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| import io | |
| import matplotlib.pyplot as plt | |
| import requests | |
| import inflect | |
| from PIL import Image | |
| def load_image_from_url(url): | |
| return Image.open(requests.get(url, stream=True).raw) | |
| def render_results_in_image(in_pil_img, in_results): | |
| plt.figure(figsize=(16, 10)) | |
| plt.imshow(in_pil_img) | |
| ax = plt.gca() | |
| for prediction in in_results: | |
| x, y = prediction['box']['xmin'], prediction['box']['ymin'] | |
| w = prediction['box']['xmax'] - prediction['box']['xmin'] | |
| h = prediction['box']['ymax'] - prediction['box']['ymin'] | |
| ax.add_patch(plt.Rectangle((x, y), | |
| w, | |
| h, | |
| fill=False, | |
| color="green", | |
| linewidth=2)) | |
| ax.text( | |
| x, | |
| y, | |
| f"{prediction['label']}: {round(prediction['score']*100, 1)}%", | |
| color='red' | |
| ) | |
| plt.axis("off") | |
| # Save the modified image to a BytesIO object | |
| img_buf = io.BytesIO() | |
| plt.savefig(img_buf, format='png', | |
| bbox_inches='tight', | |
| pad_inches=0) | |
| img_buf.seek(0) | |
| modified_image = Image.open(img_buf) | |
| # Close the plot to prevent it from being displayed | |
| plt.close() | |
| return modified_image | |
| def summarize_predictions_natural_language(predictions): | |
| summary = {} | |
| p = inflect.engine() | |
| for prediction in predictions: | |
| label = prediction['label'] | |
| if label in summary: | |
| summary[label] += 1 | |
| else: | |
| summary[label] = 1 | |
| result_string = "In this image, there are " | |
| for i, (label, count) in enumerate(summary.items()): | |
| count_string = p.number_to_words(count) | |
| result_string += f"{count_string} {label}" | |
| if count > 1: | |
| result_string += "s" | |
| result_string += " " | |
| if i == len(summary) - 2: | |
| result_string += "and " | |
| # Remove the trailing comma and space | |
| result_string = result_string.rstrip(', ') + "." | |
| return result_string | |
| ##### To ignore warnings ##### | |
| import warnings | |
| import logging | |
| from transformers import logging as hf_logging | |
| def ignore_warnings(): | |
| # Ignore specific Python warnings | |
| warnings.filterwarnings("ignore", message="Some weights of the model checkpoint") | |
| warnings.filterwarnings("ignore", message="Could not find image processor class") | |
| warnings.filterwarnings("ignore", message="The `max_size` parameter is deprecated") | |
| # Adjust logging for libraries using the logging module | |
| logging.basicConfig(level=logging.ERROR) | |
| hf_logging.set_verbosity_error() | |
| ######## | |
| from transformers import pipeline | |
| from PIL import Image | |
| import gradio as gr | |
| import numpy as np | |
| import io | |
| def processed_image(image): | |
| # The uploaded image is a PIL image | |
| od_pipe= pipeline("object-detection", model="facebook/detr-resnet-50") | |
| pl_out = od_pipe(image) | |
| processed_image=render_results_in_image(image,pl_out) | |
| text=summarize_predictions_natural_language(pl_out) | |
| return processed_image,text | |
| iface = gr.Interface(processed_image, # Function to process the image | |
| inputs=gr.Image(type="pil"), # Image upload input | |
| outputs=[gr.Image(type="pil"),"text"] # Image output | |
| ) | |
| iface.launch() | |
| tts_pipe = pipeline("text-to-speech", model="kakao-enterprise/vits-vctk") | |
| narrated_text=tts_pipe(text) |