PyTorch 中的 CocoDetection（2）

from torchvision.datasets import CocoDetection  cap_train2017_data = CocoDetection(     root="data/coco/imgs/train2017",     annFile="data/coco/anns/trainval2017/captions_train2017.json" )  ins_train2017_data = CocoDetection(     root="data/coco/imgs/train2017",     annFile="data/coco/anns/trainval2017/instances_train2017.json" )  pk_train2017_data = CocoDetection(     root="data/coco/imgs/train2017",     annFile="data/coco/anns/trainval2017/person_keypoints_train2017.json" )  len(cap_train2017_data), len(ins_train2017_data), len(pk_train2017_data) # (118287, 118287, 118287)  cap_val2017_data = CocoDetection(     root="data/coco/imgs/val2017",     annFile="data/coco/anns/trainval2017/captions_val2017.json" )  ins_val2017_data = CocoDetection(     root="data/coco/imgs/val2017",     annFile="data/coco/anns/trainval2017/instances_val2017.json" )  pk_val2017_data = CocoDetection(     root="data/coco/imgs/val2017",     annFile="data/coco/anns/trainval2017/person_keypoints_val2017.json" )  len(cap_val2017_data), len(ins_val2017_data), len(pk_val2017_data) # (5000, 5000, 5000)  test2017_data = CocoDetection(     root="data/coco/imgs/test2017",     annFile="data/coco/anns/test2017/image_info_test2017.json" )  testdev2017_data = CocoDetection(     root="data/coco/imgs/test2017",     annFile="data/coco/anns/test2017/image_info_test-dev2017.json" )  len(test2017_data), len(testdev2017_data) # (40670, 20288)  cap_train2017_data[2] # (<PIL.Image.Image image mode=RGB size=640x428>, #  [{'image_id': 30, 'id': 695774, #    'caption': 'A flower vase is sitting on a porch stand.'}, #   {'image_id': 30, 'id': 696557, #    'caption': 'White vase with different colored flowers sitting inside of it. '}, #   {'image_id': 30, 'id': 699041, #    'caption': 'a white vase with many flowers on a stage'}, #   {'image_id': 30, 'id': 701216, #    'caption': 'A white vase filled with different colored flowers.'}, #   {'image_id': 30, 'id': 702428, #    'caption': 'A vase with red and white flowers outside on a sunny day.'}])  cap_train2017_data[47] # (<PIL.Image.Image image mode=RGB size=640x427>, #  [{'image_id': 294, 'id': 549895, #    'caption': 'A man standing in front of a microwave next to pots and pans.'}, #   {'image_id': 294, 'id': 556411, #    'caption': 'A man displaying pots and utensils on a wall.'}, #   {'image_id': 294, 'id': 556507, #    'caption': 'A man stands in a kitchen and motions towards pots and pans. '}, #   {'image_id': 294, 'id': 556993, #    'caption': 'a man poses in front of some pots and pans '}, #   {'image_id': 294, 'id': 560728, #    'caption': 'A man pointing to pots hanging from a pegboard on a gray wall.'}])  cap_train2017_data[64] # (<PIL.Image.Image image mode=RGB size=480x640>, #  [{'image_id': 370, 'id': 468271, #    'caption': 'A little girl holding wet broccoli in her hand. '}, #   {'image_id': 370, 'id': 471646, #    'caption': 'The young child is happily holding a fresh vegetable. '}, #   {'image_id': 370, 'id': 475471, #    'caption': 'A little girl holds a hand full of wet broccoli. '}, #   {'image_id': 370, 'id': 475663, #    'caption': 'A little girl holds a piece of broccoli towards the camera.'}, #   {'image_id': 370, 'id': 822588, #    'caption': 'a small kid holds on to some vegetables '}])  ins_train2017_data[2] # (<PIL.Image.Image image mode=RGB size=640x428>, #  [{'segmentation': [[267.38, 330.14, 281.81, ..., 269.3, 329.18]], #    'area': 47675.66289999999, 'iscrowd': 0, 'image_id': 30, #    'bbox': [204.86, 31.02, 254.88, 324.12], 'category_id': 64, #    'id': 291613}, #   {'segmentation': ..., 'category_id': 86, 'id': 1155486}])  ins_train2017_data[47] # (<PIL.Image.Image image mode=RGB size=640x427>, #  [{'segmentation': [[27.7, 423.27, 27.7, ..., 28.66, 427.0]], #    'area': 64624.86664999999, 'iscrowd': 0, 'image_id': 294, #    'bbox': [27.7, 69.83, 364.91, 357.17], 'category_id': 1, #    'id': 470246}, #   {'segmentation': ..., 'category_id': 50, 'id': 708187}, #   ... #   {'segmentation': ..., 'category_id': 50, 'id': 2217190}])  ins_train2017_data[67] # (<PIL.Image.Image image mode=RGB size=480x640>, #  [{'segmentation': [[90.81, 155.68, 90.81, ..., 98.02, 207.57]], #    'area': 137679.34520000007, 'iscrowd': 0, 'image_id': 370, #    'bbox': [90.81, 24.5, 389.19, 615.5], 'category_id': 1, #    'id': 436109}, #   {'segmentation': [[257.51, 446.79, 242.45, ..., 262.02, 460.34]], #    'area': 43818.18095, 'iscrowd': 0, 'image_id': 370, #    'bbox': [242.45, 257.05, 237.55, 243.95], 'category_id': 56, #    'id': 1060727}])  pk_train2017_data[2] # (<PIL.Image.Image image mode=RGB size=640x428>, [])  pk_train2017_data[47] # (<PIL.Image.Image image mode=RGB size=640x427>, #  [{'segmentation': [[27.7, 423.27, 27.7, ..., 28.66, 427]], #    'num_keypoints': 11, 'area': 64624.86665, 'iscrowd': 0, #    'keypoints': [149, 133, 2, 159, ..., 0, 0], 'image_id': 294, #    'bbox': [27.7, 69.83, 364.91, 357.17], 'category_id': 1, #    'id': 470246}])  pk_train2017_data[64] # (<PIL.Image.Image image mode=RGB size=480x640>, #  [{'segmentation': [[90.81, 155.68, 90.81, ..., 98.02, 207.57]], #    'num_keypoints': 12, 'area': 137679.3452, 'iscrowd': 0, #    'keypoints': [229, 171, 2, 263, ..., 0, 0], 'image_id': 370, #    'bbox': [90.81, 24.5, 389.19, 615.5], 'category_id': 1, #    'id': 436109}])  cap_val2017_data[2] # (<PIL.Image.Image image mode=RGB size=640x483>, #  [{'image_id': 632, 'id': 301804, #    'caption': 'Bedroom scene with a bookcase, blue comforter and window.'}, #   {'image_id': 632, 'id': 302791, #    'caption': 'A bedroom with a bookshelf full of books.'}, #   {'image_id': 632, 'id': 305425, #    'caption': 'This room has a bed with blue sheets and a large bookcase'}, #   {'image_id': 632, 'id': 305953, #    'caption': 'A bed and a mirror in a small room.'}, #   {'image_id': 632, 'id': 306511, #    'caption': 'a bed room with a neatly made bed a window and a book shelf'}])  cap_val2017_data[47] # (<PIL.Image.Image image mode=RGB size=640x480>, #  [{'image_id': 5001, 'id': 542124, #    'caption': 'A group of people cutting a ribbon on a street.'}, #   {'image_id': 5001, 'id': 545685, #    'caption': 'A man uses a pair of big scissors to cut a pink ribbon.'}, #   {'image_id': 5001, 'id': 549285, #    'caption': 'A man cutting a ribbon at a ceremony '}, #   {'image_id': 5001, 'id': 549666, #    'caption': 'A group of people on the sidewalk watching two young children.'}, #   {'image_id': 5001, 'id': 549696, #    'caption': 'A group of people holding a large pair of scissors to a ribbon.'}])  cap_val2017_data[64] # (<PIL.Image.Image image mode=RGB size=375x500>, #  [{'image_id': 6763, 'id': 708378, #    'caption': 'A man and a women posing next to one another in front of a table.'}, #   {'image_id': 6763, 'id': 709983, #    'caption': 'A man and woman hugging in a restaurant'}, #   {'image_id': 6763, 'id': 711438, #    'caption': 'A man and woman standing next to a table.'}, #   {'image_id': 6763, 'id': 711723, #    'caption': 'A happy man and woman pose for a picture.'}, #   {'image_id': 6763, 'id': 714720, #    'caption': 'A man and woman posing for a picture in a sports bar.'}])  ins_val2017_data[2] # (<PIL.Image.Image image mode=RGB size=640x483>, #  [{'segmentation': [[5.45, 269.03, 25.08, ..., 3.27, 266.85]], #    'area': 64019.87940000001, 'iscrowd': 0, 'image_id': 632, #    'bbox': [3.27, 266.85, 401.23, 208.25], 'category_id': 65, #    'id': 315724}, #   {'segmentation': ..., 'category_id': 64, 'id': 1610466}, #   ... #   {'segmentation': {'counts': [201255, 6, 328, 6, 142, ..., 4, 34074], #    'size': [483, 640]}, 'area': 20933, 'iscrowd': 1, 'image_id': 632, #    'bbox': [416, 43, 153, 303], 'category_id': 84, #    'id': 908400000632}])  ins_val2017_data[47] # (<PIL.Image.Image image mode=RGB size=640x480>, #  [{'segmentation': [[210.34, 204.76, 227.6, ..., 195.24, 211.24]], #    'area': 5645.972500000001, 'iscrowd': 0, 'image_id': 5001, #    'bbox': [173.66, 204.76, 107.87, 238.39], 'category_id': 87, #    'id': 1158531}, #   {'segmentation': ..., 'category_id': 1, 'id': 1201627}, #   ... #   {'segmentation': {'counts': [251128, 24, 451, 32, 446, ..., 43, 353], #    'size': [480, 640]}, 'area': 10841, 'iscrowd': 1, 'image_id': 5001, #    'bbox': [523, 26, 116, 288], 'category_id': 1, 'id': 900100005001}])  ins_val2017_data[64] # (<PIL.Image.Image image mode=RGB size=375x500>,  #  [{'segmentation': [[232.06, 92.6, 369.96, ..., 223.09, 93.72]], #    'area': 11265.648799999995, 'iscrowd': 0, 'image_id': 6763 #    'bbox': [219.73, 64.57, 151.35, 126.69], 'category_id': 72, #    'id': 30601}, #   {'segmentation': ..., 'category_id': 1, 'id': 197649}, #   ... #   {'segmentation': ..., 'category_id': 1, 'id': 1228674}])  pk_val2017_data[2] # (<PIL.Image.Image image mode=RGB size=640x483>, [])  pk_val2017_data[47] # (<PIL.Image.Image image mode=RGB size=640x480>, #  [{'segmentation': [[42.07, 190.11, 45.3, ..., 48.54, 201.98]], #    'num_keypoints': 8, 'area': 5156.63, 'iscrowd': 0, #    'keypoints': [58, 56, 2, 61, ..., 0, 0], 'image_id': 5001, #    'bbox': [10.79, 32.63, 58.24, 169.35], 'category_id': 1, #    'id': 1201627},  #   {'segmentation': ..., 'category_id': 1, 'id': 1220394}, #   ... #   {'segmentation': {'counts': [251128, 24, 451, 32, 446, ..., 43, 353], #    'size': [480, 640]}, 'num_keypoints': 0, 'area': 10841, #    'iscrowd': 1, 'keypoints': [0, 0, 0, 0, ..., 0, 0], #    'image_id': 5001, 'bbox': [523, 26, 116, 288], #    'category_id': 1, 'id': 900100005001}])  pk_val2017_data[64] # (<PIL.Image.Image image mode=RGB size=375x500>, #  [{'segmentation': [[94.38, 462.92, 141.57, ..., 100.27, 459.94]], #    'num_keypoints': 10, 'area': 36153.48825, 'iscrowd': 0, #    'keypoints': [228, 202, 2, 252, ..., 0, 0], 'image_id': 6763, #    'bbox': [79.48, 131.87, 254.23, 331.05], 'category_id': 1, #    'id': 197649}, #   {'segmentation': ..., 'category_id': 1, 'id': 212640}, #   ... #   {'segmentation': ..., 'category_id': 1, 'id': 1228674}])  test2017_data[2] # (<PIL.Image.Image image mode=RGB size=640x427>, [])  test2017_data[47] # (<PIL.Image.Image image mode=RGB size=640x406>, [])  test2017_data[64] # (<PIL.Image.Image image mode=RGB size=640x427>, [])  testdev2017_data[2] # (<PIL.Image.Image image mode=RGB size=640x427>, [])  testdev2017_data[47] # (<PIL.Image.Image image mode=RGB size=480x640>, [])  testdev2017_data[64] # (<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 = (2, 47, 64)  show_images1(data=cap_train2017_data, ims=ims,              main_title="cap_train2017_data") show_images1(data=ins_train2017_data, ims=ims,               main_title="ins_train2017_data") show_images1(data=pk_train2017_data, ims=ims,               main_title="pk_train2017_data") print() show_images1(data=cap_val2017_data, ims=ims,               main_title="cap_val2017_data") show_images1(data=ins_val2017_data, ims=ims,               main_title="ins_val2017_data") show_images1(data=pk_val2017_data, ims=ims,              main_title="pk_val2017_data") print() show_images(data=test2017_data, ims=ims,             main_title="test2017_data") show_images(data=testdev2017_data, ims=ims,              main_title="testdev2017_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, 8))             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, 8))         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, 8))         plt.imshow(X=img)         plt.suptitle(t=main_title, y=1, fontsize=14)         plt.show() show_images2(data=ins_val2017_data, index=2,               main_title="ins_val2017_data") print() show_images2(data=pk_val2017_data, index=2,              main_title="pk_val2017_data") print() show_images2(data=ins_val2017_data, index=47,              main_title="ins_val2017_data") print() show_images2(data=pk_val2017_data, index=47,               main_title="pk_val2017_data")