该内容围绕小麦和昆虫检测数据集展开探索性数据分析（EDA）。先进行环境准备与数据集解压，接着分析数据整体分布，涵盖图片数量、类别、尺寸等，还探究了图像分辨率、亮度、目标分布、单张图片目标情况、目标遮挡及颜色等，最后实现了VOC到COCO格式的转换。
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# 调用一些需要的第三方库import numpy as npimport pandas as pdimport shutilimport jsonimport osimport cv2import globimport matplotlib.pyplot as pltimport matplotlib.patches as patchesimport seaborn as snsfrom matplotlib.font_manager import FontPropertiesfrom PIL import Imageimport random myfont = FontProperties(fname=r"NotoSansCJKsc-Medium.otf", size=12) plt.rcParams['figure.figsize'] = (12, 12) plt.rcParams['font.family']= myfont.get_family() plt.rcParams['font.sans-serif'] = myfont.get_name() plt.rcParams['axes.unicode_minus'] = False
# !unzip data/data54680/coco.zip
!unzip data/data42353/wheat.zip
# Setup the paths to train and test imagesTRAIN_DIR = 'wheat/train/'TRAIN_CSV_PATH = 'wheat/train.json'# Glob the directories and get the lists of train and test imagestrain_fns = glob.glob(TRAIN_DIR + '*')print('数据集图片数量: {}'.format(len(train_fns)))数据集图片数量: 3422
def generate_anno_eda(dataset_path, anno_file):
with open(os.path.join(dataset_path, anno_file)) as f:
anno = json.load(f) print('标签类别:', anno['categories']) print('类别数量：', len(anno['categories'])) print('训练集图片数量：', len(anno['images'])) print('训练集标签数量：', len(anno['annotations']))
total=[] for img in anno['images']:
hw = (img['height'],img['width'])
total.append(hw)
unique = set(total) for k in unique: print('长宽为(%d,%d)的图片数量为：'%k,total.count(k))
ids=[]
images_id=[] for i in anno['annotations']:
ids.append(i['id'])
images_id.append(i['image_id']) print('训练集图片数量:', len(anno['images'])) print('unique id 数量：', len(set(ids))) print('unique image_id 数量', len(set(images_id)))
# 创建类别标签字典
category_dic=dict([(i['id'],i['name']) for i in anno['categories']])
counts_label=dict([(i['name'],0) for i in anno['categories']]) for i in anno['annotations']:
counts_label[category_dic[i['category_id']]] += 1
label_list = counts_label.keys() # 各部分标签
print('标签列表:', label_list)
size = counts_label.values() # 各部分大小
color = ['#FFB6C1', '#D8BFD8', '#9400D3', '#483D8B', '#4169E1', '#00FFFF','#B1FFF0','#ADFF2F','#EEE8AA','#FFA500','#FF6347'] # 各部分颜色
# explode = [0.05, 0, 0] # 各部分突出值
patches, l_text, p_text = plt.pie(size, labels=label_list, colors=color, labeldistance=1.1, autopct="%1.1f%%", shadow=False, startangle=90, pctdistance=0.6, textprops={'fontproperties':myfont})
plt.axis("equal") # 设置横轴和纵轴大小相等，这样饼才是圆的
plt.legend(prop=myfont)
plt.show()# 分析训练集数据generate_anno_eda('wheat', 'train.json')# 读取训练集标注文件with open(TRAIN_CSV_PATH, 'r', encoding='utf-8') as f:
train_data = json.load(f)
train_fig = pd.DataFrame(train_data['images'])train_fig.head()
file_name height id width 0 b6ab77fd7.jpg 1024 1 1024 1 b53afdf5c.jpg 1024 2 1024 2 7b72ea0fb.jpg 1024 3 1024 3 91c9d9c38.jpg 1024 4 1024 4 41c0123cc.jpg 1024 5 1024
ps = np.zeros(len(train_fig))for i in range(len(train_fig)):
ps[i]=train_fig['width'][i] * train_fig['height'][i]/1e6plt.title('训练集图片大小分布', fontproperties=myfont)
sns.distplot(ps, bins=21,kde=False)train_anno = pd.DataFrame(train_data['annotations']) df_train = pd.merge(left=train_fig, right=train_anno, how='inner', left_on='id', right_on='image_id') df_train['bbox_xmin'] = df_train['bbox'].apply(lambda x: x[0]) df_train['bbox_ymin'] = df_train['bbox'].apply(lambda x: x[1]) df_train['bbox_w'] = df_train['bbox'].apply(lambda x: x[2]) df_train['bbox_h'] = df_train['bbox'].apply(lambda x: x[3]) df_train['bbox_xcenter'] = df_train['bbox'].apply(lambda x: (x[0]+0.5*x[2])) df_train['bbox_ycenter'] = df_train['bbox'].apply(lambda x: (x[1]+0.5*x[3]))
def get_all_bboxes(df, name):
image_bboxes = df[df.file_name == name]
bboxes = []
categories = [] for _,row in image_bboxes.iterrows():
bboxes.append((row.bbox_xmin, row.bbox_ymin, row.bbox_w, row.bbox_h, row.category_id)) return bboxesdef plot_image_examples(df, rows=3, cols=3, title='Image examples'):
fig, axs = plt.subplots(rows, cols, figsize=(15,15))
color = ['#FFB6C1', '#D8BFD8', '#9400D3', '#483D8B', '#4169E1', '#00FFFF','#B1FFF0','#ADFF2F','#EEE8AA','#FFA500','#FF6347'] # 各部分颜色
for row in range(rows): for col in range(cols):
idx = np.random.randint(len(df), size=1)[0]
name = df.iloc[idx]["file_name"]
img = Image.open(TRAIN_DIR + str(name))
axs[row, col].imshow(img)
bboxes = get_all_bboxes(df, name) for bbox in bboxes:
rect = patches.Rectangle((bbox[0],bbox[1]),bbox[2],bbox[3],linewidth=1,edgecolor=color[bbox[4]],facecolor='none')
axs[row, col].add_patch(rect)
axs[row, col].axis('off')
plt.suptitle(title,fontproperties=myfont)def plot_gray_examples(df, rows=3, cols=3, title='Image examples'):
fig, axs = plt.subplots(rows, cols, figsize=(15,15))
color = ['#FFB6C1', '#D8BFD8', '#9400D3', '#483D8B', '#4169E1', '#00FFFF','#B1FFF0','#ADFF2F','#EEE8AA','#FFA500','#FF6347'] # 各部分颜色
for row in range(rows): for col in range(cols):
idx = np.random.randint(len(df), size=1)[0]
name = df.iloc[idx]["file_name"]
img = Image.open(TRAIN_DIR + str(name)).convert('L')
axs[row, col].imshow(img)
bboxes = get_all_bboxes(df, name) for bbox in bboxes:
rect = patches.Rectangle((bbox[0],bbox[1]),bbox[2],bbox[3],linewidth=1,edgecolor=color[bbox[4]],facecolor='none')
axs[row, col].add_patch(rect)
axs[row, col].axis('off')
plt.suptitle(title,fontproperties=myfont)def get_image_brightness(image):
# convert to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# get average brightness
return np.array(gray).mean()def add_brightness(df):
brightness = [] for _, row in df.iterrows():
name = row["file_name"]
image = cv2.imread(TRAIN_DIR + name)
brightness.append(get_image_brightness(image))
brightness_df = pd.DataFrame(brightness)
brightness_df.columns = ['brightness']
df = pd.concat([df, brightness_df], ignore_index=True, axis=1)
df.columns = ['file_name', 'brightness']
return df
images_df = pd.DataFrame(df_train.file_name.unique())
images_df.columns = ['file_name']
brightness_df = add_brightness(images_df)brightness_df.head()
dark_names = brightness_df[brightness_df['brightness'] < 50].file_name plot_image_examples(df_train[df_train.file_name.isin(dark_names)], title='暗图片')
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/font_manager.py:1331: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
<Figure size 1080x1080 with 9 Axes>
bright_names = brightness_df[brightness_df['brightness'] > 130].file_name plot_image_examples(df_train[df_train.file_name.isin(bright_names)], title='亮图片')
sns.set(rc={'figure.figsize':(12,6)})
ps = np.zeros(len(brightness_df))for i in range(len(brightness_df)):
ps[i]=brightness_df['brightness'][i]
plt.title('图片亮度分布', fontproperties=myfont)
sns.distplot(ps, bins=21,kde=False)<matplotlib.axes._subplots.AxesSubplot at 0x7fd23c687450>
<Figure size 864x432 with 1 Axes>
ps = np.zeros(len(df_train))for i in range(len(df_train)):
ps[i]=df_train['area'][i]/1e6plt.title('训练集目标大小分布', fontproperties=myfont)
sns.distplot(ps, bins=21,kde=False)<matplotlib.axes._subplots.AxesSubplot at 0x7fd23c5e8fd0>
<Figure size 864x432 with 1 Axes>
# 各类别目标形状分布sns.set(rc={'figure.figsize':(12,6)})
sns.relplot(x="bbox_w", y="bbox_h", hue="category_id", col="category_id", data=df_train[0:1000])<seaborn.axisgrid.FacetGrid at 0x7fd23c50d4d0>
<Figure size 464.225x360 with 1 Axes>
# 各类别目标中心点形状分布sns.set(rc={'figure.figsize':(12,6)})
sns.relplot(x="bbox_xcenter", y="bbox_ycenter", hue="category_id", col="category_id", data=df_train[0:1000]);<Figure size 464.225x360 with 1 Axes>
sns.set(rc={'figure.figsize':(12,6)})
plt.title('训练集目标大小分布', fontproperties=myfont)
sns.violinplot(x=df_train['category_id'],y=df_train['area'])df_train.area.describe()
count 147793.000000 mean 6843.356576 std 5876.326590 min 2.000000 25% 3658.000000 50% 5488.000000 75% 8272.000000 max 529788.000000 Name: area, dtype: float64
sns.set(rc={'figure.figsize':(12,6)})
plt.title('训练集小目标分布', fontproperties=myfont)
plt.ylim(0, 4000)
sns.violinplot(x=df_train['category_id'],y=df_train['area'])sns.set(rc={'figure.figsize':(12,6)})
plt.title('训练集大目标分布', fontproperties=myfont)
plt.ylim(10000, max(df_train.area))
sns.violinplot(x=df_train['category_id'],y=df_train['area'])graph=sns.countplot(data=df_train, x='category_id')
graph.set_xticklabels(graph.get_xticklabels(), rotation=90)
plt.title('各类别目标数量分布', fontproperties=myfont)for p in graph.patches:
height = p.get_height()
graph.text(p.get_x()+p.get_width()/2., height + 0.1,height ,ha="center")df_train['bbox_count'] = df_train.apply(lambda row: 1 if any(row.bbox) else 0, axis=1)
train_images_count = df_train.groupby('file_name').sum().reset_index()train_images_count['bbox_count'].describe()
count 3373.000000 mean 43.816484 std 20.374820 min 1.000000 25% 28.000000 50% 43.000000 75% 59.000000 max 116.000000 Name: bbox_count, dtype: float64
# 目标数量超过50个的图片train_images_count['file_name'][train_images_count['bbox_count']>50]
0 00333207f.jpg
7 00ea5e5ee.jpg
17 015939012.jpg
23 02640d9da.jpg
24 026b6f389.jpg
...
3356 feac3a701.jpg
3360 feda9265c.jpg
3366 ffaa964a2.jpg
3368 ffb445410.jpg
3369 ffbf75e5b.jpg
Name: file_name, Length: 1272, dtype: object# 目标数量超过100个的图片train_images_count['file_name'][train_images_count['bbox_count']>50]
0 00333207f.jpg
7 00ea5e5ee.jpg
17 015939012.jpg
23 02640d9da.jpg
24 026b6f389.jpg
...
3356 feac3a701.jpg
3360 feda9265c.jpg
3366 ffaa964a2.jpg
3368 ffb445410.jpg
3369 ffbf75e5b.jpg
Name: file_name, Length: 1272, dtype: objectless_spikes_ids = train_images_count[train_images_count['bbox_count'] > 50].file_name plot_image_examples(df_train[df_train.file_name.isin(less_spikes_ids)], title='单图目标超过50个（示例）')
less_spikes_ids = train_images_count[train_images_count['bbox_count'] > 100].file_name plot_image_examples(df_train[df_train.file_name.isin(less_spikes_ids)], title='单图目标超过100个（示例）')
less_spikes_ids = train_images_count[train_images_count['bbox_count'] < 5].file_name plot_image_examples(df_train[df_train.file_name.isin(less_spikes_ids)], title='单图目标少于5个（示例）')
less_spikes_ids = train_images_count[train_images_count['area'] > max(train_images_count['area'])*0.9].file_name plot_image_examples(df_train[df_train.file_name.isin(less_spikes_ids)], title='目标总面积最大（示例）')
less_spikes_ids = train_images_count[train_images_count['area'] < min(train_images_count['area'])*1.1].file_name plot_image_examples(df_train[df_train.file_name.isin(less_spikes_ids)], title='目标总面积最小（示例）')
df_train['bbox_count'] = df_train.apply(lambda row: 1 if any(row.bbox) else 0, axis=1)
train_images_count = df_train.groupby('file_name').max().reset_index()
less_spikes_ids = train_images_count[train_images_count['area'] > max(train_images_count['area'])*0.8].file_name
plot_image_examples(df_train[df_train.file_name.isin(less_spikes_ids)], title='单目标面积最大（示例）')df_train['bbox_count'] = df_train.apply(lambda row: 1 if any(row.bbox) else 0, axis=1)
train_images_count = df_train.groupby('file_name').min().reset_index()
less_spikes_ids = train_images_count[train_images_count['area'] > min(train_images_count['area'])*1.2].file_name
plot_image_examples(df_train[df_train.file_name.isin(less_spikes_ids)], title='单目标面积最小（示例）')<Figure size 1080x1080 with 9 Axes>
# 计算IOUdef bb_intersection_over_union(boxA, boxB):
boxA = [int(x) for x in boxA]
boxB = [int(x) for x in boxB]
boxA = [boxA[0], boxA[1], boxA[0]+boxA[2], boxA[1]+boxA[3]]
boxB = [boxB[0], boxB[1], boxB[0]+boxB[2], boxB[1]+boxB[3]]
xA = max(boxA[0], boxB[0])
yA = max(boxA[1], boxB[1])
xB = min(boxA[2], boxB[2])
yB = min(boxA[3], boxB[3])
interArea = max(0, xB - xA + 1) * max(0, yB - yA + 1)
boxAArea = (boxA[2] - boxA[0] + 1) * (boxA[3] - boxA[1] + 1)
boxBArea = (boxB[2] - boxB[0] + 1) * (boxB[3] - boxB[1] + 1)
iou = interArea / float(boxAArea + boxBArea - interArea) return iou# tmp 是一个pandas Series，且索引从0开始def bbox_iou(tmp):
iou_agg = 0
iou_cnt = 0
for i in range(len(tmp)): for j in range(len(tmp)): if i != j:
iou_agg += bb_intersection_over_union(tmp[i], tmp[j]) if bb_intersection_over_union(tmp[i], tmp[j]) > 0:
iou_cnt += 1
iou_agg = iou_agg/2
iou_cnt = iou_cnt/2
return iou_agg, iou_cntfile_list = df_train['file_name'].unique()
train_iou_cal = pd.DataFrame(columns=('file_name', 'iou_agg', 'iou_cnt'))for i in range(len(file_list)):
tmp = df_train['bbox'][df_train.file_name==file_list[i]].reset_index(drop=True)
iou_agg, iou_cnt = bbox_iou(tmp)
train_iou_cal.loc[len(train_iou_cal)] = [file_list[i], iou_agg, iou_cnt]train_iou_cal.iou_agg.describe()
ps = np.zeros(len(train_iou_cal))for i in range(len(train_iou_cal)):
ps[i]=train_iou_cal['iou_agg'][i]
plt.title('训练集目标遮挡程度分布', fontproperties=myfont)
sns.distplot(ps, bins=21,kde=False)train_iou_cal.iou_cnt.describe()
ps = np.zeros(len(train_iou_cal))for i in range(len(train_iou_cal)):
ps[i]=train_iou_cal['iou_cnt'][i]
plt.title('训练集目标遮挡数量分布', fontproperties=myfont)
sns.distplot(ps, bins=21,kde=False)less_spikes_ids = train_iou_cal[train_iou_cal['iou_agg'] > max(train_iou_cal['iou_agg'])*0.9].file_name plot_image_examples(df_train[df_train.file_name.isin(less_spikes_ids)], title='目标遮挡程度最高（示例）')
less_spikes_ids = train_iou_cal[train_iou_cal['iou_agg'] <= min(train_iou_cal['iou_agg'])*1.1].file_name plot_image_examples(df_train[df_train.file_name.isin(less_spikes_ids)], title='目标遮挡程度最低（示例）')
less_spikes_ids = train_iou_cal[train_iou_cal['iou_cnt'] > max(train_iou_cal['iou_cnt'])*0.9].file_name plot_image_examples(df_train[df_train.file_name.isin(less_spikes_ids)], title='目标遮挡数量最高（示例）')
less_spikes_ids = train_iou_cal[train_iou_cal['iou_cnt'] <= min(train_iou_cal['iou_cnt'])*1.1].file_name plot_image_examples(df_train[df_train.file_name.isin(less_spikes_ids)], title='目标遮挡数量最低（示例）')
files = os.listdir(TRAIN_DIR)
R = 0.G = 0.B = 0.R_2 = 0.G_2 = 0.B_2 = 0.N = 0for f in files:
img = cv2.imread(TRAIN_DIR+f)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = np.array(img)
h, w, c = img.shape
N += h*w
R_t = img[:, :, 0]
R += np.sum(R_t)
R_2 += np.sum(np.power(R_t, 2.0))
G_t = img[:, :, 1]
G += np.sum(G_t)
G_2 += np.sum(np.power(G_t, 2.0))
B_t = img[:, :, 2]
B += np.sum(B_t)
B_2 += np.sum(np.power(B_t, 2.0))
R_mean = R/N
G_mean = G/N
B_mean = B/N
R_std = np.sqrt(R_2/N - R_mean*R_mean)
G_std = np.sqrt(G_2/N - G_mean*G_mean)
B_std = np.sqrt(B_2/N - B_mean*B_mean)print("R_mean: %f, G_mean: %f, B_mean: %f" % (R_mean, G_mean, B_mean))print("R_std: %f, G_std: %f, B_std: %f" % (R_std, G_std, B_std))R_mean: 80.398947, G_mean: 80.899598, B_mean: 54.711709 R_std: 62.528853, G_std: 60.699236, B_std: 49.439114
# 计算bbox的RGBdef bb_rgb_cal(img, boxA):
boxA = [int(x) for x in boxA]
boxA = [boxA[0], boxA[1], boxA[0]+boxA[2], boxA[1]+boxA[3]]
img = img.crop(boxA)
width = img.size[0]
height = img.size[1]
img = img.convert('RGB')
array = [] for x in range(width): for y in range(height):
r, g, b = img.getpixel((x,y))
rgb = (r, g, b)
array.append(rgb) return round(np.mean(array[0]),2), round(np.mean(array[1]),2), round(np.mean(array[2]),2)# 可能遇到jupyter输出内存报错from tqdm import tqdm
df_train['r_channel'] = 0df_train['g_channel'] = 0df_train['b_channel'] = 0for i in tqdm(df_train.index):
array = bb_rgb_cal(Image.open(TRAIN_DIR + str(df_train.file_name[i])), df_train.bbox[i])
df_train['r_channel'].at[i] = array[0]
df_train['g_channel'].at[i] = array[1]
df_train['b_channel'].at[i] = array[2]ps = np.zeros(len(df_train[:10000]))for i in range(len(df_train[:10000])):
ps[i]=df_train['r_channel'][df_train.category_id==1][i]
plt.title('类别1目标r_channel分布', fontproperties=myfont)
sns.distplot(ps, bins=21,kde=False)<matplotlib.axes._subplots.AxesSubplot at 0x7f4133d14910>
<Figure size 864x432 with 1 Axes>
ps = np.zeros(len(df_train[:10000]))for i in range(len(df_train[:10000])):
ps[i]=df_train['g_channel'][df_train.g_channel>0][df_train.category_id==1][i]
plt.title('类别1目标g_channel分布', fontproperties=myfont)
sns.distplot(ps, bins=21,kde=False)<matplotlib.axes._subplots.AxesSubplot at 0x7f4133331dd0>
<Figure size 864x432 with 1 Axes>
ps = np.zeros(len(df_train[:10000]))for i in range(len(df_train[:10000])):
ps[i]=df_train['b_channel'][df_train.b_channel>0][df_train.category_id==1][i]
plt.title('类别1目标b_channel分布', fontproperties=myfont)
sns.distplot(ps, bins=21,kde=False)<matplotlib.axes._subplots.AxesSubplot at 0x7f41333e5d50>
<Figure size 864x432 with 1 Axes>
less_spikes_ids = train_iou_cal[train_iou_cal['iou_cnt'] > max(train_iou_cal['iou_cnt'])*0.8].file_name plot_gray_examples(df_train[df_train.file_name.isin(less_spikes_ids)], title='目标遮挡数量最高（灰度）')
<Figure size 1080x1080 with 9 Axes>
less_spikes_ids = train_iou_cal[train_iou_cal['iou_cnt'] <= min(train_iou_cal['iou_cnt'])*1.1].file_name plot_gray_examples(df_train[df_train.file_name.isin(less_spikes_ids)], title='目标遮挡数量最低（灰度）')
<Figure size 1080x1080 with 9 Axes>
# 获取示例数据集!wget https://bj.bcebos.com/paddlex/datasets/insect_det.tar.gz# 解压数据集!tar -zxvf insect_det.tar.gz
import xml.etree.ElementTree as ETimport osimport json
coco = dict()
coco['images'] = []
coco['type'] = 'instances'coco['annotations'] = []
coco['categories'] = []
category_set = dict()
image_set = set()
category_item_id = -1image_id = 20180000000annotation_id = 0
def addCatItem(name):
global category_item_id
category_item = dict()
category_item['supercategory'] = 'none'
category_item_id += 1
category_item['id'] = category_item_id
category_item['name'] = name
coco['categories'].append(category_item)
category_set[name] = category_item_id return category_item_id
def addImgItem(file_name, size):
global image_id if file_name is None: raise Exception('Could not find filename tag in xml file.') if size['width'] is None: raise Exception('Could not find width tag in xml file.') if size['height'] is None: raise Exception('Could not find height tag in xml file.')
image_id += 1
image_item = dict()
image_item['id'] = image_id
image_item['file_name'] = file_name
image_item['width'] = size['width']
image_item['height'] = size['height']
coco['images'].append(image_item)
image_set.add(file_name) return image_id
def addAnnoItem(object_name, image_id, category_id, bbox):
global annotation_id
annotation_item = dict()
annotation_item['segmentation'] = []
seg = [] # bbox[] is x,y,w,h
# left_top
seg.append(bbox[0])
seg.append(bbox[1]) # left_bottom
seg.append(bbox[0])
seg.append(bbox[1] + bbox[3]) # right_bottom
seg.append(bbox[0] + bbox[2])
seg.append(bbox[1] + bbox[3]) # right_top
seg.append(bbox[0] + bbox[2])
seg.append(bbox[1])
annotation_item['segmentation'].append(seg)
annotation_item['area'] = bbox[2] * bbox[3]
annotation_item['iscrowd'] = 0
annotation_item['ignore'] = 0
annotation_item['image_id'] = image_id
annotation_item['bbox'] = bbox
annotation_item['category_id'] = category_id
annotation_id += 1
annotation_item['id'] = annotation_id
coco['annotations'].append(annotation_item)
def _read_image_ids(image_sets_file):
ids = [] with open(image_sets_file) as f: for line in f:
ids.append(line.rstrip()) return ids
"""通过txt文件生成"""#split ='train' 'va' 'trainval' 'test'def parseXmlFiles_by_txt(data_dir,json_save_path,split='train'):
print("hello")
labelfile=split+".txt"
image_sets_file = data_dir + "/ImageSets/Main/"+labelfile
ids=_read_image_ids(image_sets_file)
for _id in ids:
xml_file=data_dir + f"/Annotations/{_id}.xml"
bndbox = dict()
size = dict()
current_image_id = None
current_category_id = None
file_name = None
size['width'] = None
size['height'] = None
size['depth'] = None
tree = ET.parse(xml_file)
root = tree.getroot() if root.tag != 'annotation': raise Exception('pascal voc xml root element should be annotation, rather than {}'.format(root.tag))
# elem is <folder>, <filename>, <size>, <object>
for elem in root:
current_parent = elem.tag
current_sub = None
object_name = None
if elem.tag == 'folder': continue
if elem.tag == 'filename':
file_name = elem.text if file_name in category_set: raise Exception('file_name duplicated')
# add img item only after parse <size> tag
elif current_image_id is None and file_name is not None and size['width'] is not None: if file_name not in image_set:
current_image_id = addImgItem(file_name, size) print('add image with {} and {}'.format(file_name, size)) else: raise Exception('duplicated image: {}'.format(file_name)) # subelem is <width>, <height>, <depth>, <name>, <bndbox>
for subelem in elem:
bndbox['xmin'] = None
bndbox['xmax'] = None
bndbox['ymin'] = None
bndbox['ymax'] = None
current_sub = subelem.tag if current_parent == 'object' and subelem.tag == 'name':
object_name = subelem.text if object_name not in category_set:
current_category_id = addCatItem(object_name) else:
current_category_id = category_set[object_name]
elif current_parent == 'size': if size[subelem.tag] is not None: raise Exception('xml structure broken at size tag.')
size[subelem.tag] = int(subelem.text)
# option is <xmin>, <ymin>, <xmax>, <ymax>, when subelem is <bndbox>
for option in subelem: if current_sub == 'bndbox': if bndbox[option.tag] is not None: raise Exception('xml structure corrupted at bndbox tag.')
bndbox[option.tag] = int(option.text)
# only after parse the <object> tag
if bndbox['xmin'] is not None: if object_name is None: raise Exception('xml structure broken at bndbox tag') if current_image_id is None: raise Exception('xml structure broken at bndbox tag') if current_category_id is None: raise Exception('xml structure broken at bndbox tag')
bbox = [] # x
bbox.append(bndbox['xmin']) # y
bbox.append(bndbox['ymin']) # w
bbox.append(bndbox['xmax'] - bndbox['xmin']) # h
bbox.append(bndbox['ymax'] - bndbox['ymin']) print('add annotation with {},{},{},{}'.format(object_name, current_image_id, current_category_id,
bbox))
addAnnoItem(object_name, current_image_id, current_category_id, bbox)
json.dump(coco, open(json_save_path, 'w'))
"""直接从xml文件夹中生成"""def parseXmlFiles(xml_path,json_save_path):
for f in os.listdir(xml_path): if not f.endswith('.xml'): continue
bndbox = dict()
size = dict()
current_image_id = None
current_category_id = None
file_name = None
size['width'] = None
size['height'] = None
size['depth'] = None
xml_file = os.path.join(xml_path, f) print(xml_file)
tree = ET.parse(xml_file)
root = tree.getroot() if root.tag != 'annotation': raise Exception('pascal voc xml root element should be annotation, rather than {}'.format(root.tag))
# elem is <folder>, <filename>, <size>, <object>
for elem in root:
current_parent = elem.tag
current_sub = None
object_name = None
if elem.tag == 'folder': continue
if elem.tag == 'filename':
file_name = elem.text if file_name in category_set: raise Exception('file_name duplicated')
# add img item only after parse <size> tag
elif current_image_id is None and file_name is not None and size['width'] is not None: if file_name not in image_set:
current_image_id = addImgItem(file_name, size) print('add image with {} and {}'.format(file_name, size)) else: raise Exception('duplicated image: {}'.format(file_name)) # subelem is <width>, <height>, <depth>, <name>, <bndbox>
for subelem in elem:
bndbox['xmin'] = None
bndbox['xmax'] = None
bndbox['ymin'] = None
bndbox['ymax'] = None
current_sub = subelem.tag if current_parent == 'object' and subelem.tag == 'name':
object_name = subelem.text if object_name not in category_set:
current_category_id = addCatItem(object_name) else:
current_category_id = category_set[object_name]
elif current_parent == 'size': if size[subelem.tag] is not None: raise Exception('xml structure broken at size tag.')
size[subelem.tag] = int(subelem.text)
# option is <xmin>, <ymin>, <xmax>, <ymax>, when subelem is <bndbox>
for option in subelem: if current_sub == 'bndbox': if bndbox[option.tag] is not None: raise Exception('xml structure corrupted at bndbox tag.')
bndbox[option.tag] = int(option.text)
# only after parse the <object> tag
if bndbox['xmin'] is not None: if object_name is None: raise Exception('xml structure broken at bndbox tag') if current_image_id is None: raise Exception('xml structure broken at bndbox tag') if current_category_id is None: raise Exception('xml structure broken at bndbox tag')
bbox = [] # x
bbox.append(bndbox['xmin']) # y
bbox.append(bndbox['ymin']) # w
bbox.append(bndbox['xmax'] - bndbox['xmin']) # h
bbox.append(bndbox['ymax'] - bndbox['ymin']) print('add annotation with {},{},{},{}'.format(object_name, current_image_id, current_category_id,
bbox))
addAnnoItem(object_name, current_image_id, current_category_id, bbox)
json.dump(coco, open(json_save_path, 'w'))#通过文件夹生成ann_path="insect_det/Annotations"json_save_path="insect_det/train.json"parseXmlFiles(ann_path,json_save_path)
# Setup the paths to train and test imagesTRAIN_DIR = 'insect_det/JPEGImages/'TRAIN_CSV_PATH = 'insect_det/train.json'# Glob the directories and get the lists of train and test imagestrain_fns = glob.glob(TRAIN_DIR + '*')print('数据集图片数量: {}'.format(len(train_fns)))数据集图片数量: 217
# 效果测试generate_anno_eda('insect_det', 'train.json')标签类别: [{'supercategory': 'none', 'id': 0, 'name': 'leconte'}, {'supercategory': 'none', 'id': 1, 'name': 'boerner'}, {'supercategory': 'none', 'id': 2, 'name': 'armandi'}, {'supercategory': 'none', 'id': 3, 'name': 'linnaeus'}, {'supercategory': 'none', 'id': 4, 'name': 'coleoptera'}, {'supercategory': 'none', 'id': 5, 'name': 'acuminatus'}]
类别数量： 6
训练集图片数量： 217
训练集标签数量： 1407
长宽为(749,749)的图片数量为： 1
长宽为(565,565)的图片数量为： 1
长宽为(570,570)的图片数量为： 1
长宽为(557,557)的图片数量为： 1
长宽为(523,523)的图片数量为： 1
长宽为(635,635)的图片数量为： 2
长宽为(645,645)的图片数量为： 1
长宽为(718,718)的图片数量为： 1
长宽为(702,702)的图片数量为： 2
长宽为(641,641)的图片数量为： 5
长宽为(639,639)的图片数量为： 2
长宽为(513,513)的图片数量为： 1
长宽为(602,602)的图片数量为： 1
长宽为(601,601)的图片数量为： 1
长宽为(729,729)的图片数量为： 2
长宽为(536,536)的图片数量为： 1
长宽为(657,657)的图片数量为： 3
长宽为(587,587)的图片数量为： 1
长宽为(605,605)的图片数量为： 1
长宽为(613,613)的图片数量为： 1
长宽为(554,554)的图片数量为： 1
长宽为(733,733)的图片数量为： 1
长宽为(740,740)的图片数量为： 1
长宽为(631,631)的图片数量为： 3
长宽为(649,649)的图片数量为： 1
长宽为(623,623)的图片数量为： 6
长宽为(670,670)的图片数量为： 1
长宽为(558,558)的图片数量为： 1
长宽为(610,610)的图片数量为： 3
长宽为(671,671)的图片数量为： 2
长宽为(609,609)的图片数量为： 1
长宽为(661,661)的图片数量为： 2
长宽为(653,653)的图片数量为： 4
长宽为(627,627)的图片数量为： 5
长宽为(619,619)的图片数量为： 4
长宽为(499,499)的图片数量为： 1
长宽为(647,647)的图片数量为： 2
长宽为(583,583)的图片数量为： 1
长宽为(633,633)的图片数量为： 1
长宽为(697,697)的图片数量为： 1
长宽为(632,632)的图片数量为： 4
长宽为(637,637)的图片数量为： 2
长宽为(643,643)的图片数量为： 3
长宽为(636,636)的图片数量为： 3
长宽为(644,644)的图片数量为： 1
长宽为(638,638)的图片数量为： 8
长宽为(514,514)的图片数量为： 1
长宽为(655,655)的图片数量为： 3
长宽为(625,625)的图片数量为： 1
长宽为(621,621)的图片数量为： 1
长宽为(640,640)的图片数量为： 2
长宽为(624,624)的图片数量为： 1
长宽为(541,541)的图片数量为： 1
长宽为(549,549)的图片数量为： 1
长宽为(630,630)的图片数量为： 5
长宽为(650,650)的图片数量为： 3
长宽为(681,681)的图片数量为： 1
长宽为(617,617)的图片数量为： 4
长宽为(663,663)的图片数量为： 1
长宽为(599,599)的图片数量为： 1
长宽为(616,616)的图片数量为： 3
长宽为(495,495)的图片数量为： 1
长宽为(659,659)的图片数量为： 2
长宽为(629,629)的图片数量为： 3
长宽为(595,595)的图片数量为： 1
长宽为(651,651)的图片数量为： 2
长宽为(582,582)的图片数量为： 1
长宽为(693,693)的图片数量为： 1
长宽为(660,660)的图片数量为： 3
长宽为(628,628)的图片数量为： 2
长宽为(652,652)的图片数量为： 5
长宽为(620,620)的图片数量为： 8
长宽为(581,581)的图片数量为： 1
长宽为(580,580)的图片数量为： 1
长宽为(572,572)的图片数量为： 1
长宽为(590,590)的图片数量为： 1
长宽为(577,577)的图片数量为： 1
长宽为(576,576)的图片数量为： 1
长宽为(704,704)的图片数量为： 1
长宽为(560,560)的图片数量为： 1
长宽为(614,614)的图片数量为： 3
长宽为(600,600)的图片数量为： 2
长宽为(676,676)的图片数量为： 2
长宽为(612,612)的图片数量为： 4
长宽为(552,552)的图片数量为： 1
长宽为(622,622)的图片数量为： 3
长宽为(674,674)的图片数量为： 1
长宽为(656,656)的图片数量为： 3
长宽为(608,608)的图片数量为： 1
长宽为(691,691)的图片数量为： 1
长宽为(592,592)的图片数量为： 1
长宽为(634,634)的图片数量为： 4
长宽为(518,518)的图片数量为： 1
长宽为(589,589)的图片数量为： 1
长宽为(596,596)的图片数量为： 1
长宽为(588,588)的图片数量为： 1
长宽为(692,692)的图片数量为： 1
长宽为(564,564)的图片数量为： 3
长宽为(684,684)的图片数量为： 1
长宽为(569,569)的图片数量为： 1
长宽为(765,765)的图片数量为： 1
长宽为(707,707)的图片数量为： 1
长宽为(498,498)的图片数量为： 1
长宽为(754,754)的图片数量为： 1
长宽为(626,626)的图片数量为： 1
长宽为(512,512)的图片数量为： 1
长宽为(615,615)的图片数量为： 2
长宽为(665,665)的图片数量为： 1
长宽为(611,611)的图片数量为： 5
长宽为(603,603)的图片数量为： 1
长宽为(618,618)的图片数量为： 2
长宽为(662,662)的图片数量为： 3
长宽为(607,607)的图片数量为： 2
训练集图片数量: 217
unique id 数量： 1407
unique image_id 数量 217
标签列表: dict_keys(['leconte', 'boerner', 'armandi', 'linnaeus', 'coleoptera', 'acuminatus'])<Figure size 864x432 with 1 Axes>
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