PyTorch 中的 CocoDetection（2）

from torchvision.datasets import CocoDetectioncap_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 pltfrom matplotlib.patches import Polygon, Rectangleimport numpy as npfrom 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")