RandomCrop in PyTorch (1)
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*Memos:
- My post explains OxfordIIITPet().
RandomCrop() can crop an image randomly as shown below:
*Memos:
- The 1st argument for initialization is size(Required-Type:int or tuple/list(int) or size()):
*Memos:
- It's [height, width].
- It must be 1 <= x.
- A tuple/list must be the 1D with 1 or 2 elements.
- A single value(int or tuple/list(int)) means [size, size].
- The 2nd argument for initialization is padding(Optional-Default:None-Type:int or tuple/list(int)):
*Memos:
- It's [left, top, right, bottom] which can be converted from [left-right, top-bottom] or [left-top-right-bottom].
- A tuple/list must be the 1D with 1, 2 or 4 elements.
- A single value(int or tuple/list(int)) means [padding, padding, padding, padding].
- Double values(tuple/list(int)) means [padding[0], padding[1], padding[0], padding[1]].
- The 3rd argument for initialization is pad_if_needed(Optional-Default:False-Type:bool):
- If it's False and size is smaller than an original image or the padded image by padding, there is error.
- If it's True and size is smaller than an original image or the padded image by padding, there is no error, then the image is randomly padded to become size.
- The 4th argument for initialization is fill(Optional-Default:0-Type:int, float or tuple/list(int or float)):
*Memos:
- It can change the background of an image. *The background can be seen when an image is positively padded.
- A tuple/list must be the 1D with 1 or 3 elements.
- The 5th argument for initialization is padding_mode(Optional-Default:'constant'-Type:str). *'constant', 'edge', 'reflect' or 'symmetric' can be set to it.
- The 1st argument is img(Required-Type:PIL Image or tensor(int)):
*Memos:
- A tensor must be 2D or 3D.
- Don't use img=.
- v2 is recommended to use according to V1 or V2? Which one should I use?.
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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