飞桨深度学习实践YOLOAI识虫
1.数据集处理部分
读取AI识虫数据集标注信息
AI识虫数据集结构如下：
提供了2183张图片，其中训练集1693张，验证集245，测试集245张。
包含7种昆虫，分别是Boerner、Leconte、Linnaeus、acuminatus、armandi、coleoptera和linnaeus。
包含了图片和标注，请读者先将数据解压，并存放在insects目录下。
# 解压数据脚本，第一次运行时打开注释，将文件解压到work目录下
!unzip -d /home/aistudio/work /home/aistudio/data/data19638/insects.zip
数据集中读取xml文件，将每张图片的标注信息读取出来。在读取具体的标注文件之前，我们先完成一件事情，就是将昆虫的类别名字（字符串）转化成数字表示的类别。因为神经网络里面计算时需要的输入类型是数值型的，所以需要将字符串表示的类别转化成具体的数字。昆虫类别名称的列表是：[‘Boerner’, ‘Leconte’, ‘Linnaeus’, ‘acuminatus’, ‘armandi’, ‘coleoptera’, ‘linnaeus’]，这里我们约定此列表中：'Boerner’对应类别0，'Leconte’对应类别1，…，'linnaeus’对应类别6。使用下面的程序可以得到表示名称字符串和数字类别之间映射关系的字典。
INSECT_NAMES = ['Boerner', 'Leconte', 'Linnaeus',
'acuminatus', 'armandi', 'coleoptera', 'linnaeus']
def get_insect_names():
"""
return a dict, as following,
{'Boerner': 0,
'Leconte': 1,
'Linnaeus': 2,
'acuminatus': 3,
'armandi': 4,
'coleoptera': 5,
'linnaeus': 6
}
It can map the insect name into an integer label.
"""
insect_category2id = {}
for i, item in enumerate(INSECT_NAMES):
insect_category2id[item] = i
return insect_category2id`
cname2cid = get_insect_names()
cname2cid
数据读取和预处理
### 数据读取
import cv2
def get_bbox(gt_bbox, gt_class):
# 对于一般的检测任务来说，一张图片上往往会有多个目标物体
# 设置参数MAX_NUM = 50， 即一张图片最多取50个真实框；如果真实
# 框的数目少于50个，则将不足部分的gt_bbox, gt_class和gt_score的各项数值全设置为0
MAX_NUM = 50
gt_bbox2 = np.zeros((MAX_NUM, 4))
gt_class2 = np.zeros((MAX_NUM,))
for i in range(len(gt_bbox)):
gt_bbox2[i, :] = gt_bbox[i, :]
gt_class2[i] = gt_class[i]
if i >= MAX_NUM:
break
return gt_bbox2, gt_class2
def get_img_data_from_file(record):#读取真实框的信息
"""
record is a dict as following,
record = {
'im_file': img_file,
'im_id': im_id,
'h': im_h,
'w': im_w,
'is_crowd': is_crowd,
'gt_class': gt_class,
'gt_bbox': gt_bbox,
'gt_poly': [],
'difficult': difficult
}
"""
im_file = record['im_file']
h = record['h']
w = record['w']
is_crowd = record['is_crowd']
gt_class = record['gt_class']
gt_bbox = record['gt_bbox']
difficult = record['difficult']
img = cv2.imread(im_file)#opencv2读取文件路径
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)#把文件BGR格式转为RGB格式
# check if h and w in record equals that read from img
assert img.shape[0] == int(h), \
"image height of {} inconsistent in record({}) and img file({})".format(
im_file, h, img.shape[0])#看读取出来的数据是否和标注的信息是否一致，进行高的检查
assert img.shape[1] == int(w), \
"image width of {} inconsistent in record({}) and img file({})".format(
im_file, w, img.shape[1])#看读取出来的数据是否和标注的信息是否一致，进行宽的检查
gt_boxes, gt_labels = get_bbox(gt_bbox, gt_class)
# gt_bbox 用相对值
gt_boxes[:, 0] = gt_boxes[:, 0] / float(w)
gt_boxes[:, 1] = gt_boxes[:, 1] / float(h)
gt_boxes[:, 2] = gt_boxes[:, 2] / float(w)
gt_boxes[:, 3] = gt_boxes[:, 3] / float(h)
return img, gt_boxes, gt_labels, (h, w)
数据预处理
在计算机视觉中，通常会对图像做一些随机的变化，产生相似但又不完全相同的样本。主要作用是扩大训练数据集，抑制过拟合，提升模型的泛化能力，常用的方法见下面的程序。
随机改变亮暗、对比度和颜色等`
import numpy as np
import cv2
from PIL import Image, ImageEnhance
import random
# 随机改变亮暗、对比度和颜色等
def random_distort(img):
# 随机改变亮度
def random_brightness(img, lower=0.5, upper=1.5):
e = np.random.uniform(lower, upper)
return ImageEnhance.Brightness(img).enhance(e)
# 随机改变对比度
def random_contrast(img, lower=0.5, upper=1.5):
e = np.random.uniform(lower, upper)
return ImageEnhance.Contrast(img).enhance(e)
# 随机改变颜色
def random_color(img, lower=0.5, upper=1.5):
e = np.random.uniform(lower, upper)
return ImageEnhance.Color(img).enhance(e)
ops = [random_brightness, random_contrast, random_color]
np.random.shuffle(ops)
img = Image.fromarray(img)
img = ops[0](img)
img = ops[1](img)
img = ops[2](img)
img = np.asarray(img)
return img
随机填充
随机填充
def random_expand(img,
gtboxes,
max_ratio=4.,
fill=None,
keep_ratio=True,
thresh=0.5):
if random.random() > thresh:
return img, gtboxes
if max_ratio < 1.0:
return img, gtboxes
h, w, c = img.shape
ratio_x = random.uniform(1, max_ratio)
if keep_ratio:
ratio_y = ratio_x
else:
ratio_y = random.uniform(1, max_ratio)
oh = int(h * ratio_y)
ow = int(w * ratio_x)
off_x = random.randint(0, ow - w)
off_y = random.randint(0, oh - h)
out_img = np.zeros((oh, ow, c))
if fill and len(fill) == c:
for i in range(c):
out_img[:, :, i] = fill[i] * 255.0
out_img[off_y:off_y + h, off_x:off_x + w, :] = img
gtboxes[:, 0] = ((gtboxes[:, 0] * w) + off_x) / float(ow)
gtboxes[:, 1] = ((gtboxes[:, 1] * h) + off_y) / float(oh)
gtboxes[:, 2] = gtboxes[:, 2] / ratio_x
gtboxes[:, 3] = gtboxes[:, 3] / ratio_y
return out_img.astype('uint8'), gtboxes
随机裁剪
import numpy as np
def multi_box_iou_xywh(box1, box2):
"""
In this case, box1 or box2 can contain multi boxes.
Only two cases can be processed in this method:
1, box1 and box2 have the same shape, box1.shape == box2.shape
2, either box1 or box2 contains only one box, len(box1) == 1 or len(box2) == 1
If the shape of box1 and box2 does not match, and both of them contain multi boxes, it will be wrong.
"""
assert box1.shape[-1] == 4, "Box1 shape[-1] should be 4."
assert box2.shape[-1] == 4, "Box2 shape[-1] should be 4."
b1_x1, b1_x2 = box1[:, 0] - box1[:, 2] / 2, box1[:, 0] + box1[:, 2] / 2
b1_y1, b1_y2 = box1[:, 1] - box1[:, 3] / 2, box1[:, 1] + box1[:, 3] / 2
b2_x1, b2_x2 = box2[:, 0] - box2[:, 2] / 2, box2[:, 0] + box2[:, 2] / 2
b2_y1, b2_y2 = box2[:, 1] - box2[:, 3] / 2, box2[:, 1] + box2[:, 3] / 2
inter_x1 = np.maximum(b1_x1, b2_x1)
inter_x2 = np.minimum(b1_x2, b2_x2)
inter_y1 = np.maximum(b1_y1, b2_y1)
inter_y2 = np.minimum(b1_y2, b2_y2)
inter_w = inter_x2 - inter_x1
inter_h = inter_y2 - inter_y1
inter_w = np.clip(inter_w, a_min=0., a_max=None)
inter_h = np.clip(inter_h, a_min=0., a_max=None)
inter_area = inter_w * inter_h
b1_area = (b1_x2 - b1_x1) * (b1_y2 - b1_y1)
b2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1)
return inter_area / (b1_area + b2_area - inter_area)
def box_crop(boxes, labels, crop, img_shape):
x, y, w, h = map(float, crop)
im_w, im_h = map(float, img_shape)
boxes = boxes.copy()
boxes[:, 0], boxes[:, 2] = (boxes[:, 0] - boxes[:, 2] / 2) * im_w, (
boxes[:, 0] + boxes[:, 2] / 2) * im_w
boxes[:, 1], boxes[:, 3] = (boxes[:, 1] - boxes[:, 3] / 2) * im_h, (
boxes[:, 1] + boxes[:, 3] / 2) * im_h
crop_box = np.array([x, y, x + w, y + h])
centers = (boxes[:, :2] + boxes[:, 2:]) / 2.0
mask = np.logical_and(crop_box[:2] <= centers, centers <= crop_box[2:]).all(
axis=1)
boxes[:, :2] = np.maximum(boxes[:, :2], crop_box[:2])
boxes[:, 2:] = np.minimum(boxes[:, 2:], crop_box[2:])
boxes[:, :2] -= crop_box[:2]
boxes[:, 2:] -= crop_box[:2]
mask = np.logical_and(mask, (boxes[:, :2] < boxes[:, 2:]).all(axis=1))
boxes = boxes * np.expand_dims(mask.astype('float32'), axis=1)
labels = labels * mask.astype('float32')
boxes[:, 0], boxes[:, 2] = (boxes[:, 0] + boxes[:, 2]) / 2 / w, (
boxes[:, 2] - boxes[:, 0]) / w
boxes[:, 1], boxes[:, 3] = (boxes[:, 1] + boxes[:, 3]) / 2 / h, (
boxes[:, 3] - boxes[:, 1]) / h
return boxes, labels, mask.sum()
# 随机裁剪
def random_crop(img,
boxes,
labels,
scales=[0.3, 1.0],
max_ratio=2.0,
constraints=None,
max_trial=50):
if len(boxes) == 0:
return img, boxes
if not constraints:
constraints = [(0.1, 1.0), (0.3, 1.0), (0.5, 1.0), (0.7, 1.0),
(0.9, 1.0), (0.0, 1.0)]
img = Image.fromarray(img)
w, h = img.size
crops = [(0, 0, w, h)]
for min_iou, max_iou in constraints:
for _ in range(max_trial):
scale = random.uniform(scales[0], scales[1])
aspect_ratio = random.uniform(max(1 / max_ratio, scale * scale), \
min(max_ratio, 1 / scale / scale))
crop_h = int(h * scale / np.sqrt(aspect_ratio))
crop_w = int(w * scale * np.sqrt(aspect_ratio))
crop_x = random.randrange(w - crop_w)
crop_y = random.randrange(h - crop_h)
crop_box = np.array([[(crop_x + crop_w / 2.0) / w,
(crop_y + crop_h / 2.0) / h,
crop_w / float(w), crop_h / float(h)]])
iou = multi_box_iou_xywh(crop_box, boxes)
if min_iou <= iou.min() and max_iou >= iou.max():
crops.append((crop_x, crop_y, crop_w, crop_h))
break
while crops:
crop = crops.pop(np.random.randint(0, len(crops)))
crop_boxes, crop_labels, box_num = box_crop(boxes, labels, crop, (w, h))
if box_num < 1:
continue
img = img.crop((crop[0], crop[1], crop[0] + crop[2],
crop[1] + crop[3])).resize(img.size, Image.LANCZOS)
img = np.asarray(img)
return img, crop_boxes, crop_labels
img = np.asarray(img)
return img, boxes, labels
随机缩放`
# 随机缩放
def random_interp(img, size, interp=None):
interp_method = [
cv2.INTER_NEAREST,
cv2.INTER_LINEAR,
cv2.INTER_AREA,
cv2.INTER_CUBIC,
cv2.INTER_LANCZOS4,
]
if not interp or interp not in interp_method:
interp = interp_method[random.randint(0, len(interp_method) - 1)]
h, w, _ = img.shape
im_scale_x = size / float(w)
im_scale_y = size / float(h)
img = cv2.resize(
img, None, None, fx=im_scale_x, fy=im_scale_y, interpolation=interp)
return img
随机翻转
# 随机翻转
def random_flip(img, gtboxes, thresh=0.5):
if random.random() > thresh:
img = img[:, ::-1, :]
gtboxes[:, 0] = 1.0 - gtboxes[:, 0]
return img, gtboxes
随机打乱真实框排列顺序
# 随机打乱真实框排列顺序
def shuffle_gtbox(gtbox, gtlabel):
gt = np.concatenate(
[gtbox, gtlabel[:, np.newaxis]], axis=1)
idx = np.arange(gt.shape[0])
np.random.shuffle(idx)
gt = gt[idx, :]
return gt[:, :4], gt[:, 4]
图像增广方法汇总
# 图像增广方法汇总
def image_augment(img, gtboxes, gtlabels, size, means=None):
# 随机改变亮暗、对比度和颜色等
img = random_distort(img)
# 随机填充
img, gtboxes = random_expand(img, gtboxes, fill=means)
# 随机裁剪
img, gtboxes, gtlabels, = random_crop(img, gtboxes, gtlabels)
# 随机缩放
img = random_interp(img, size)
# 随机翻转
img, gtboxes = random_flip(img, gtboxes)
# 随机打乱真实框排列顺序
gtboxes, gtlabels = shuffle_gtbox(gtboxes, gtlabels)
return img.astype('float32'), gtboxes.astype('float32'), gtlabels.astype('int32')