Spaces:
Runtime error
Runtime error
File size: 4,337 Bytes
cdfe05c 53178f3 cdfe05c fe9959f cdfe05c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 |
# %%
"""
# The following cell connects Kaggle to the internet for successful running of the code
"""
"""
# Including all necessary packages and libraries
"""
# %%
from fastai.vision.all import *
import gradio as gr
import subprocess
subprocess.call(['pip', 'install', 'duckduckgo_search'])
from duckduckgo_search import ddg_images
from fastcore.all import *
from fastdownload import download_url
# %%
"""
# This cell consists of the function that gets image from the internet using DuckDuckGo library, which imports images from the internet
"""
# %%
def search_images(term, max_images=30):
print(f"Searching for '{term}'")
return L(ddg_images(term, max_results=max_images)).itemgot('image')
# %%
"""
# This cell displays the URL thats been searched and provided of the image searched
"""
# %%
#NB: `search_images` depends on duckduckgo.com, which doesn't always return correct responses.
# If you get a JSON error, just try running it again (it may take a couple of tries).
urls = search_images('black person', max_images=1)
urls[0]
# %%
"""
# This cell downloads and displays the image thats been searched
"""
# %%
dest = 'black-person.jpg'
download_url(urls[0], dest, show_progress=False)
from fastai.vision.all import *
im = Image.open(dest)
im.to_thumb(192,192)
# %%
"""
# This cell downloads the second image thats been searched for classifying the two images respectively
"""
# %%
download_url(search_images('white person', max_images=1)[0], 'white-person.jpg', show_progress=False)
Image.open('white-person.jpg').to_thumb(192,192)
# %%
"""
# This cell searches for a random image for testing purposes
"""
# %%
download_url(search_images('random', max_images=1)[0], 'dunno.jpg', show_progress=False)
Image.open('dunno.jpg').to_thumb(192,192)
# %%
"""
# This cell searches different images with the subject being the same in different scenarios, to train the model better and for better results
"""
# %%
searches = 'white person','black person'
path = Path('black_or_not')
from time import sleep
for o in searches:
dest = (path/o)
dest.mkdir(exist_ok=True, parents=True)
download_images(dest, urls=search_images(f'{o} photo'))
sleep(10) # Pause between searches to avoid over-loading server
download_images(dest, urls=search_images(f'{o} photo in sun'))
sleep(10)
download_images(dest, urls=search_images(f'{o} photo in shade'))
sleep(10)
resize_images(path/o, max_size=400, dest=path/o)
# %%
"""
# This cell removes the error images that may corrupt the found dataset
"""
# %%
failed = verify_images(get_image_files(path))
failed.map(Path.unlink)
len(failed)
# %%
"""
# This block loads the images from the dataset acquired and further classifies it into their categories respectively
"""
# %%
dls = DataBlock(
blocks=(ImageBlock, CategoryBlock),
get_items=get_image_files,
splitter=RandomSplitter(valid_pct=0.2, seed=42),
get_y=parent_label,
item_tfms=[Resize(192, method='squish')]
).dataloaders(path, bs=32)
dls.show_batch(max_n=6)
# %%
"""
# This cell trains on the photos taken from the internet and calculates the metrics of the model and the dataset its being trained on
"""
# %%
learn = vision_learner(dls, resnet18, metrics=error_rate)
learn.fine_tune(7)
# %%
"""
# This cell checks how well the model will run if its provided with any other input related to the image classication database
"""
# %%
is_black,_,probs = learn.predict(PILImage.create('black-person.jpg'))
print(f"This is a: {is_black}.")
print(f"Probability it's black person: {probs[0]:.4f}")
# %%
"""
# This cell exports the trained dataset
"""
# %%
learn.export('racialModel.pkl')
# %%
learn = load_learner('racialModel.pkl')
# %%
download_url(search_images('african male', max_images=1)[0], 'test.jpg', show_progress=False)
Image.open('test.jpg').to_thumb(192,192)
# %%
learn.predict('test.jpg')
# %%
categories = ('Black Person', 'White Person')
def classify_img(img):
pred,pred_idx,probs = learn.predict(img)
return dict(zip(categories, map(float,probs)))
# %%
classify_img('test.jpg')
# %%
image = gr.inputs.Image(shape=(192,192))
label = gr.outputs.Label()
examples = ['white-person.jpg','black-person.jpg', 'dunno.jpg']
intf = gr.Interface(fn=classify_img, inputs=image, outputs=label, examples=examples)
intf.launch(inline=False) |