Randomcrop in Pytorch (1)

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*Memos:

• Mein Beitrag erklärt Oxfordiiitpet ().

randomCrop () kann ein Bild zufällig wie unten gezeigt aufnehmen:

*Memos:

• Das erste Argument für die Initialisierung ist die Größe (Erforderlich: int oder tuple/list (int) oder size ()): *Memos:
  • Es ist [Höhe, Breite].
  • Es muss 1 & lt; = x.
  sein
  • Eine Tupel/Liste muss der 1D mit 1 oder 2 Elementen sein.
  • Ein einzelner Wert (int oder tuple/list (int)) bedeutet [Größe, Größe].
• Das 2. Argument für die Initialisierung ist die Polsterung (optional-default: none-type: int oder tuple/list (int)): *Memos:
  • Es ist [links, oben, rechts, unten], die aus [links-rechts, oben bottom] oder [links-top-right-bottom] konvertiert werden können.
  • Ein Tupel/eine Liste muss der 1D mit 1, 2 oder 4 Elementen sein.
  • Ein einzelner Wert (int oder tuple/list (int)) bedeutet [Polsterung, Polsterung, Polsterung, Polsterung].
  • Doppelwerte (Tuple/List (int)) bedeutet [Polster [0], Polster [1], Polster [0], Polster [1]].
• Das 3. Argument für die Initialisierung ist pad_if_needed (Optional-Default: False-Typ: Bool):
  • Wenn es falsch ist und die Größe kleiner als ein Originalbild oder das gepolsterte Bild durch Polsterung ist, gibt es Fehler.
  • Wenn es wahr ist und die Größe kleiner als ein Originalbild oder das gepolsterte Bild durch Polsterung ist, gibt es keinen Fehler, dann wird das Bild zufällig gepolstert, um zu Größe zu werden.
• Das 4. Argument für die Initialisierung ist gefüllt (Optional-Default: 0-Typ: INT, Float oder Tupel/List (int oder Float)): *Memos:
  • Es kann den Hintergrund eines Bildes ändern. *Der Hintergrund ist zu sehen, wenn ein Bild positiv gepolstert ist.
  • Ein Tupel/eine Liste muss die 1D mit 1 oder 3 Elementen sein.
• Das 5. Argument für die Initialisierung ist padding_mode (optional-default: 'Constant'-Typ: str). *'Konstant', 'Edge', 'reflektiert' oder 'symmetrisch' kann darauf eingestellt werden.
• Das erste Argument ist IMG (Erforderlich: Pil-Bild oder Tensor (int)): *Memos:
  • Ein Tensor muss 2D oder 3D.
  sein
  • Verwenden Sie nicht img =.
• V2 wird empfohlen, nach V1 oder V2 zu verwenden? Welches soll ich verwenden?

from torchvision.datasets import OxfordIIITPet
from torchvision.transforms.v2 import RandomCrop

randomcrop = RandomCrop(size=100)
randomcrop = RandomCrop(size=100,
                        padding=None,
                        pad_if_needed=False, 
                        fill=0,
                        padding_mode='constant')
randomcrop
# RandomCrop(size=(100, 100),
#            pad_if_needed=False,
#            fill=0,
#            padding_mode=constant)

randomcrop.size
# (100, 100)

print(randomcrop.padding)
# None

randomcrop.pad_if_needed
# False

randomcrop.fill
# 0

randomcrop.padding_mode
# 'constant'

origin_data = OxfordIIITPet(
    root="data",
    transform=None
)

s300_data = OxfordIIITPet( # `s` is size.
    root="data",
    transform=RandomCrop(size=300)
    # transform=RandomCrop(size=[300, 300])
)

s200_data = OxfordIIITPet(
    root="data",
    transform=RandomCrop(size=200)
)

s100_data = OxfordIIITPet(
    root="data",
    transform=RandomCrop(size=100)
)

s50_data = OxfordIIITPet(
    root="data",
    transform=RandomCrop(size=50)
)

s10_data = OxfordIIITPet(
    root="data",
    transform=RandomCrop(size=10)
)

s1_data = OxfordIIITPet(
    root="data",
    transform=RandomCrop(size=1)
)

s200_300_data = OxfordIIITPet(
    root="data",
    transform=RandomCrop(size=[200, 300])
)

s300_200_data = OxfordIIITPet(
    root="data",
    transform=RandomCrop(size=[300, 200])
)

s300p100_data = OxfordIIITPet( # `p` is padding.
    root="data",
    transform=RandomCrop(size=300, padding=100)
    # transform=RandomCrop(size=300, padding=[100, 100])
    # transform=RandomCrop(size=300, padding=[100, 100, 100, 100])
)

s300p200_data = OxfordIIITPet(
    root="data",
    transform=RandomCrop(size=300, padding=200)
)

s700_594p100origin_data = OxfordIIITPet(
    root="data",
    transform=RandomCrop(size=[700, 594], padding=100)
)

s300p100_data = OxfordIIITPet(
    root="data",
    transform=RandomCrop(size=300, padding=100)
)

s600_594p100_50origin_data = OxfordIIITPet(
    root="data",
    transform=RandomCrop(size=[600, 594], padding=[100, 50])
)

s300p100_50_data = OxfordIIITPet(
    root="data",
    transform=RandomCrop(size=300, padding=[100, 50])
)

s650_494p25_50_75_100origin_data = OxfordIIITPet(
    root="data",
    transform=RandomCrop(size=[650, 494], padding=[25, 50, 75, 100])
)

s300p25_50_75_100_data = OxfordIIITPet(
    root="data",
    transform=RandomCrop(size=300, padding=[25, 50, 75, 100])
)

s300_194pn100origin_data = OxfordIIITPet( # `n` is negative.
    root="data",
    transform=RandomCrop(size=[300, 194], padding=-100)
)

s150pn100_data = OxfordIIITPet(
    root="data",
    transform=RandomCrop(size=150, padding=-100)
)

s300_294pn50n100origin_data = OxfordIIITPet(
    root="data",
    transform=RandomCrop(size=[300, 294], padding=[-50, -100])
)

s150pn50n100_data = OxfordIIITPet(
    root="data",
    transform=RandomCrop(size=150, padding=[-50, -100])
)

s350_294pn25n50n75n100origin_data = OxfordIIITPet(
    root="data",
    transform=RandomCrop(size=[350, 294], padding=[-25, -50, -75, -100])
)

s150pn25n50n75n100_data = OxfordIIITPet(
    root="data",
    transform=RandomCrop(size=150, padding=[-25, -50, -75, -100])
)

s600_444p25_50origin_data = OxfordIIITPet(
    root="data",
    transform=RandomCrop(size=[600, 444], padding=[25, 50])
)

s200p25_50_data = OxfordIIITPet(
    root="data",
    transform=RandomCrop(size=200, padding=[25, 50])
)

s400_344pn25n50origin_data = OxfordIIITPet(
    root="data",
    transform=RandomCrop(size=[400, 344], padding=[-25, -50])
)

s200pn25n50_data = OxfordIIITPet(
    root="data",
    transform=RandomCrop(size=200, padding=[-25, -50])
)

s400_444p25n50origin_data = OxfordIIITPet(
    root="data",
    transform=RandomCrop(size=[400, 444], padding=[25, -50])
)

s200p25n50_data = OxfordIIITPet(
    root="data",
    transform=RandomCrop(size=200, padding=[25, -50])
)

s600_344pn25_50origin_data = OxfordIIITPet(
    root="data",
    transform=RandomCrop(size=[600, 344], padding=[-25, 50])
)

s200pn25_50_data = OxfordIIITPet(
    root="data",
    transform=RandomCrop(size=200, padding=[-25, 50])
)

s700_594p100fgrayorigin_data = OxfordIIITPet( # `f` is fill.
    root="data",
    transform=RandomCrop(size=[700, 594], padding=100, fill=150)
    # transform=RandomCrop(size=[700, 594], padding=100, fill=[150])
)

s300p100fgray_data = OxfordIIITPet(
    root="data",
    transform=RandomCrop(size=300, padding=100, fill=150)
)

s700_594p100fpurpleorigin_data = OxfordIIITPet(
    root="data",
    transform=RandomCrop(size=[700, 594], padding=100, fill=[160, 32, 240])
)

s300p100fpurple_data = OxfordIIITPet(
    root="data",
    transform=RandomCrop(size=300, padding=100, fill=[160, 32, 240])
)

s700_594p100pmconstorigin_data = OxfordIIITPet( # `pm` is padding_mode.
    root="data",                                # `const` is constant.
    transform=RandomCrop(size=[700, 594], padding=100, padding_mode='constant')
)

s300p100pmconst_data = OxfordIIITPet(
    root="data",
    transform=RandomCrop(size=300, padding=100, padding_mode='constant')
)

s700_594p100pmedgeorigin_data = OxfordIIITPet(
    root="data",
    transform=RandomCrop(size=[700, 594], padding=100, padding_mode='edge')
)

s300p100pmedge_data = OxfordIIITPet(
    root="data",
    transform=RandomCrop(size=300, padding=100, padding_mode='edge')
)

s700_594p100pmrefleorigin_data = OxfordIIITPet( # `refle` is reflect.
    root="data",
    transform=RandomCrop(size=[700, 594], padding=100, padding_mode='reflect')
)

s300p100pmrefle_data = OxfordIIITPet(
    root="data",
    transform=RandomCrop(size=300, padding=100, padding_mode='reflect')
)

s700_594p100pmsymmeorigin_data = OxfordIIITPet( # `symme` is symmetric.
    root="data",
    transform=RandomCrop(size=[700, 594], padding=100, 
                         padding_mode='symmetric')
)

s300p100pmsymme_data = OxfordIIITPet(
    root="data",
    transform=RandomCrop(size=300, padding=100, padding_mode='symmetric')
)

import matplotlib.pyplot as plt

def show_images1(data, main_title=None):
    plt.figure(figsize=(10, 5))
    plt.suptitle(t=main_title, y=0.8, fontsize=14)
    for i in range(1, 6):
        plt.subplot(1, 5, i)
        plt.imshow(X=data[0][0])
    plt.tight_layout()
    plt.show()

plt.figure(figsize=(7, 9))
plt.title(label="s500_394origin_data", fontsize=14)
plt.imshow(X=origin_data[0][0])
show_images1(data=origin_data, main_title="s500_394origin_data")
show_images1(data=s300_data, main_title="s300_data")
show_images1(data=s200_data, main_title="s200_data")
show_images1(data=s100_data, main_title="s100_data")
show_images1(data=s50_data, main_title="s50_data")
show_images1(data=s10_data, main_title="s10_data")
show_images1(data=s1_data, main_title="s1_data")
show_images1(data=s200_300_data, main_title="s200_300_data")
show_images1(data=s300_200_data, main_title="s300_200_data")
print()
show_images1(data=s700_594p100origin_data,
             main_title="s700_594p100origin_data")
show_images1(data=s300p100_data, main_title="s300p100_data")
print()
show_images1(data=s600_594p100_50origin_data,
             main_title="s600_594p100_50origin_data")
show_images1(data=s300p100_50_data, main_title="s300p100_50_data")
print()
show_images1(data=s650_494p25_50_75_100origin_data,
             main_title="s650_494p25_50_75_100origin_data")
show_images1(data=s300p25_50_75_100_data, 
             main_title="s300p25_50_75_100_data")
print()
show_images1(data=s300_194pn100origin_data,
             main_title="s300_194pn100origin_data")
show_images1(data=s150pn100_data, 
             main_title="s150pn100_data")
print()
show_images1(data=s300_294pn50n100origin_data,
             main_title="s300_294pn50n100origin_data")
show_images1(data=s150pn50n100_data, 
             main_title="s150pn50n100_data")
print()
show_images1(data=s350_294pn25n50n75n100origin_data,
             main_title="s350_294pn25n50n75n100origin_data")
show_images1(data=s150pn25n50n75n100_data, 
             main_title="s150pn25n50n75n100_data")
print()
show_images1(data=s600_444p25_50origin_data,
             main_title="s600_444p25_50origin_data")
show_images1(data=s200p25_50_data, 
             main_title="s200p25_50_data")
print()
show_images1(data=s400_344pn25n50origin_data,
             main_title="s400_344pn25n50origin_data")
show_images1(data=s200pn25n50_data, 
             main_title="s200pn25n50_data")
print()
show_images1(data=s400_444p25n50origin_data,
             main_title="s400_444p25n50origin_data")
show_images1(data=s200p25n50_data, 
             main_title="s200p25n50_data")
print()
show_images1(data=s600_344pn25_50origin_data,
             main_title="s600_344pn25_50origin_data")
show_images1(data=s200pn25_50_data, 
             main_title="s200pn25_50_data")
print()
show_images1(data=s700_594p100fgrayorigin_data,
             main_title="s700_594p100fgrayorigin_data")
show_images1(data=s300p100fgray_data, main_title="s300p100fgray_data")
print()
show_images1(data=s700_594p100fpurpleorigin_data,
             main_title="s700_594p100fpurpleorigin_data")
show_images1(data=s300p100fpurple_data, main_title="s300p100fpurple_data")
print()
show_images1(data=s700_594p100pmconstorigin_data,
             main_title="s700_594p100pmconstorigin_data")
show_images1(data=s300p100pmconst_data, main_title="s300p100pmconst_data")
print()
show_images1(data=s700_594p100pmedgeorigin_data,
             main_title="s700_594p100pmedgeorigin_data")
show_images1(data=s300p100pmedge_data, main_title="s300p100pmedge_data")
print()
show_images1(data=s700_594p100pmrefleorigin_data,
             main_title="s700_594p100pmrefleorigin_data")
show_images1(data=s300p100pmrefle_data, main_title="s300p100pmrefle_data")
print()
show_images1(data=s700_594p100pmsymmeorigin_data,
             main_title="s700_594p100pmsymmeorigin_data")
show_images1(data=s300p100pmsymme_data, main_title="s300p100pmsymme_data")

# ↓ ↓ ↓ ↓ ↓ ↓ The code below is identical to the code above. ↓ ↓ ↓ ↓ ↓ ↓
def show_images2(data, main_title=None, s=None, p=None,
                 pin=False, f=0, pm='constant'):
    plt.figure(figsize=(10, 5))
    plt.suptitle(t=main_title, y=0.8, fontsize=14)
    temp_s = s
    im = data[0][0]
    for i in range(1, 6):
        plt.subplot(1, 5, i)
        if not temp_s:
            s = [im.size[1], im.size[0]]
        rc = RandomCrop(size=s, padding=p, # Here
                        pad_if_needed=pin, fill=f, padding_mode=pm)
        plt.imshow(X=rc(im)) # Here
    plt.tight_layout()
    plt.show()

plt.figure(figsize=(7, 9))
plt.title(label="s500_394origin_data", fontsize=14)
plt.imshow(X=origin_data[0][0])
show_images2(data=origin_data, main_title="s500_394origin_data")
show_images2(data=origin_data, main_title="s300_data", s=300)
show_images2(data=origin_data, main_title="s200_data", s=200)
show_images2(data=origin_data, main_title="s100_data", s=100)
show_images2(data=origin_data, main_title="s50_data", s=50)
show_images2(data=origin_data, main_title="s10_data", s=10)
show_images2(data=origin_data, main_title="s1_data", s=1)
show_images2(data=origin_data, main_title="s200_300_data", s=[200, 300])
show_images2(data=origin_data, main_title="s300_200_data", s=[300, 200])
print()
show_images2(data=origin_data, main_title="s700_594p100origin_data",
             s=[700, 594], p=100)
show_images2(data=origin_data, main_title="s300p100_data", s=300, p=100)
print()
show_images2(data=origin_data, main_title="s600_594p100_50origin_data",
             s=[600, 594], p=[100, 50])
show_images2(data=origin_data, main_title="s300p100_50_data", s=300,
             p=[100, 50])
print()
show_images2(data=origin_data, main_title="s650_494p25_50_75_100origin_data",
             s=[650, 494], p=[25, 50, 75, 100])
show_images2(data=origin_data, main_title="s300p25_50_75_100_data", s=300, 
             p=[25, 50, 75, 100])
print()
show_images2(data=origin_data, main_title="s300_194pn100origin_data",
             s=[300, 194], p=-100)
show_images2(data=origin_data, main_title="s150pn100_data", s=150, p=-100)
print()
show_images2(data=origin_data, main_title="s300_294pn50n100origin_data",
             s=[300, 294], p=[-50, -100])
show_images2(data=origin_data, main_title="s150pn50n100_data", s=150,
             p=[-50, -100])
print()
show_images2(data=origin_data, main_title="s350_294pn25n50n75n100origin_data",
             s=[350, 294], p=[-25, -50, -75, -100])
show_images2(data=origin_data, main_title="s150pn25n50n75n100_data", s=150,
             p=[-25, -50, -75, -100])
print()
show_images2(data=origin_data, main_title="s600_444p25_50origin_data",
             s=[600, 444], p=[25, 50])
show_images2(data=origin_data, main_title="s200p25_50_data", s=200,
             p=[25, 50])
print()
show_images2(data=origin_data, main_title="s400_344pn25n50origin_data",
             s=[400, 344], p=[-25, -50])
show_images2(data=origin_data, main_title="s200pn25n50_data", s=200,
             p=[-25, -50])
print()
show_images2(data=origin_data, main_title="s400_444p25n50origin_data",
             s=[400, 444], p=[25, -50])
show_images2(data=origin_data, main_title="s200p25n50_data", s=200,
             p=[25, -50])
print()
show_images2(data=origin_data, main_title="s600_344pn25_50origin_data",
             s=[600, 344], p=[-25, 50])
show_images2(data=origin_data, main_title="s200pn25_50_data", s=200,
             p=[-25, 50])
print()
show_images2(data=origin_data, main_title="s700_594p100fgrayorigin_data", 
             s=[700, 594], p=100, f=150)
show_images2(data=origin_data, main_title="s300p100fgray_data", s=300,
             p=100, f=150)
print()
show_images2(data=origin_data, main_title="s700_594p100fpurpleorigin_data",
             s=[700, 594], p=100, f=[160, 32, 240])
show_images2(data=origin_data, main_title="s300p100fpurple_data", s=300,
             p=100, f=[160, 32, 240])
print()
show_images2(data=origin_data, main_title="s700_594p100pmconstorigin_data",
             s=[700, 594], p=100, pm='constant')
show_images2(data=origin_data, main_title="s300p100pmconst_data", s=300, 
             p=100, pm='constant')
print()
show_images2(data=origin_data, main_title="s700_594p100pmedgeorigin_data",
             s=[700, 594], p=100, pm='edge')
show_images2(data=origin_data, main_title="s300p100pmedge_data", s=300, 
             p=100, pm='edge')
print()
show_images2(data=origin_data, main_title="s700_594p100pmrefleorigin_data",
             s=[700, 594], p=100, pm='reflect')
show_images2(data=origin_data, main_title="s300p100pmrefle_data", s=300, 
             p=100, pm='reflect')
print()
show_images2(data=origin_data, main_title="s700_594p100pmsymmeorigin_data",
             s=[700, 594], p=100, pm='symmetric')
show_images2(data=origin_data, main_title="s300p100pmsymme_data", s=300, 
             p=100, pm='symmetric')

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