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from duckduckgo_search import ddg_images
from fastai.vision.all import download_images, resize_images, verify_images, get_image_files, ImageBlock, \
CategoryBlock, RandomSplitter, parent_label, ResizeMethod, Resize, vision_learner, resnet18, error_rate, \
L, Path, DataBlock
def search_images(search_term, max_images=30):
print(f"Searching for '{search_term}'")
return L(ddg_images(search_term, max_results=max_images)).itemgot('image')
def search_and_populate(search_term, category, file_path, max_images=30):
dest = (file_path/category)
dest.mkdir(exist_ok=True, parents=True)
download_images(dest, urls=search_images(f'{search_term} photo', max_images=max_images))
resize_images(file_path/category, max_size=400, dest=file_path/category)
path = Path('seefood')
search_and_populate("hotdog", "hotdog", path, max_images=90)
for o in ['burger', 'sandwich', 'fruit', 'chips', 'salad']:
search_and_populate(o, "not_hotdog", path, max_images=30)
failed = verify_images(get_image_files(path))
failed.map(Path.unlink)
print(f"{len(failed)} failed images")
dls = DataBlock(
blocks=(ImageBlock, CategoryBlock),
get_items=get_image_files,
splitter=RandomSplitter(valid_pct=0.2),
get_y=parent_label,
item_tfms=[Resize(256, ResizeMethod.Squish)]
).dataloaders(path, bs=32)
learn = vision_learner(dls, resnet18, metrics=error_rate)
learn.fine_tune(3)
learn.export("hotdogModel.pkl")
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