[图像算法]-目标检测三
2021-02-02 本文已影响0人
六千宛
预测
预测过程流程 图21 如下所示：
图21：端到端训练流程
预测过程可以分为两步：
1.通过网络输出计算出预测框位置和所属类别的得分。
2.使用非极大值抑制来消除重叠较大的预测框。
对于第1步，前面我们已经讲过如何通过网络输出值计算pred_objectness_probability, pred_boxes以及pred_classification_probability，这里推荐大家直接使用fluid.layers.yolo_box，其使用方法是：
fluid.layers.yolo_box(x, img_size, anchors, class_num, conf_thresh, downsample_ratio, name=None)
x，网络输出特征图，例如上面提到的P0或者P1、P2
img_size，输入图片尺寸
anchors，使用到的anchor的尺寸，如[10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326]
anchor_mask: 每个层级上使用的anchor的掩码，[[6, 7, 8], [3, 4, 5], [0, 1, 2]]
class_num，物体类别数目
conf_thresh, 置信度阈值，得分低于该阈值的预测框位置数值不用计算直接设置为0.0
downsample_ratio, 特征图的下采样比例，例如P0是32，P1是16，P2是8
name=None，名字，例如'yolo_box'
返回值包括两项，boxes和scores，其中boxes是所有预测框的坐标值，scores是所有预测框的得分。
image.png
# 定义YOLO-V3模型
class YOLOv3(fluid.dygraph.Layer):
def __init__(self, num_classes=7, is_train=True):
super(YOLOv3,self).__init__()
self.is_train = is_train
self.num_classes = num_classes
# 提取图像特征的骨干代码
self.block = DarkNet53_conv_body(
is_test = not self.is_train)
self.block_outputs = []
self.yolo_blocks = []
self.route_blocks_2 = []
# 生成3个层级的特征图P0, P1, P2
for i in range(3):
# 添加从ci生成ri和ti的模块
yolo_block = self.add_sublayer(
"yolo_detecton_block_%d" % (i),
YoloDetectionBlock(
ch_in=512//(2**i)*2 if i==0 else 512//(2**i)*2 + 512//(2**i),
ch_out = 512//(2**i),
is_test = not self.is_train))
self.yolo_blocks.append(yolo_block)
num_filters = 3 * (self.num_classes + 5)
# 添加从ti生成pi的模块，这是一个Conv2D操作，输出通道数为3 * (num_classes + 5)
block_out = self.add_sublayer(
"block_out_%d" % (i),
Conv2D(num_channels=512//(2**i)*2,
num_filters=num_filters,
filter_size=1,
stride=1,
padding=0,
act=None,
param_attr=ParamAttr(
initializer=fluid.initializer.Normal(0., 0.02)),
bias_attr=ParamAttr(
initializer=fluid.initializer.Constant(0.0),
regularizer=L2Decay(0.))))
self.block_outputs.append(block_out)
if i < 2:
# 对ri进行卷积
route = self.add_sublayer("route2_%d"%i,
ConvBNLayer(ch_in=512//(2**i),
ch_out=256//(2**i),
filter_size=1,
stride=1,
padding=0,
is_test=(not self.is_train)))
self.route_blocks_2.append(route)
# 将ri放大以便跟c_{i+1}保持同样的尺寸
self.upsample = Upsample()
def forward(self, inputs):
outputs = []
blocks = self.block(inputs)
for i, block in enumerate(blocks):
if i > 0:
# 将r_{i-1}经过卷积和上采样之后得到特征图，与这一级的ci进行拼接
block = fluid.layers.concat(input=[route, block], axis=1)
# 从ci生成ti和ri
route, tip = self.yolo_blocks[i](block)
# 从ti生成pi
block_out = self.block_outputs[i](tip)
# 将pi放入列表
outputs.append(block_out)
if i < 2:
# 对ri进行卷积调整通道数
route = self.route_blocks_2[i](route)
# 对ri进行放大，使其尺寸和c_{i+1}保持一致
route = self.upsample(route)
return outputs
def get_loss(self, outputs, gtbox, gtlabel, gtscore=None,
anchors = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326],
anchor_masks = [[6, 7, 8], [3, 4, 5], [0, 1, 2]],
ignore_thresh=0.7,
use_label_smooth=False):
self.losses = []
downsample = 32
for i, out in enumerate(outputs):
anchor_mask_i = anchor_masks[i]
loss = fluid.layers.yolov3_loss(
x=out,
gt_box=gtbox,
gt_label=gtlabel,
gt_score=gtscore,
anchors=anchors,
anchor_mask=anchor_mask_i,
class_num=self.num_classes,
ignore_thresh=ignore_thresh,
downsample_ratio=downsample,
use_label_smooth=False)
self.losses.append(fluid.layers.reduce_mean(loss))
downsample = downsample // 2
return sum(self.losses)
def get_pred(self,
outputs,
im_shape=None,
anchors = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326],
anchor_masks = [[6, 7, 8], [3, 4, 5], [0, 1, 2]],
valid_thresh = 0.01):
downsample = 32
total_boxes = []
total_scores = []
for i, out in enumerate(outputs):
anchor_mask = anchor_masks[i]
anchors_this_level = []
for m in anchor_mask:
anchors_this_level.append(anchors[2 * m])
anchors_this_level.append(anchors[2 * m + 1])
boxes, scores = fluid.layers.yolo_box(
x=out,
img_size=im_shape,
anchors=anchors_this_level,
class_num=self.num_classes,
conf_thresh=valid_thresh,
downsample_ratio=downsample,
name="yolo_box" + str(i))
total_boxes.append(boxes)
total_scores.append(
fluid.layers.transpose(
scores, perm=[0, 2, 1]))
downsample = downsample // 2
yolo_boxes = fluid.layers.concat(total_boxes, axis=1)
yolo_scores = fluid.layers.concat(total_scores, axis=2)
return yolo_boxes, yolo_scores
第1步的计算结果会在每个小方块区域都会产生多个预测框，输出预测框中会有很多重合度比较大，需要消除重叠较大的冗余预测框。
下面示例代码中的预测框是使用模型对图片预测之后输出的，这里一共选出了11个预测框，在图上画出预测框如下所示。在每个人像周围，都出现了多个预测框，需要消除冗余的预测框以得到最终的预测结果。
# 画图展示目标物体边界框
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from matplotlib.image import imread
import math
# 定义画矩形框的程序
def draw_rectangle(currentAxis, bbox, edgecolor = 'k', facecolor = 'y', fill=False, linestyle='-'):
# currentAxis，坐标轴，通过plt.gca()获取
# bbox，边界框，包含四个数值的list， [x1, y1, x2, y2]
# edgecolor，边框线条颜色
# facecolor，填充颜色
# fill, 是否填充
# linestype，边框线型
# patches.Rectangle需要传入左上角坐标、矩形区域的宽度、高度等参数
rect=patches.Rectangle((bbox[0], bbox[1]), bbox[2]-bbox[0]+1, bbox[3]-bbox[1]+1, linewidth=1,
edgecolor=edgecolor,facecolor=facecolor,fill=fill, linestyle=linestyle)
currentAxis.add_patch(rect)
plt.figure(figsize=(10, 10))
filename = '/home/aistudio/work/images/section3/000000086956.jpg'
im = imread(filename)
plt.imshow(im)
currentAxis=plt.gca()
# 预测框位置
boxes = np.array([[4.21716537e+01, 1.28230896e+02, 2.26547668e+02, 6.00434631e+02],
[3.18562988e+02, 1.23168472e+02, 4.79000000e+02, 6.05688416e+02],
[2.62704697e+01, 1.39430557e+02, 2.20587097e+02, 6.38959656e+02],
[4.24965363e+01, 1.42706665e+02, 2.25955185e+02, 6.35671204e+02],
[2.37462646e+02, 1.35731537e+02, 4.79000000e+02, 6.31451294e+02],
[3.19390472e+02, 1.29295090e+02, 4.79000000e+02, 6.33003845e+02],
[3.28933838e+02, 1.22736115e+02, 4.79000000e+02, 6.39000000e+02],
[4.44292603e+01, 1.70438187e+02, 2.26841858e+02, 6.39000000e+02],
[2.17988785e+02, 3.02472412e+02, 4.06062927e+02, 6.29106628e+02],
[2.00241089e+02, 3.23755096e+02, 3.96929321e+02, 6.36386108e+02],
[2.14310303e+02, 3.23443665e+02, 4.06732849e+02, 6.35775269e+02]])
# 预测框得分
scores = np.array([0.5247661 , 0.51759845, 0.86075854, 0.9910175 , 0.39170712,
0.9297706 , 0.5115228 , 0.270992 , 0.19087596, 0.64201415, 0.879036])
# 画出所有预测框
for box in boxes:
draw_rectangle(currentAxis, box)
image.png
这里使用非极大值抑制（non-maximum suppression, nms）来消除冗余框，其基本思想是，如果有多个预测框都对应同一个物体，则只选出得分最高的那个预测框，剩下的预测框被丢弃掉。那么如何判断两个预测框对应的是同一个物体呢，标准该怎么设置？如果两个预测框的类别一样，而且他们的位置重合度比较大，则可以认为他们是在预测同一个目标。非极大值抑制的做法是，选出某个类别得分最高的预测框，然后看哪些预测框跟它的IoU大于阈值，就把这些预测框给丢弃掉。这里IoU的阈值是超参数，需要提前设置，YOLO-V3模型里面设置的是0.5。
比如在上面的程序中，boxes里面一共对应11个预测框，scores给出了它们预测"人"这一类别的得分。
Step0 创建选中列表，keep_list = []
Step1 对得分进行排序，remain_list = [ 3, 5, 10, 2, 9, 0, 1, 6, 4, 7, 8]，
Step2 选出boxes[3]，此时keep_list为空，不需要计算IoU，直接将其放入keep_list，keep_list = [3]， remain_list=[5, 10, 2, 9, 0, 1, 6, 4, 7, 8]
Step3 选出boxes[5]，此时keep_list中已经存在boxes[3]，计算出IoU(boxes[3], boxes[5]) = 0.0，显然小于阈值，则keep_list=[3, 5], remain_list = [10, 2, 9, 0, 1, 6, 4, 7, 8]
Step4 选出boxes[10]，此时keep_list=[3, 5]，计算IoU(boxes[3], boxes[10])=0.0268，IoU(boxes[5], boxes[10])=0.0268 = 0.24，都小于阈值，则keep_list = [3, 5, 10]，remain_list=[2, 9, 0, 1, 6, 4, 7, 8]
Step5 选出boxes[2]，此时keep_list = [3, 5, 10]，计算IoU(boxes[3], boxes[2]) = 0.88，超过了阈值，直接将boxes[2]丢弃，keep_list=[3, 5, 10]，remain_list=[9, 0, 1, 6, 4, 7, 8]
Step6 选出boxes[9]，此时keep_list = [3, 5, 10]，计算IoU(boxes[3], boxes[9]) = 0.0577，IoU(boxes[5], boxes[9]) = 0.205，IoU(boxes[10], boxes[9]) = 0.88，超过了阈值，将boxes[9]丢弃掉。keep_list=[3, 5, 10]，remain_list=[0, 1, 6, 4, 7, 8]
Step7 重复上述Step6直到remain_list为空
最终得到keep_list=[3, 5, 10]，也就是预测框3、5、10被最终挑选出来了，如下图所示
# 画图展示目标物体边界框
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from matplotlib.image import imread
import math
# 定义画矩形框的程序
def draw_rectangle(currentAxis, bbox, edgecolor = 'k', facecolor = 'y', fill=False, linestyle='-'):
# currentAxis，坐标轴，通过plt.gca()获取
# bbox，边界框，包含四个数值的list， [x1, y1, x2, y2]
# edgecolor，边框线条颜色
# facecolor，填充颜色
# fill, 是否填充
# linestype，边框线型
# patches.Rectangle需要传入左上角坐标、矩形区域的宽度、高度等参数
rect=patches.Rectangle((bbox[0], bbox[1]), bbox[2]-bbox[0]+1, bbox[3]-bbox[1]+1, linewidth=1,
edgecolor=edgecolor,facecolor=facecolor,fill=fill, linestyle=linestyle)
currentAxis.add_patch(rect)
plt.figure(figsize=(10, 10))
filename = '/home/aistudio/work/images/section3/000000086956.jpg'
im = imread(filename)
plt.imshow(im)
currentAxis=plt.gca()
boxes = np.array([[4.21716537e+01, 1.28230896e+02, 2.26547668e+02, 6.00434631e+02],
[3.18562988e+02, 1.23168472e+02, 4.79000000e+02, 6.05688416e+02],
[2.62704697e+01, 1.39430557e+02, 2.20587097e+02, 6.38959656e+02],
[4.24965363e+01, 1.42706665e+02, 2.25955185e+02, 6.35671204e+02],
[2.37462646e+02, 1.35731537e+02, 4.79000000e+02, 6.31451294e+02],
[3.19390472e+02, 1.29295090e+02, 4.79000000e+02, 6.33003845e+02],
[3.28933838e+02, 1.22736115e+02, 4.79000000e+02, 6.39000000e+02],
[4.44292603e+01, 1.70438187e+02, 2.26841858e+02, 6.39000000e+02],
[2.17988785e+02, 3.02472412e+02, 4.06062927e+02, 6.29106628e+02],
[2.00241089e+02, 3.23755096e+02, 3.96929321e+02, 6.36386108e+02],
[2.14310303e+02, 3.23443665e+02, 4.06732849e+02, 6.35775269e+02]])
scores = np.array([0.5247661 , 0.51759845, 0.86075854, 0.9910175 , 0.39170712,
0.9297706 , 0.5115228 , 0.270992 , 0.19087596, 0.64201415, 0.879036])
left_ind = np.where((boxes[:, 0]<60) * (boxes[:, 0]>20))
left_boxes = boxes[left_ind]
left_scores = scores[left_ind]
colors = ['r', 'g', 'b', 'k']
# 画出最终保留的预测框
inds = [3, 5, 10]
for i in range(3):
box = boxes[inds[i]]
draw_rectangle(currentAxis, box, edgecolor=colors[i])
image.png
非极大值抑制的具体实现代码如下面nms函数的定义，需要说明的是数据集中含有多个类别的物体，所以这里需要做多分类非极大值抑制，其实现原理与非极大值抑制相同，区别在于需要对每个类别都做非极大值抑制，实现代码如下面的multiclass_nms所示。
# 非极大值抑制
def nms(bboxes, scores, score_thresh, nms_thresh, pre_nms_topk, i=0, c=0):
"""
nms
"""
inds = np.argsort(scores)
inds = inds[::-1]
keep_inds = []
while(len(inds) > 0):
cur_ind = inds[0]
cur_score = scores[cur_ind]
# if score of the box is less than score_thresh, just drop it
if cur_score < score_thresh:
break
keep = True
for ind in keep_inds:
current_box = bboxes[cur_ind]
remain_box = bboxes[ind]
iou = box_iou_xyxy(current_box, remain_box)
if iou > nms_thresh:
keep = False
break
if i == 0 and c == 4 and cur_ind == 951:
print('suppressed, ', keep, i, c, cur_ind, ind, iou)
if keep:
keep_inds.append(cur_ind)
inds = inds[1:]
return np.array(keep_inds)
# 多分类非极大值抑制
def multiclass_nms(bboxes, scores, score_thresh=0.01, nms_thresh=0.45, pre_nms_topk=1000, pos_nms_topk=100):
"""
This is for multiclass_nms
"""
batch_size = bboxes.shape[0]
class_num = scores.shape[1]
rets = []
for i in range(batch_size):
bboxes_i = bboxes[i]
scores_i = scores[i]
ret = []
for c in range(class_num):
scores_i_c = scores_i[c]
keep_inds = nms(bboxes_i, scores_i_c, score_thresh, nms_thresh, pre_nms_topk, i=i, c=c)
if len(keep_inds) < 1:
continue
keep_bboxes = bboxes_i[keep_inds]
keep_scores = scores_i_c[keep_inds]
keep_results = np.zeros([keep_scores.shape[0], 6])
keep_results[:, 0] = c
keep_results[:, 1] = keep_scores[:]
keep_results[:, 2:6] = keep_bboxes[:, :]
ret.append(keep_results)
if len(ret) < 1:
rets.append(ret)
continue
ret_i = np.concatenate(ret, axis=0)
scores_i = ret_i[:, 1]
if len(scores_i) > pos_nms_topk:
inds = np.argsort(scores_i)[::-1]
inds = inds[:pos_nms_topk]
ret_i = ret_i[inds]
rets.append(ret_i)
return rets
下面是完整的测试程序，在测试数据集上的输出结果将会被保存在pred_results.json文件中。
import json
import os
ANCHORS = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326]
ANCHOR_MASKS = [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
VALID_THRESH = 0.01
NMS_TOPK = 400
NMS_POSK = 100
NMS_THRESH = 0.45
NUM_CLASSES = 7
if __name__ == '__main__':
TRAINDIR = '/home/aistudio/work/insects/train/images'
TESTDIR = '/home/aistudio/work/insects/test/images'
VALIDDIR = '/home/aistudio/work/insects/val'
with fluid.dygraph.guard():
model = YOLOv3(num_classes=NUM_CLASSES, is_train=False)
params_file_path = '/home/aistudio/work/yolo_epoch50'
model_state_dict, _ = fluid.load_dygraph(params_file_path)
model.load_dict(model_state_dict)
model.eval()
total_results = []
test_loader = test_data_loader(TESTDIR, batch_size= 1, mode='test')
for i, data in enumerate(test_loader()):
img_name, img_data, img_scale_data = data
img = to_variable(img_data)
img_scale = to_variable(img_scale_data)
outputs = model.forward(img)
bboxes, scores = model.get_pred(outputs,
im_shape=img_scale,
anchors=ANCHORS,
anchor_masks=ANCHOR_MASKS,
valid_thresh = VALID_THRESH)
bboxes_data = bboxes.numpy()
scores_data = scores.numpy()
result = multiclass_nms(bboxes_data, scores_data,
score_thresh=VALID_THRESH,
nms_thresh=NMS_THRESH,
pre_nms_topk=NMS_TOPK,
pos_nms_topk=NMS_POSK)
for j in range(len(result)):
result_j = result[j]
img_name_j = img_name[j]
total_results.append([img_name_j, result_j.tolist()])
print('processed {} pictures'.format(len(total_results)))
print('')
json.dump(total_results, open('pred_results.json', 'w'))
json文件中保存着测试结果，是包含所有图片预测结果的list，其构成如下：
[[img_name, [[label, score, x1, x2, y1, y2], ..., [label, score, x1, x2, y1, y2]]],
[img_name, [[label, score, x1, x2, y1, y2], ..., [label, score, x1, x2, y1, y2]]],
...
[img_name, [[label, score, x1, x2, y1, y2],..., [label, score, x1, x2, y1, y2]]]]
list中的每一个元素是一张图片的预测结果，list的总长度等于图片的数目，每张图片预测结果的格式是：
[img_name, [[label, score, x1, x2, y1, y2],..., [label, score, x1, x2, y1, y2]]]
其中第一个元素是图片名称image_name，第二个元素是包含该图片所有预测框的list， 预测框列表：
[[label, score, x1, x2, y1, y2],..., [label, score, x1, x2, y1, y2]]
预测框列表中每个元素[label, score, x1, x2, y1, y2]描述了一个预测框，label是预测框所属类别标签，score是预测框的得分；x1, x2, y1, y2对应预测框左上角坐标(x1, y1)，右下角坐标(x2, y2)。每张图片可能有很多个预测框，则将其全部放在预测框列表中。
在AI识虫比赛的基础版本中，老师提供了mAP指标计算代码，使用此pred_results.json文件即可计算出最终的评估指标。
模型效果及可视化展示
上面的程序展示了如何读取测试数据集的读片，并将最终结果保存在json格式的文件中。为了更直观的给读者展示模型效果，下面的程序添加了如何读取单张图片，并画出其产生的预测框。
1.创建数据读取器以读取单张图片的数据
# 读取单张测试图片
def single_image_data_loader(filename, test_image_size=608, mode='test'):
"""
加载测试用的图片，测试数据没有groundtruth标签
"""
batch_size= 1
def reader():
batch_data = []
img_size = test_image_size
file_path = os.path.join(filename)
img = cv2.imread(file_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
H = img.shape[0]
W = img.shape[1]
img = cv2.resize(img, (img_size, img_size))
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
mean = np.array(mean).reshape((1, 1, -1))
std = np.array(std).reshape((1, 1, -1))
out_img = (img / 255.0 - mean) / std
out_img = out_img.astype('float32').transpose((2, 0, 1))
img = out_img #np.transpose(out_img, (2,0,1))
im_shape = [H, W]
batch_data.append((image_name.split('.')[0], img, im_shape))
if len(batch_data) == batch_size:
yield make_test_array(batch_data)
batch_data = []
return reader
2.定义绘制预测框的画图函数，代码如下
# 定义画图函数
INSECT_NAMES = ['Boerner', 'Leconte', 'Linnaeus',
'acuminatus', 'armandi', 'coleoptera', 'linnaeus']
# 定义画矩形框的函数
def draw_rectangle(currentAxis, bbox, edgecolor = 'k', facecolor = 'y', fill=False, linestyle='-'):
# currentAxis，坐标轴，通过plt.gca()获取
# bbox，边界框，包含四个数值的list， [x1, y1, x2, y2]
# edgecolor，边框线条颜色
# facecolor，填充颜色
# fill, 是否填充
# linestype，边框线型
# patches.Rectangle需要传入左上角坐标、矩形区域的宽度、高度等参数
rect=patches.Rectangle((bbox[0], bbox[1]), bbox[2]-bbox[0]+1, bbox[3]-bbox[1]+1, linewidth=1,
edgecolor=edgecolor,facecolor=facecolor,fill=fill, linestyle=linestyle)
currentAxis.add_patch(rect)
# 定义绘制预测结果的函数
def draw_results(result, filename, draw_thresh=0.5):
plt.figure(figsize=(10, 10))
im = imread(filename)
plt.imshow(im)
currentAxis=plt.gca()
colors = ['r', 'g', 'b', 'k', 'y', 'c', 'purple']
for item in result:
box = item[2:6]
label = int(item[0])
name = INSECT_NAMES[label]
if item[1] > draw_thresh:
draw_rectangle(currentAxis, box, edgecolor = colors[label])
plt.text(box[0], box[1], name, fontsize=12, color=colors[label])
3.使用上面定义的single_image_data_loader函数读取指定的图片，输入网络并计算出预测框和得分，然后使用多分类非极大值抑制消除冗余的框。将最终结果画图展示出来
import json
import paddle
import paddle.fluid as fluid
ANCHORS = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326]
ANCHOR_MASKS = [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
VALID_THRESH = 0.01
NMS_TOPK = 400
NMS_POSK = 100
NMS_THRESH = 0.45
NUM_CLASSES = 7
if __name__ == '__main__':
image_name = '/home/aistudio/work/insects/test/images/2599.jpeg'
params_file_path = '/home/aistudio/work/yolo_epoch50'
with fluid.dygraph.guard():
model = YOLOv3(num_classes=NUM_CLASSES, is_train=False)
model_state_dict, _ = fluid.load_dygraph(params_file_path)
model.load_dict(model_state_dict)
model.eval()
total_results = []
test_loader = single_image_data_loader(image_name, mode='test')
for i, data in enumerate(test_loader()):
img_name, img_data, img_scale_data = data
img = to_variable(img_data)
img_scale = to_variable(img_scale_data)
outputs = model.forward(img)
bboxes, scores = model.get_pred(outputs,
im_shape=img_scale,
anchors=ANCHORS,
anchor_masks=ANCHOR_MASKS,
valid_thresh = VALID_THRESH)
bboxes_data = bboxes.numpy()
scores_data = scores.numpy()
results = multiclass_nms(bboxes_data, scores_data,
score_thresh=VALID_THRESH,
nms_thresh=NMS_THRESH,
pre_nms_topk=NMS_TOPK,
pos_nms_topk=NMS_POSK)
result = results[0]
draw_results(result, image_name, draw_thresh=0.5)
通过上面的程序，清晰的给读者展示了如何使用训练好的权重，对图片进行预测并将结果可视化。最终输出的图片上，检测出了每个昆虫，标出了它们的边界框和具体类别。
