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首页 后端开发 Python教程 PyTorch 中的 CocoDetection (1)

PyTorch 中的 CocoDetection (1)

DDD
DDD
Jan 04, 2025 pm 12:26 PM

请我喝杯咖啡☕

*我的帖子解释了 MS COCO。

CocoDetection()可以使用MS COCO数据集，如下所示：

*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 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/test2014.json"
)

test2015_data = CocoDetection(
    root="data/coco/imgs/test2015",
    annFile="data/coco/anns/test2015/test2015.json"
)

testdev2015_data = CocoDetection(
    root="data/coco/imgs/test2015",
    annFile="data/coco/anns/test2015/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[0]
# (<PIL.Image.Image image mode=RGB size=640x480>,
#  [{'image_id': 9, 'id': 661611,
#    'caption': 'Closeup of bins of food that include broccoli and bread.'},
#   {'image_id': 9, 'id': 661977,
#    'caption': 'A meal is presented in brightly colored plastic trays.'},
#   {'image_id': 9, 'id': 663627,
#    'caption': 'there are containers filled with different kinds of foods'},
#   {'image_id': 9, 'id': 666765,
#    'caption': 'Colorful dishes holding meat, vegetables, fruit, and bread.'},
#   {'image_id': 9, 'id': 667602,
#    'caption': 'A bunch of trays that have different food.'}])　

cap_train2014_data[1]
# (<PIL.Image.Image image mode=RGB size=640x426>,
#  [{'image_id': 25, 'id': 122312,
#    'caption': 'A giraffe eating food from the top of the tree.'},
#   {'image_id': 25, 'id': 127076,
#    'caption': 'A giraffe standing up nearby a tree '},
#   {'image_id': 25, 'id': 127238,
#    'caption': 'A giraffe mother with its baby in the forest.'},
#   {'image_id': 25, 'id': 133058,
#    'caption': 'Two giraffes standing in a tree filled area.'},
#   {'image_id': 25, 'id': 133676,
#    'caption': 'A giraffe standing next to a forest filled with trees.'}])

cap_train2014_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.'}])

ins_train2014_data[0]
# (<PIL.Image.Image image mode=RGB size=640x480>,
#  [{'segmentation': [[500.49, 473.53, 599.73, ..., 20.49, 473.53]],
#    'area': 120057.13925, 'iscrowd': 0, 'image_id': 9,
#    'bbox': [1.08, 187.69, 611.59, 285.84], 'category_id': 51,
#    'id': 1038967},
#   {'segmentation': ..., 'category_id': 51, 'id': 1039564},
#   ...,
#   {'segmentation': ..., 'category_id': 55, 'id': 1914001}])

ins_train2014_data[1]
# (<PIL.Image.Image image mode=RGB size=640x426>,
#  [{'segmentation': [[437.52, 353.33, 437.87, ..., 437.87, 357.19]],
#    'area': 19686.597949999996, 'iscrowd': 0, 'image_id': 25,
#    'bbox': [385.53, 60.03, 214.97, 297.16], 'category_id': 25,
#    'id': 598548},
#  {'segmentation': [[99.26, 405.72, 133.57, ..., 97.77, 406.46]],
#   'area': 2785.8475500000004, 'iscrowd': 0, 'image_id': 25,
#   'bbox': [53.01, 356.49, 132.03, 55.19], 'category_id': 25,
#   'id': 599491}])

ins_train2014_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': [[394.34, 155.81, 403.96, ..., 393.38, 157.73]],
#    'area': 16202.798250000003, 'iscrowd': 0, 'image_id': 30,
#    'bbox': [237.56, 155.81, 166.4, 195.25], 'category_id': 86,
#    'id': 1155486}])

pk_train2014_data[0]
# (<PIL.Image.Image image mode=RGB size=640x480>, [])

pk_train2014_data[1]
# (<PIL.Image.Image image mode=RGB size=640x426>, [])

pk_train2014_data[2]
# (<PIL.Image.Image image mode=RGB size=640x428>, [])

cap_val2014_data[0]
# (<PIL.Image.Image image mode=RGB size=640x478>,
#  [{'image_id': 42, 'id': 641613,
#    'caption': 'This wire metal rack holds several pairs of shoes and sandals'},
#   {'image_id': 42, 'id': 645309,
#    'caption': 'A dog sleeping on a show rack in the shoes.'},
#   {'image_id': 42, 'id': 650217,
#    'caption': 'Various slides and other footwear rest in a metal basket outdoors.'},
#   {'image_id': 42,
#    'id': 650868,
#    'caption': 'A small dog is curled up on top of the shoes'},
#   {'image_id': 42,
#    'id': 652383,
#    'caption': 'a shoe rack with some shoes and a dog sleeping on them'}])

cap_val2014_data[1]
# (<PIL.Image.Image image mode=RGB size=565x640>,
#  [{'image_id': 73, 'id': 593422,
#    'caption': 'A motorcycle parked in a parking space next to another motorcycle.'},
#   {'image_id': 73, 'id': 746071,
#    'caption': 'An old motorcycle parked beside other motorcycles with a brown leather seat.'},
#   {'image_id': 73, 'id': 746170,
#    'caption': 'Motorcycle parked in the parking lot of asphalt.'},
#   {'image_id': 73, 'id': 746914,
#    'caption': 'A close up view of a motorized bicycle, sitting in a rack. '},
#   {'image_id': 73, 'id': 748185,
#    'caption': 'The back tire of an old style motorcycle is resting in a metal stand. '}])

cap_val2014_data[2]
# (<PIL.Image.Image image mode=RGB size=640x426>,
#  [{'image_id': 74, 'id': 145996,
#    'caption': 'A picture of a dog laying on the ground.'},
#   {'image_id': 74, 'id': 146710,
#    'caption': 'Dog snoozing by a bike on the edge of a cobblestone street'},
#   {'image_id': 74, 'id': 149398,
#    'caption': 'The white dog lays next to the bicycle on the sidewalk.'},
#   {'image_id': 74, 'id': 149638,
#    'caption': 'a white dog is sleeping on a street and a bicycle'},
#   {'image_id': 74, 'id': 150181,
#    'caption': 'A puppy rests on the street next to a bicycle.'}])

ins_val2014_data[0]
# (<PIL.Image.Image image mode=RGB size=640x478>,
#  [{'segmentation': [[382.48, 268.63, 330.24, ..., 394.09, 264.76]],
#    'area': 53481.5118, 'iscrowd': 0, 'image_id': 42,
#    'bbox': [214.15, 41.29, 348.26, 243.78], 'category_id': 18,
#    'id': 1817255}])

ins_val2014_data[1]
# (<PIL.Image.Image image mode=RGB size=565x640>,
#  [{'segmentation': [[134.36, 145.55, 117.02, ..., 138.69, 141.22]],
#    'area': 172022.43864999997, 'iscrowd': 0, 'image_id': 73,
#    'bbox': [13.0, 22.75, 535.98, 609.67], 'category_id': 4,
#    'id': 246920},
#   {'segmentation': [[202.28, 4.97, 210.57, 26.53, ..., 192.33, 3.32]],
#    'area': 52666.3402, 'iscrowd': 0, 'image_id': 73,
#    'bbox': [1.66, 3.32, 268.6, 271.91], 'category_id': 4,
#    'id': 2047387}])

ins_val2014_data[2]
# (<PIL.Image.Image image mode=RGB size=640x426>,
#  [{'segmentation': [[321.02, 321.0, 314.25, ..., 320.57, 322.86]],
#    'area': 18234.62355, 'iscrowd': 0, 'image_id': 74,
#    'bbox': [61.87, 276.25, 296.42, 103.18], 'category_id': 18,
#    'id': 1774},
#   {'segmentation': ..., 'category_id': 2, 'id': 128367},
#   ...
#   {'segmentation': ..., 'category_id': 1, 'id': 1751664}])

pk_val2014_data[0]
# (<PIL.Image.Image image mode=RGB size=640x478>, [])

pk_val2014_data[1]
# (<PIL.Image.Image image mode=RGB size=565x640>, [])

pk_val2014_data[2]
# (<PIL.Image.Image image mode=RGB size=640x426>,
#  [{'segmentation': [[301.32, 93.96, 305.72, ..., 299.67, 94.51]],
#    'num_keypoints': 0, 'area': 638.7158, 'iscrowd': 0,
#    'keypoints': [0, 0, 0, 0, ..., 0, 0], 'image_id': 74,
#    'bbox': [295.55, 93.96, 18.42, 58.83], 'category_id': 1,
#    'id': 195946},
#   {'segmentation': ..., 'category_id': 1, 'id': 253933},
#   ...
#   {'segmentation': ..., 'category_id': 1, 'id': 1751664}])

test2014_data[0]
# (<PIL.Image.Image image mode=RGB size=640x480>, [])

test2014_data[1]
# (<PIL.Image.Image image mode=RGB size=480x640>, [])

test2014_data[2]
# (<PIL.Image.Image image mode=RGB size=480x640>, [])

test2015_data[0]
# (<PIL.Image.Image image mode=RGB size=640x480>, [])

test2015_data[1]
# (<PIL.Image.Image image mode=RGB size=480x640>, [])

test2015_data[2]
# (<PIL.Image.Image image mode=RGB size=480x640>, [])

testdev2015_data[0]
# (<PIL.Image.Image image mode=RGB size=640x480>, [])

testdev2015_data[1]
# (<PIL.Image.Image image mode=RGB size=480x640>, [])

testdev2015_data[2]
# (<PIL.Image.Image image mode=RGB size=640x427>, [])

import matplotlib.pyplot as plt
from matplotlib.patches import Polygon, Rectangle
import torch

def show_images(data, main_title=None):
    file = data.root.split('/')[-1]
    if data[0][1] and "caption" in data[0][1][0]:
        if file == "train2014":
            plt.figure(figsize=(14, 5))
            plt.suptitle(t=main_title, y=0.9, fontsize=14)
            x_axis = 0.02
            x_axis_incr = 0.325
            fs = 10.5
        elif file == "val2014":
            plt.figure(figsize=(14, 6.5))
            plt.suptitle(t=main_title, y=0.94, fontsize=14)
            x_axis = 0.01
            x_axis_incr = 0.32
            fs = 9.4
        for i, (im, ann) in zip(range(1, 4), data):
            plt.subplot(1, 3, i)
            plt.imshow(X=im)
            plt.title(label=ann[0]["image_id"])
            y_axis = 0.0
            for j in range(0, 5):
                plt.figtext(x=x_axis, y=y_axis, fontsize=fs,
                            s=f'{ann[j]["id"]}:\n{ann[j]["caption"]}')
                if file == "train2014":
                    y_axis -= 0.1
                elif file == "val2014":
                    y_axis -= 0.07
            x_axis += x_axis_incr
            if i == 2 and file == "val2014":
                x_axis += 0.06
        plt.tight_layout()
        plt.show()
    elif data[0][1] and "segmentation" in data[0][1][0]:
        if file == "train2014":
            fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(14, 4))
        elif file == "val2014":
            fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(14, 5))
        fig.suptitle(t=main_title, y=1.0, fontsize=14)
        for (im, anns), axis in zip(data, axes.ravel()):
            for ann in anns:
                for seg in ann['segmentation']:
                    seg_tsors = torch.tensor(seg).split(2)
                    seg_lists = [seg_tsor.tolist() for seg_tsor in seg_tsors]
                    poly = Polygon(xy=seg_lists,
                                   facecolor="lightgreen", alpha=0.7)
                    axis.add_patch(p=poly)
                    px = []
                    py = []
                    for j, v in enumerate(seg):
                        if j%2 == 0:
                            px.append(v)
                        else:
                            py.append(v)
                    axis.plot(px, py, 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)
            axis.imshow(X=im)
            axis.set_title(label=anns[0]["image_id"])
        fig.tight_layout()
        plt.show()
    elif not data[0][1]:
        if file == "train2014":
            plt.figure(figsize=(14, 5))
            plt.suptitle(t=main_title, y=0.9, fontsize=14)
        elif file == "val2014":
            plt.figure(figsize=(14, 5))
            plt.suptitle(t=main_title, y=1.05, fontsize=14)
        elif file == "test2014" or "test2015":
            plt.figure(figsize=(14, 8))
            plt.suptitle(t=main_title, y=0.9, fontsize=14)
        for i, (im, _) in zip(range(1, 4), data):
            plt.subplot(1, 3, i)
            plt.imshow(X=im)
        plt.tight_layout()
        plt.show()

show_images(data=cap_train2014_data, main_title="cap_train2014_data")
show_images(data=ins_train2014_data, main_title="ins_train2014_data")
show_images(data=pk_train2014_data, main_title="pk_train2014_data")

show_images(data=cap_val2014_data, main_title="cap_val2014_data")
show_images(data=ins_val2014_data, main_title="ins_val2014_data")
show_images(data=pk_val2014_data, main_title="pk_val2014_data")

show_images(data=test2014_data, main_title="test2014_data")
show_images(data=test2015_data, main_title="test2015_data")
show_images(data=testdev2015_data, main_title="testdev2015_data")
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CocoDetection in PyTorch (1)

CocoDetection in PyTorch (1)

CocoDetection in PyTorch (1)

CocoDetection in PyTorch (1)

CocoDetection in PyTorch (1)

CocoDetection in PyTorch (1)

CocoDetection in PyTorch (1)

CocoDetection in PyTorch (1)

CocoDetection in PyTorch (1)

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