from torchvision.datasets import CocoDetection  cap_train2014_data = CocoDetection(     root="data/coco/imgs/train2014",     annFile="data/coco/anns/trainval2014/captions_train2014.json" )  cap_train2014_data = CocoDetection(     root="data/coco/imgs/train2014",     annFile="data/coco/anns/trainval2014/captions_train2014.json",     transform=None,     target_transform=None,     transforms=None )  ins_train2014_data = CocoDetection(     root="data/coco/imgs/train2014",     annFile="data/coco/anns/trainval2014/instances_train2014.json" )  pk_train2014_data = CocoDetection(     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 = CocoDetection(     root="data/coco/imgs/val2014",     annFile="data/coco/anns/trainval2014/captions_val2014.json" )  ins_val2014_data = CocoDetection(     root="data/coco/imgs/val2014",     annFile="data/coco/anns/trainval2014/instances_val2014.json" )  pk_val2014_data = CocoDetection(     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 = CocoDetection(     root="data/coco/imgs/test2014",     annFile="data/coco/anns/test2014/image_info_test2014.json" )  test2015_data = CocoDetection(     root="data/coco/imgs/test2015",     annFile="data/coco/anns/test2015/image_info_test2015.json" )  testdev2015_data = CocoDetection(     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 CocoDetection #     Number of datapoints: 82783 #     Root location: data/coco/imgs/train2014  cap_train2014_data.root # 'data/coco/imgs/train2014'  print(cap_train2014_data.transform) # None  print(cap_train2014_data.target_transform) # None  print(cap_train2014_data.transforms) # None  cap_train2014_data.coco # <pycocotools.coco.COCO at 0x7c8a5f09d4f0>  cap_train2014_data[26] # (<PIL.Image.Image image mode=RGB size=427x640>, #  [{'image_id': 154, 'id': 202466, #    'caption': 'three zeebras standing in a grassy field walking'}, #   {'image_id': 154, 'id': 211904, #    'caption': 'Three zebras are standing in an open field.'}, #   {'image_id': 154, 'id': 215654, #    'caption': 'Three zebra are walking through the grass of a field.'}, #   {'image_id': 154, 'id': 216620, #    'caption': 'Three zebras standing on a grassy dirt field.'}, #   {'image_id': 154, 'id': 231686, #    'caption': 'Three zebras grazing in green grass field area.'}])  cap_train2014_data[179] # (<PIL.Image.Image image mode=RGB size=480x640>, #  [{'image_id': 1330, 'id': 721877, #    'caption': 'a young guy walking in a forrest holding ... his hand'}, #   {'image_id': 1330, 'id': 727442, #    'caption': 'A partially black and white photo of a ... the woods.'}, #   {'image_id': 1330, 'id': 730133, #    'caption': 'A disc golfer releases a throw ... wooded course.'}, #   {'image_id': 1330, 'id': 731450, #    'caption': 'The person is in the clearing of a wooded area. '}, #   {'image_id': 1330, 'id': 732335, #    'caption': 'a person throwing a frisbee at many trees '}])  cap_train2014_data[194] # (<PIL.Image.Image image mode=RGB size=428x640>, #  [{'image_id': 1407, 'id': 451510, #    'caption': 'A person on a court with a tennis racket.'}, #   {'image_id': 1407, 'id': 457735, #    'caption': 'A man that is holding a racquet ... the grass.'}, #   {'image_id': 1407, 'id': 460600, #    'caption': 'A tennis player hits the ball during a match.'}, #   {'image_id': 1407, 'id': 460612, #    'caption': 'The tennis player is poised to serve a ball.'}, #   {'image_id': 1407, 'id': 821947, #    'caption': 'Man in white playing tennis on a court.'}])  ins_train2014_data[26] # (<PIL.Image.Image image mode=RGB size=427x640>, #  [{'segmentation': [[229.5, 618.18, 235.64, ..., 219.85, 618.18]], #    'area': 53702.50415, 'iscrowd': 0, 'image_id': 154, #    'bbox': [11.98, 315.59, 349.08, 324.41], 'category_id': 24, #    'id': 590410}, #   {'segmentation': ..., 'category_id': 24, 'id': 590623}, #   {'segmentation': ..., 'category_id': 24, 'id': 593205}])  ins_train2014_data[179] # (<PIL.Image.Image image mode=RGB size=480x640>, #  [{'segmentation': [[160.87, 574.0, 174.15, ..., 162.77, 577.6]], #    'area': 21922.32225, 'iscrowd': 0, 'image_id': 1330, #    'bbox': [38.47, 228.02, 249.55, 349.58], 'category_id': 1, #    'id': 497247}, #   {'segmentation': ..., 'category_id': 34, 'id': 604179}])  ins_train2014_data[194] # (<PIL.Image.Image image mode=RGB size=428x640>, #  [{'segmentation': [[203.26, 465.95, 215.13, ..., 207.22, 466.94]],  #    'area': 20449.62315, 'iscrowd': 0, 'image_id': 1407, #    'bbox': [138.97, 198.88, 175.08, 355.11], 'category_id': 1, #    'id': 434962}, #   {'segmentation': ..., 'category_id': 43, 'id': 658155}, #   ... #   {'segmentation': ..., 'category_id': 1, 'id': 2000535}])  pk_train2014_data[26] # (<PIL.Image.Image image mode=RGB size=427x640>, [])  pk_train2014_data[179] # (<PIL.Image.Image image mode=RGB size=480x640>, #  [{'segmentation': [[160.87, 574, 174.15, ..., 162.77, 577.6]], #    'num_keypoints': 14, 'area': 21922.32225, 'iscrowd': 0, #    'keypoints': [0, 0, 0, 0, ..., 510, 2], 'image_id': 1330, #    'bbox': [38.47, 228.02, 249.55, 349.58], 'category_id': 1, #    'id': 497247}])  pk_train2014_data[194] # (<PIL.Image.Image image mode=RGB size=428x640>, #  [{'segmentation': [[203.26, 465.95, 215.13, ..., 207.22, 466.94]], #    'num_keypoints': 16, 'area': 20449.62315, 'iscrowd': 0, #    'keypoints': [243, 289, 2, 247, ..., 516, 2], 'image_id': 1407, #    'bbox': [138.97, 198.88, 175.08, 355.11], 'category_id': 1, #    'id': 434962}, #   {'segmentation': ..., 'category_id': 1, 'id': 1246131}, #   ... #   {'segmentation': ..., 'category_id': 1, 'id': 2000535}])  cap_val2014_data[26] # (<PIL.Image.Image image mode=RGB size=640x360>, #  [{'image_id': 428, 'id': 281051, #    'caption': 'a close up of a child next to a cake with balloons'}, #   {'image_id': 428, 'id': 283808, #    'caption': 'A baby sitting in front of a cake wearing a tie.'}, #   {'image_id': 428, 'id': 284135, #    'caption': 'The young boy is dressed in a tie that ... his cake. '}, #  {'image_id': 428, 'id': 284627, #   'caption': 'A child eating a birthday cake near some balloons.'}, #  {'image_id': 428, 'id': 401924, #   'caption': 'A baby eating a cake with a tie ... the background.'}])  cap_val2014_data[179] # (<PIL.Image.Image image mode=RGB size=500x302>, #  [{'image_id': 2299, 'id': 692974, #    'caption': 'Many small children are posing ... white photo. '}, #   {'image_id': 2299, 'id': 693640, #    'caption': 'A vintage school picture of grade school aged children.'}, #   {'image_id': 2299, 'id': 694699, #    'caption': 'A black and white photo of a group of kids.'}, #   {'image_id': 2299, 'id': 697432, #    'caption': 'A group of children standing next to each other.'}, #   {'image_id': 2299, 'id': 698791, #    'caption': 'A group of children standing and ... each other. '}])  cap_val2014_data[194] # (<PIL.Image.Image image mode=RGB size=640x427>, #  [{'image_id': 2562, 'id': 267259, #    'caption': 'A man hitting a tennis ball with a racquet.'}, #   {'image_id': 2562, 'id': 277075, #    'caption': 'champion tennis player swats at the ball ... to win'}, #   {'image_id': 2562, 'id': 279091, #    'caption': 'A man is hitting his tennis ball with ... the court.'}, #   {'image_id': 2562, 'id': 406135, #    'caption': 'a tennis player on a court with a racket'}, #   {'image_id': 2562, 'id': 823086, #    'caption': 'A professional tennis player hits a ... fans watch.'}])  ins_val2014_data[26] # (<PIL.Image.Image image mode=RGB size=640x360>, #  [{'segmentation': [[378.61, 210.2, 409.35, ..., 374.56, 217.48]],  #    'area': 3573.3858000000005, 'iscrowd': 0, 'image_id': 428, #    'bbox': [374.56, 200.49, 94.65, 154.52], 'category_id': 32, #    'id': 293908}, #   {'segmentation': ..., 'category_id': 1, 'id': 487626}, #   {'segmentation': ..., 'category_id': 61, 'id': 1085469}])  ins_val2014_data[179] # (<PIL.Image.Image image mode=RGB size=500x302>, #  [{'segmentation': [[107.49, 226.51, 108.17, ..., 105.8, 226.43]], #    'area': 66.15510000000003, 'iscrowd': 0, 'image_id': 2299, #    'bbox': [101.74, 226.43, 7.53, 15.83], 'category_id': 32, #    'id': 295960}, #   {'segmentation': ..., 'category_id': 32, 'id': 298359}, #   ... #   {'segmentation': {'counts': [152, 13, 263, 40, 2, ..., 132, 75], #    'size': [302, 500]}, 'area': 87090, 'iscrowd': 1, 'image_id': 2299, #    'bbox': [0, 18, 499, 263], 'category_id': 1, 'id': 900100002299}])  ins_val2014_data[194] # (<PIL.Image.Image image mode=RGB size=640x427>, #  [{'segmentation': [[389.92, 6.17, 391.48, ..., 393.57, 0.57]], #    'area': 482.5815999999996, 'iscrowd': 0, 'image_id': 2562, #    'bbox': [389.92, 0.57, 28.15, 21.38], 'category_id': 37, #    'id': 302161}, #   {'segmentation': ..., 'category_id': 43, 'id': 659770}, #   ... #   {'segmentation': {'counts': [132, 8, 370, 37, 3, ..., 82, 268], #    'size': [427, 640]}, 'area': 19849, 'iscrowd': 1, 'image_id': 2562,  #    'bbox': [0, 49, 639, 193], 'category_id': 1, 'id': 900100002562}])  pk_val2014_data[26] # (<PIL.Image.Image image mode=RGB size=640x360>, #  [{'segmentation': [[239.18, 244.08, 229.39, ..., 256.33, 251.43]], #    'num_keypoints': 10, 'area': 55007.0814, 'iscrowd': 0, #    'keypoints': [383, 132, 2, 418, ..., 0, 0], 'image_id': 428, #    'bbox': [226.94, 32.65, 355.92, 323.27], 'category_id': 1, #    'id': 487626}])  pk_val2014_data[179] # (<PIL.Image.Image image mode=RGB size=500x302>, #  [{'segmentation': [[75, 272.02, 76.92, ..., 74.67, 272.66]], #    'num_keypoints': 17, 'area': 4357.5248, 'iscrowd': 0, #    'keypoints': [108, 213, 2, 113, ..., 289, 2], 'image_id': 2299, #    'bbox': [70.18, 189.51, 64.2, 112.04], 'category_id': 1, #    'id': 1219726}, #   {'segmentation': ..., 'category_id': 1, 'id': 1226789}, #   ... #   {'segmentation': {'counts': [152, 13, 263, 40, 2, ..., 132, 75], #    'size': [302, 500]}, 'num_keypoints': 0, 'area': 87090, #    'iscrowd': 1, 'keypoints': [0, 0, 0, 0, ..., 0, 0], 'image_id': 2299, #    'bbox': [0, 18, 499, 263], 'category_id': 1, 'id': 900100002299}])  pk_val2014_data[194] # (<PIL.Image.Image image mode=RGB size=640x427>, #  [{'segmentation': [[19.26, 270.62, 4.3, ..., 25.98, 273.61]], #    'num_keypoints': 13, 'area': 6008.95835, 'iscrowd': 0, #    'keypoints': [60, 160, 2, 64, ..., 257, 1], 'image_id': 2562, #    'bbox': [4.3, 144.26, 100.19, 129.35], 'category_id': 1, #    'id': 1287168}, #   {'segmentation': ..., 'category_id': 1, 'id': 1294190}, #   ... #   {'segmentation': {'counts': [132, 8, 370, 37, 3, ..., 82, 268], #    'size': [427, 640]}, 'num_keypoints': 0, 'area': 19849, 'iscrowd': 1, #    'keypoints': [0, 0, 0, 0, ..., 0, 0], 'image_id': 2562, #    'bbox': [0, 49, 639, 193], 'category_id': 1, 'id': 900100002562}])  test2014_data[26] # (<PIL.Image.Image image mode=RGB size=640x640>, [])  test2014_data[179] # (<PIL.Image.Image image mode=RGB size=640x480>, [])  test2014_data[194] # (<PIL.Image.Image image mode=RGB size=640x360>, [])  test2015_data[26] # (<PIL.Image.Image image mode=RGB size=640x480>, [])  test2015_data[179] # (<PIL.Image.Image image mode=RGB size=640x426>, [])  test2015_data[194] # (<PIL.Image.Image image mode=RGB size=640x480>, [])  testdev2015_data[26] # (<PIL.Image.Image image mode=RGB size=640x360>, [])  testdev2015_data[179] # (<PIL.Image.Image image mode=RGB size=640x480>, [])  testdev2015_data[194] # (<PIL.Image.Image image mode=RGB size=640x480>, [])  import matplotlib.pyplot as plt from matplotlib.patches import Polygon, Rectangle import numpy as np from pycocotools import mask  # `show_images1()` doesn't work very well for the images with # segmentations and keypoints so for them, use `show_images2()` which # more uses the original coco functions.  def show_images1(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] and "caption" in data[i][1][0]:             im, anns = data[i]             axis.imshow(X=im)             axis.set_title(label=anns[0]["image_id"])             y_crd = 0.0             for ann in anns:                 text_list = ann["caption"].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'{ann["id"]}: {text}')                 y_crd -= 0.06             x_crd += 0.325             if i == 2 and file == "val2017":                 x_crd += 0.06         if data[i][1] and "segmentation" in data[i][1][0]:             im, anns = data[i]             axis.imshow(X=im)             axis.set_title(label=anns[0]["image_id"])             for ann in anns:                 if "counts" in ann['segmentation']:                     seg = ann['segmentation']                      # rle is Run Length Encoding.                     uncompressed_rle = [seg['counts']]                     height, width = seg['size']                     compressed_rle = mask.frPyObjects(pyobj=uncompressed_rle,                                                       h=height, w=width)                     # rld is Run Length Decoding.                     compressed_rld = mask.decode(rleObjs=compressed_rle)                     y_plts, x_plts = np.nonzero(a=np.squeeze(a=compressed_rld))                     axis.plot(x_plts, y_plts, color='yellow')                 else:                     for seg in ann['segmentation']:                         seg_arrs = np.split(ary=np.array(seg),                                             indices_or_sections=len(seg)/2)                         poly = Polygon(xy=seg_arrs,                                        facecolor="lightgreen", alpha=0.7)                         axis.add_patch(p=poly)                         x_plts = [seg_arr[0] for seg_arr in seg_arrs]                         y_plts = [seg_arr[1] for seg_arr in seg_arrs]                         axis.plot(x_plts, y_plts, color='yellow')                 x, y, w, h = ann['bbox']                 rect = Rectangle(xy=(x, y), width=w, height=h,                                  linewidth=3, edgecolor='r',                                  facecolor='none', zorder=2)                 axis.add_patch(p=rect)                 if data[i][1] and 'keypoints' in data[i][1][0]:                     kps = ann['keypoints']                     kps_arrs = np.split(ary=np.array(kps),                                         indices_or_sections=len(kps)/3)                     x_plts = [kps_arr[0] for kps_arr in kps_arrs]                     y_plts = [kps_arr[1] for kps_arr in kps_arrs]                     nonzeros_x_plts = []                     nonzeros_y_plts = []                     for x_plt, y_plt in zip(x_plts, y_plts):                         if x_plt == 0 and y_plt == 0:                             continue                         nonzeros_x_plts.append(x_plt)                         nonzeros_y_plts.append(y_plt)                     axis.scatter(x=nonzeros_x_plts, y=nonzeros_y_plts,                                  color='yellow')                     # ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ Bad result ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓                     # axis.plot(nonzeros_x_plts, nonzeros_y_plts)         if not data[i][1]:             im, _ = data[i]             axis.imshow(X=im)     fig.tight_layout()     plt.show()  ims = (26, 179, 194)  show_images1(data=cap_train2014_data, ims=ims,              main_title="cap_train2014_data") show_images1(data=ins_train2014_data, ims=ims,              main_title="ins_train2014_data") show_images1(data=pk_train2014_data, ims=ims,              main_title="pk_train2014_data") print() show_images1(data=cap_val2014_data, ims=ims,               main_title="cap_val2014_data") show_images1(data=ins_val2014_data, ims=ims,               main_title="ins_val2014_data") show_images1(data=pk_val2014_data, ims=ims,              main_title="pk_val2014_data") print() show_images1(data=test2014_data, ims=ims,              main_title="test2014_data") show_images1(data=test2015_data, ims=ims,              main_title="test2015_data") show_images1(data=testdev2015_data, ims=ims,              main_title="testdev2015_data")  # `show_images2()` works very well for the images with segmentations and # keypoints. def show_images2(data, index, main_title=None):     img_set = data[index]     img, img_anns = img_set      if img_anns and "segmentation" in img_anns[0]:         img_id = img_anns[0]['image_id']         coco = data.coco         def show_image(imgIds, areaRng=[],                        iscrowd=None, draw_bbox=False):             plt.figure(figsize=(11, 6))             plt.imshow(X=img)             plt.suptitle(t=main_title, y=1, fontsize=14)             plt.title(label=img_id, fontsize=14)             anns_ids = coco.getAnnIds(imgIds=img_id,                                       areaRng=areaRng, iscrowd=iscrowd)             anns = coco.loadAnns(ids=anns_ids)             coco.showAnns(anns=anns, draw_bbox=draw_bbox)             plt.show()         show_image(imgIds=img_id, draw_bbox=True)         show_image(imgIds=img_id, draw_bbox=False)         show_image(imgIds=img_id, iscrowd=False, draw_bbox=True)         show_image(imgIds=img_id, areaRng=[0, 5000], draw_bbox=True)     elif img_anns and not "segmentation" in img_anns[0]:         plt.figure(figsize=(11, 6))         img_id = img_anns[0]['image_id']         plt.imshow(X=img)         plt.suptitle(t=main_title, y=1, fontsize=14)         plt.title(label=img_id, fontsize=14)         plt.show()     elif not img_anns:         plt.figure(figsize=(11, 6))         plt.imshow(X=img)         plt.suptitle(t=main_title, y=1, fontsize=14)         plt.show() show_images2(data=ins_val2014_data, index=179,              main_title="ins_val2014_data") print() show_images2(data=pk_val2014_data, index=179,              main_title="pk_val2014_data") print() show_images2(data=ins_val2014_data, index=194,              main_title="ins_val2014_data") print() show_images2(data=pk_val2014_data, index=194,              main_title="pk_val2014_data")