Gemeinschaft
Lernen
Tools-Bibliothek
KI-Tools
Freizeit
suchen
Deutsch
Heim > Backend-Entwicklung > Python-Tutorial > CocoDetection in PyTorch (1)

CocoDetection in PyTorch (1)

DDD
Freigeben： 2025-01-04 12:26:40
Original
162 Leute haben es durchsucht

Kauf mir einen Kaffee☕

*Mein Beitrag erklärt MS COCO.

CocoDetection() kann den MS COCO-Datensatz wie unten gezeigt verwenden:

*Memos:

  • Das 1. Argument ist root(Required-Type:str oder pathlib.Path): *Memos:
    • Es ist der Weg zu den Bildern.
    • Ein absoluter oder relativer Pfad ist möglich.
  • Das 2. Argument ist annFile(Required-Type:str oder pathlib.Path): *Memos:
    • Es ist der Pfad zu den Anmerkungen.
    • Ein absoluter oder relativer Pfad ist möglich.
  • Das dritte Argument ist transform(Optional-Default:None-Type:callable).
  • Das 4. Argument ist target_transform(Optional-Default:None-Type:callable).
  • Das 5. Argument ist 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")
Nach dem Login kopieren

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)

Das obige ist der detaillierte Inhalt vonCocoDetection in PyTorch (1). Für weitere Informationen folgen Sie bitte anderen verwandten Artikeln auf der PHP chinesischen Website!

Quelle：dev.to
Vorheriger Artikel：Wie kann itertools.product das kartesische Produkt mehrerer Listen in Python generieren? Nächster Artikel：Lösen Sie die wöchentliche Herausforderungsaufgabe Schritt für Schritt in Python
Erklärung dieser Website
Der Inhalt dieses Artikels wird freiwillig von Internetnutzern beigesteuert und das Urheberrecht liegt beim ursprünglichen Autor. Diese Website übernimmt keine entsprechende rechtliche Verantwortung. Wenn Sie Inhalte finden, bei denen der Verdacht eines Plagiats oder einer Rechtsverletzung besteht, wenden Sie sich bitte an admin@php.cn
Neueste Artikel des Autors
Aktuelle Ausgaben
function_exists() kann die benutzerdefinierte Funktion nicht ermitteln Funktionstest () {Verwendung der Verwendung durch -Durch -Durch -Durch -Durch -Durch -Durc...
Aus 2024-04-29 11:01:01
0
3
2432
So zeigen Sie die mobile Version von Google Chrome an Hallo Lehrer, wie kann ich Google Chrome in eine mobile Version umwandeln?
Aus 2024-04-23 00:22:19
0
11
2562
Das untergeordnete Fenster bedient das übergeordnete Fenster, aber die Ausgabe antwortet nicht. Die ersten beiden Sätze sind ausführbar, der letzte Satz jedoch nicht.
Aus 2024-04-19 15:37:47
0
1
2166
Im übergeordneten Fenster erfolgt keine Ausgabe document.onclick = function(){ window.opener.document.write('Ich bin die Ausgabe des unter...
Aus 2024-04-18 23:52:34
0
1
2047
Wo gibt es die Kursunterlagen zum CSS-Mindmapping? Kursunterlagen
Aus 2024-04-16 10:10:18
0
0
2146
verwandte Themen
Mehr>
Beliebte Empfehlungen
Beliebte Tutorials
Mehr>
Verwandte Tutorials
Beliebte Empfehlungen
Aktuelle Kurse
Neueste Downloads
Mehr>
Web-Effekte
Quellcode der Website
Website-Materialien
Frontend-Vorlage