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Create helper.py
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helper.py
ADDED
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import io
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import matplotlib.pyplot as plt
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import requests
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import inflect
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from PIL import Image
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def load_image_from_url(url):
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return Image.open(requests.get(url, stream=True).raw)
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def render_results_in_image(in_pil_img, in_results):
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plt.figure(figsize=(16, 10))
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plt.imshow(in_pil_img)
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ax = plt.gca()
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for prediction in in_results:
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x, y = prediction['box']['xmin'], prediction['box']['ymin']
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w = prediction['box']['xmax'] - prediction['box']['xmin']
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h = prediction['box']['ymax'] - prediction['box']['ymin']
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ax.add_patch(plt.Rectangle((x, y),
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w,
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h,
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fill=False,
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color="green",
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linewidth=2))
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ax.text(
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x,
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y,
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f"{prediction['label']}: {round(prediction['score']*100, 1)}%",
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color='red'
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)
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plt.axis("off")
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# Save the modified image to a BytesIO object
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img_buf = io.BytesIO()
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plt.savefig(img_buf, format='png',
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bbox_inches='tight',
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pad_inches=0)
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img_buf.seek(0)
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modified_image = Image.open(img_buf)
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# Close the plot to prevent it from being displayed
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plt.close()
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return modified_image
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def summarize_predictions_natural_language(predictions):
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summary = {}
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p = inflect.engine()
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for prediction in predictions:
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label = prediction['label']
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if label in summary:
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summary[label] += 1
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else:
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summary[label] = 1
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result_string = "In this image, there are "
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for i, (label, count) in enumerate(summary.items()):
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count_string = p.number_to_words(count)
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result_string += f"{count_string} {label}"
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if count > 1:
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result_string += "s"
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result_string += " "
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if i == len(summary) - 2:
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result_string += "and "
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# Remove the trailing comma and space
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result_string = result_string.rstrip(', ') + "."
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return result_string
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##### To ignore warnings #####
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import warnings
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import logging
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from transformers import logging as hf_logging
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def ignore_warnings():
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# Ignore specific Python warnings
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warnings.filterwarnings("ignore", message="Some weights of the model checkpoint")
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warnings.filterwarnings("ignore", message="Could not find image processor class")
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warnings.filterwarnings("ignore", message="The `max_size` parameter is deprecated")
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# Adjust logging for libraries using the logging module
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logging.basicConfig(level=logging.ERROR)
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hf_logging.set_verbosity_error()
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########
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