方法1： 用plt.imshow()显示
import json
import os, cv2
import matplotlib.pyplot as plt
train_json = './WIDER_train/aaa/trainset.json'
train_path = ''
def visualization_bbox1(num_image, json_path,img_path):# 需要画的第num副图片， 对应的json路径和图片路径
with open(json_path) as annos:
annotation_json = json.load(annos)
print('the annotation_json num_key is:',len(annotation_json)) # 统计json文件的关键字长度
print('the annotation_json key is:', annotation_json.keys()) # 读出json文件的关键字
print('the annotation_json num_images is:', len(annotation_json['images'])) # json文件中包含的图片数量
image_name = annotation_json['images'][num_image - 1]['file_name'] # 读取图片名
id = annotation_json['images'][num_image - 1]['id'] # 读取图片id
image_path = os.path.join(img_path, str(image_name).zfill(5)) # 拼接图像路径
print(image_path)
image = cv2.imread(image_path, 1) # 保持原始格式的方式读取图像
num_bbox = 0 # 统计一幅图片中bbox的数量
for i in range(len(annotation_json['annotations'][::])):
if annotation_json['annotations'][i-1]['image_id'] == id:
num_bbox = num_bbox + 1
x, y, w, h = annotation_json['annotations'][i-1]['bbox'] # 读取边框
image = cv2.rectangle(image, (int(x), int(y)), (int(x + w), int(y + h)), (0, 255, 255), 2)
print('The unm_bbox of the display image is:', num_bbox)
# 显示方式1：用plt.imshow()显示
plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) #绘制图像，将CV的BGR换成RGB
plt.show() #显示图像
if __name__ == "__main__":
visualization_bbox1(0, train_json, train_path)
方法2：用cv2.imshow()显示
import json
import os, cv2
import matplotlib.pyplot as plt
train_json = './WIDER_train/aaa/trainset.json'
train_path = ''
def visualization_bbox1(num_image, json_path,img_path):# 需要画的第num副图片， 对应的json路径和图片路径
with open(json_path) as annos:
annotation_json = json.load(annos)
print('the annotation_json num_key is:',len(annotation_json)) # 统计json文件的关键字长度
print('the annotation_json key is:', annotation_json.keys()) # 读出json文件的关键字
print('the annotation_json num_images is:', len(annotation_json['images'])) # json文件中包含的图片数量
image_name = annotation_json['images'][num_image - 1]['file_name'] # 读取图片名
id = annotation_json['images'][num_image - 1]['id'] # 读取图片id
image_path = os.path.join(img_path, str(image_name).zfill(5)) # 拼接图像路径
print(image_path)
image = cv2.imread(image_path, 1) # 保持原始格式的方式读取图像
num_bbox = 0 # 统计一幅图片中bbox的数量
for i in range(len(annotation_json['annotations'][::])):
if annotation_json['annotations'][i-1]['image_id'] == id:
num_bbox = num_bbox + 1
x, y, w, h = annotation_json['annotations'][i-1]['bbox'] # 读取边框
image = cv2.rectangle(image, (int(x), int(y)), (int(x + w), int(y + h)), (0, 255, 255), 2)
print('The unm_bbox of the display image is:', num_bbox)
# 显示方式2：用cv2.imshow()显示
cv2.namedWindow(image_name, 0) # 创建窗口
cv2.resizeWindow(image_name, 1000, 1000) # 创建500*500的窗口
cv2.imshow(image_name, image)
cv2.waitKey(0)
if __name__ == "__main__":
visualization_bbox1(0, train_json, train_path)
json：
{
"images": [
{
"file_name": "./44--Aerobics/44_Aerobics_Aerobics_44_53.jpg",
"height": 683,
"width": 1024,
"id": 0
}
],
"annotations": [
{
"segmentation": [
[
421.1,
133.2,
445.5,
125.5,
432.6,
145.4,
432.3,
159.9,
450.6,
153.4
]
],
"area": 4356.76,
"iscrowd": 0,
"image_id": 0,
"bbox": [
411.5,
100.1,
59.6,
73.1
],
"category_id": 1,
"id": 0,
"ignore": 0
},
{
"segmentation": [
[
537.6,
120.5,
546.8,
119.1,
541.2,
125.2,
539.9,
130.7,
547.1,
129.5
]
],
"area": 585.66,
"iscrowd": 0,
"image_id": 0,
"bbox": [
534.4,
110.9,
22.7,
25.8
],
"category_id": 1,
"id": 1,
"ignore": 0
},
{
"segmentation": [
[
104.5,
152.8,
116.5,
150.9,
109.9,
158.4,
107.3,
164.1,
117.7,
162.5
]
],
"area": 953.2800000000001,
"iscrowd": 0,
"image_id": 0,
"bbox": [
99.3,
139.2,
28.8,
33.1
],
"category_id": 1,
"id": 2,
"ignore": 0
},
{
"segmentation": [
[
823.0,
102.6,
832.1,
102.6,
826.2,
107.1,
823.6,
112.3,
830.9,
112.2
]
],
"area": 497.96000000000004,
"iscrowd": 0,
"image_id": 0,
"bbox": [
819.0,
95.6,
21.1,
23.6
],
"category_id": 1,
"id": 3,
"ignore": 0
},
{
"segmentation": [
[
955.1,
94.6,
971.3,
96.7,
961.0,
106.2,
954.8,
111.8,
966.8,
113.3
]
],
"area": 1760.5900000000001,
"iscrowd": 0,
"image_id": 0,
"bbox": [
945.4,
76.2,
37.7,
46.7
],
"category_id": 1,
"id": 4,
"ignore": 0
},
{
"segmentation": [
[
597.0,
121.9,
603.2,
121.7,
600.3,
125.3,
598.1,
127.7,
602.5,
127.5
]
],
"area": 195.35999999999999,
"iscrowd": 0,
"image_id": 0,
"bbox": [
593.3,
115.6,
13.2,
14.8
],
"category_id": 1,
"id": 5,
"ignore": 0
},
{
"segmentation": [
[
756.2,
115.3,
762.5,
115.1,
759.5,
118.2,
757.2,
121.5,
762.1,
121.3
]
],
"area": 221.1,
"iscrowd": 0,
"image_id": 0,
"bbox": [
752.5,
108.7,
13.4,
16.5
],
"category_id": 1,
"id": 6,
"ignore": 0
},
{
"segmentation": [
[
358.1,
117.4,
372.8,
116.2,
370.0,
123.7,
361.8,
134.2,
373.6,
133.4
]
],
"area": 1874.5200000000002,
"iscrowd": 0,
"image_id": 0,
"bbox": [
340.3,
96.7,
38.1,
49.2
],
"category_id": 1,
"id": 7,
"ignore": 0
},
{
"segmentation": [
[
379.5,
129.0,
389.0,
129.0,
384.7,
133.6,
380.9,
138.7,
387.5,
138.7
]
],
"area": 552.16,
"iscrowd": 0,
"image_id": 0,
"bbox": [
373.1,
116.6,
20.3,
27.2
],
"category_id": 1,
"id": 8,
"ignore": 0
},
{
"segmentation": [
[
709.0,
126.4,
713.6,
126.1,
711.4,
128.5,
709.7,
130.8,
713.6,
130.6
]
],
"area": 124.23,
"iscrowd": 0,
"image_id": 0,
"bbox": [
706.5,
121.5,
10.1,
12.3
],
"category_id": 1,
"id": 9,
"ignore": 0
},
{
"segmentation": [
[
572.8,
126.0,
581.4,
126.0,
576.7,
131.5,
574.6,
134.5,
580.3,
134.4
]
],
"area": 371.05,
"iscrowd": 0,
"image_id": 0,
"bbox": [
569.0,
116.7,
18.1,
20.5
],
"category_id": 1,
"id": 10,
"ignore": 0
}
],
"categories": [
{
"name": "background",
"id": 0
},
{
"name": "face",
"id": 1
}
]
}
