text_origin = np.array([left, top + 1])
text_origin = np.array([left + 1, top + 1])
# 绘制预测框的空心矩形
draw.rectangle(
[left, top, right, bottom],
outline=self.colors[self.class_names.index(predicted_class)],
width=thickness)
# 绘制文本框的实心矩形
draw.rectangle(
[tuple(text_origin),
tuple(text_origin + label_size)],
fill=self.colors[self.class_names.index(predicted_class)])
# 绘制文本框内的文字
# draw.text(text_origin, str(label,'UTF-8'), fill=(0, 0, 0), font = font)
# fill = (0, 0, 0) 文字颜色纯黑
draw.text(text_origin, label, fill=(0, 0, 0), font=font)
del draw
return image
def detect_image(self, image):
image_shape = np.array(np.shape(image)[0:2])
crop_img = np.array(
letterbox_image(
image, (self.model_image_size[0], self.model_image_size[1])))
photo = np.array(crop_img, dtype=np.float32)
photo /= 255.0
photo = np.transpose(photo, (2, 0, 1))
photo = photo.astype(np.float32)
images = []
images.append(photo)
images = np.asarray(images)
images = torch.from_numpy(images)
if self.cuda:
images = images.cuda()
with torch.no_grad():
outputs = self.net(images)
output_list = []
for i in range(3):
output_list.append(self.yolo_decodes[i](outputs[i]))
output = torch.cat(output_list, 1)
batch_detections = non_max_suppression(
output,
self.config["yolo"]["classes"],
conf_thres=self.confidence,
nms_thres=self.iou)
print(f'[INFO] batch_detections: {batch_detections[0].shape}')
try:
batch_detections = batch_detections[0].cpu().numpy()
except:
return image
top_index = batch_detections[:, 4] > self.confidence
top_conf = batch_detections[top_index, 4]
top_angle = batch_detections[top_index, 5:8]
top_bboxes = np.array(batch_detections[top_index, :4])
# print(f'[INFO] top_index: {top_index.shape}')
# print(f'[INFO] top_conf: {top_conf.shape}')
# print(f'[INFO] top_angle: {top_angle.shape}')
# print(f'[INFO] top_bboxes: {top_bboxes.shape}')
top_xmin, top_ymin, top_xmax, top_ymax = np.expand_dims(
top_bboxes[:, 0],
-1), np.expand_dims(top_bboxes[:, 1], -1), np.expand_dims(
top_bboxes[:, 2], -1), np.expand_dims(top_bboxes[:, 3], -1)
# 去掉灰条
boxes = yolo_correct_boxes(
top_ymin, top_xmin, top_ymax, top_xmax,
np.array([self.model_image_size[0], self.model_image_size[1]]),
image_shape)
font = ImageFont.truetype(font='model_data/simhei.ttf', size=15)
thickness = (np.shape(image)[0] +
np.shape(image)[1]) // self.model_image_size[0]
predictions = []
for i, score in enumerate(top_conf):
infor = {}
top, left, bottom, right = boxes[i]
top = top - 5
left = left - 5
bottom = bottom + 5
right = right + 5
yaw, pitch, roll = top_angle[i] * 90
top = max(0, np.floor(top + 0.5).astype('int32'))
left = max(0, np.floor(left + 0.5).astype('int32'))
bottom = min(
np.shape(image)[0],
np.floor(bottom + 0.5).astype('int32'))
right = min(
np.shape(image)[1],
np.floor(right + 0.5).astype('int32'))
infor["box"] = [left, top, right, bottom]
infor["angle"][yaw, pitch, roll]
predictions.append(infor)
# draw box and angle in image
# draw = ImageDraw.Draw(image)
# for i in range(thickness):
# draw.rectangle(
# [left + i, top + i, right - i, bottom - i],
# outline="red")
# draw.text([left + i*10, top + i*10], str(score),
# fill=(255, 0, 0), font=font)
# del draw
# image_numpy = np.array(image)
# print(f'[PREDICT] box: {[top, left, bottom, right]}')
# print(f'[PREDICT] yaw = {yaw}, pitch = {pitch}, roll = {roll}')
# img = draw_axis(image_numpy, yaw, pitch, roll, (left+right)//2, (top + bottom)//2)
# image = Image.fromarray(img)
# image.save('test.jpg')
# print('save successfully !!!')
return predictions
def detect_image(self, image):
image_shape = np.array(np.shape(image)[0:2])
# 图片处理
crop_img = np.array(
letterbox_image(image,
(self.model_image_size[0],
self.model_image_size[1]))) # 对图片RESIZE并加灰条
photo = np.array(crop_img, dtype=np.float32)
photo /= 255.0 # 归一化
photo = np.transpose(photo, (2, 0, 1)) # 在pytorch中通道数在第一个，所以在这调整顺序
photo = photo.astype(np.float32) # 转换数据类型
images = []
images.append(photo)
images = np.asarray(images)
images = torch.from_numpy(images) # 将numpy转换成tenor类型
if self.cuda:
images = images.cuda()
# 放入网络中进行预测并画框
with torch.no_grad():
outputs = self.net(images) # 图片放入网络中
output_list = []
for i in range(3): # 特征层解码，因为特征金字塔有三个尺度的输出，所以要循环三次，将三个特征层全部解码。
output_list.append(self.yolo_decodes[i](
outputs[i])) # 解码：调整先验框
output = torch.cat(output_list, 1) # 将预测结果堆叠起来
batch_detections = non_max_suppression(
output,
self.config["yolo"]["classes"],
conf_thres=self.confidence,
nms_thres=0.3) # non_max_suppression()是进行非极大抑制
try:
batch_detections = batch_detections[0].cpu().numpy()
except:
return image
top_index = batch_detections[:,
4] * batch_detections[:,
5] > self.confidence # 将框框的置信度和类的置信度相乘进行判断
top_conf = batch_detections[top_index, 4] * batch_detections[
top_index, 5] # 下面这三行是将置信度较高的筛选出来
top_label = np.array(batch_detections[top_index, -1], np.int32)
top_bboxes = np.array(batch_detections[top_index, :4])
top_xmin, top_ymin, top_xmax, top_ymax = np.expand_dims(
top_bboxes[:, 0],
-1), np.expand_dims(top_bboxes[:, 1], -1), np.expand_dims(
top_bboxes[:, 2], -1), np.expand_dims(top_bboxes[:, 3], -1)
# 去掉灰条
'''
目前框框的位置是相对于有灰条图片左上角的位置。去掉灰条要转换为原图的左上角的位置。
yolo_correct_boxes函数就是完成这样的坐标变换
'''
boxes = yolo_correct_boxes(
top_ymin, top_xmin, top_ymax, top_xmax,
np.array([self.model_image_size[0], self.model_image_size[1]]),
image_shape)
font = ImageFont.truetype(font='model_data/simhei.ttf',
size=np.floor(3e-2 * np.shape(image)[1] +
0.5).astype('int32')) # 定义字体
thickness = (np.shape(image)[0] +
np.shape(image)[1]) // self.model_image_size[0] # 定义框框的宽度
# 下面的代码就是用来画图的
for i, c in enumerate(top_label):
predicted_class = self.class_names[c] # 获得类的名称
score = top_conf[i] # 获得得分
# 获得位置信息
top, left, bottom, right = boxes[i]
top = top - 5
left = left - 5
bottom = bottom + 5
right = right + 5
top = max(0, np.floor(top + 0.5).astype('int32'))
left = max(0, np.floor(left + 0.5).astype('int32'))
bottom = min(
np.shape(image)[0],
np.floor(bottom + 0.5).astype('int32'))
right = min(
np.shape(image)[1],
np.floor(right + 0.5).astype('int32'))
# 画框框
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
label = label.encode('utf-8')
print(label)
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[self.class_names.index(
predicted_class)])
draw.rectangle(
[tuple(text_origin),
tuple(text_origin + label_size)],
fill=self.colors[self.class_names.index(predicted_class)])
draw.text(text_origin,
str(label, 'UTF-8'),
fill=(0, 0, 0),
font=font) # 在框上写字
del draw
return image
