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*备忘录：
我的帖子解释了cocodetection()使用带有captions_train2014.json、instances_train2014.json和person_keypoints_train2014.json的train2014、带有captions_val2014.json、instances_val2014.json和person_keypoints_val2014.json的val2014以及带有image_info_test2014.json的test2017， image_info_test2015.json 和 image_info_test-dev2015.json。我的帖子解释了cocodetection()使用train2017与captions_train2017.json，instances_train2017.json和person_keypoints_train2017.json，val2017与captions_val2017.json，instances_val2017.json和person_keypoints_val2017.json和test2017与image_info_test2017.json和image_info_test-dev2017.json.我的帖子解释了cocodetection()使用train2017与stuff_train2017.json，val2017与stuff_val2017.json，stuff_train2017_pixelmaps与stuff_train2017.json，stuff_val2017_pixelmaps与stuff_val2017.json，panoptic_train2017与panoptic_train2017.json，panoptic_val2017与panoptic_val2017.json 和 unlabeled2017 以及 image_info_unlabeled2017.json。我的帖子解释了 ms coco。
cococaptions() 可以使用 ms coco 数据集，如下所示。 *这适用于带有captions_train2014.json、instances_train2014.json和person_keypoints_train2014.json的train2014，带有captions_val2014.json、instances_val2014.json和person_keypoints_val2014.json的val2014以及带有image_info_test2014.json、image_info_test2015.json和的test2017 image_info_test-dev2015.json:
*备忘录：
第一个参数是root（必需类型：str或pathlib.path）：*备注：这是图像的路径。绝对或相对路径都是可能的。第二个参数是 annfile（必需类型：str 或 pathlib.path）：*备注：这是注释的路径。 绝对或相对路径都是可能的。第三个参数是transform(optional-default:none-type:callable)。第四个参数是 target_transform(optional-default:none-type:callable)。第五个参数是transforms(optional-default:none-type:callable)。
from torchvision.datasets import CocoCaptionscap_train2014_data = CocoCaptions( root="data/coco/imgs/train2014", annFile="data/coco/anns/trainval2014/captions_train2014.json")cap_train2014_data = CocoCaptions( root="data/coco/imgs/train2014", annFile="data/coco/anns/trainval2014/captions_train2014.json", transform=None, target_transform=None, transforms=None)ins_train2014_data = CocoCaptions( root="data/coco/imgs/train2014", annFile="data/coco/anns/trainval2014/instances_train2014.json")pk_train2014_data = CocoCaptions( root="data/coco/imgs/train2014", annFile="data/coco/anns/trainval2014/person_keypoints_train2014.json")len(cap_train2014_data), len(ins_train2014_data), len(pk_train2014_data)# (82783, 82783, 82783)cap_val2014_data = CocoCaptions( root="data/coco/imgs/val2014", annFile="data/coco/anns/trainval2014/captions_val2014.json")ins_val2014_data = CocoCaptions( root="data/coco/imgs/val2014", annFile="data/coco/anns/trainval2014/instances_val2014.json")pk_val2014_data = CocoCaptions( root="data/coco/imgs/val2014", annFile="data/coco/anns/trainval2014/person_keypoints_val2014.json")len(cap_val2014_data), len(ins_val2014_data), len(pk_val2014_data)# (40504, 40504, 40504)test2014_data = CocoCaptions( root="data/coco/imgs/test2014", annFile="data/coco/anns/test2014/image_info_test2014.json")test2015_data = CocoCaptions( root="data/coco/imgs/test2015", annFile="data/coco/anns/test2015/image_info_test2015.json")testdev2015_data = CocoCaptions( root="data/coco/imgs/test2015", annFile="data/coco/anns/test2015/image_info_test-dev2015.json")len(test2014_data), len(test2015_data), len(testdev2015_data)# (40775, 81434, 20288)cap_train2014_data# Dataset CocoCaptions# Number of datapoints: 82783# Root location: data/coco/imgs/train2014cap_train2014_data.root# 'data/coco/imgs/train2014'print(cap_train2014_data.transform)# Noneprint(cap_train2014_data.target_transform)# Noneprint(cap_train2014_data.transforms)# Nonecap_train2014_data.coco# cap_train2014_data[26]# (,# ['three zeebras standing in a grassy field walking',# 'Three zebras are standing in an open field.',# 'Three zebra are walking through the grass of a field.',# 'Three zebras standing on a grassy dirt field.',# 'Three zebras grazing in green grass field area.'])cap_train2014_data[179]# (,# ['a young guy walking in a forrest holding an object in his hand',# 'A partially black and white photo of a man throwing ... the woods.',# 'A disc golfer releases a throw from a dirt tee ... wooded course.',# 'The person is in the clearing of a wooded area. ',# 'a person throwing a frisbee at many trees '])cap_train2014_data[194]# (,# ['A person on a court with a tennis racket.',# 'A man that is holding a racquet standing in the grass.',# 'A tennis player hits the ball during a match.',# 'The tennis player is poised to serve a ball.',# 'Man in white playing tennis on a court.'])ins_train2014_data[26] # Errorins_train2014_data[179] # Errorins_train2014_data[194] # Errorpk_train2014_data[26]# (, [])pk_train2014_data[179] # Errorpk_train2014_data[194] # Errorcap_val2014_data[26]# (,# ['a close up of a child next to a cake with balloons',# 'A baby sitting in front of a cake wearing a tie.',# 'The young boy is dressed in a tie that matches his cake. ',# 'A child eating a birthday cake near some balloons.',# 'A baby eating a cake with a tie around ... the background.'])cap_val2014_data[179]# (,# ['Many small children are posing together in the ... white photo. ',# 'A vintage school picture of grade school aged children.',# 'A black and white photo of a group of kids.',# 'A group of children standing next to each other.',# 'A group of children standing and sitting beside each other. '])cap_val2014_data[194]# (,# ['A man hitting a tennis ball with a racquet.',# 'champion tennis player swats at the ball hoping to win',# 'A man is hitting his tennis ball with a recket on the court.',# 'a tennis player on a court with a racket',# 'A professional tennis player hits a ball as fans watch.'])ins_val2014_data[26] # Errorins_val2014_data[179] # Errorins_val2014_data[194] # Errorpk_val2014_data[26] # Errorpk_val2014_data[179] # Errorpk_val2014_data[194] # Errortest2014_data[26]# (, [])test2014_data[179]# (, [])test2014_data[194]# (, [])test2015_data[26]# (, [])test2015_data[179]# (, [])test2015_data[194]# (, [])testdev2015_data[26]# (, [])testdev2015_data[179]# (, [])testdev2015_data[194]# (, [])import matplotlib.pyplot as pltfrom matplotlib.patches import Polygon, Rectangleimport numpy as npfrom pycocotools import maskdef show_images(data, ims, main_title=None): file = data.root.split('/')[-1] fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(14, 8)) fig.suptitle(t=main_title, y=0.9, fontsize=14) x_crd = 0.02 for i, axis in zip(ims, axes.ravel()): if data[i][1]: im, anns = data[i] axis.imshow(X=im) y_crd = 0.0 for j, ann in enumerate(iterable=anns): text_list = ann.split() if len(text_list) > 9: text = " ".join(text_list[0:10]) + " ..." else: text = " ".join(text_list) plt.figtext(x=x_crd, y=y_crd, fontsize=10, s=f'{j} : {text}') y_crd -= 0.06 x_crd += 0.325 if i == 2 and file == "val2017": x_crd += 0.06 elif not data[i][1]: im, _ = data[i] axis.imshow(X=im) fig.tight_layout() plt.show()ims = (26, 179, 194)show_images(data=cap_train2014_data, ims=ims, main_title="cap_train2014_data")show_images(data=cap_val2014_data, ims=ims, main_title="cap_val2014_data")show_images(data=test2014_data, ims=ims, main_title="test2014_data")show_images(data=test2015_data, ims=ims, main_title="test2015_data")show_images(data=testdev2015_data, ims=ims, main_title="testdev2015_data")
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