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import gradio as gr
import torch
from PIL import Image
from gtts import gTTS
import numpy as np
import cv2
from transformers import BlipProcessor, BlipForConditionalGeneration, MarianMTModel, MarianTokenizer
# Carregar o modelo YOLOv5
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
# Função para calcular a GLCM e o contraste manualmente
def calculate_glcm_contrast(image):
gray_image = cv2.cvtColor(np.array(image), cv2.COLOR_BGR2GRAY)
max_value = gray_image.max() + 1
glcm = np.zeros((max_value, max_value), dtype=np.float64)
for i in range(gray_image.shape[0] - 1):
for j in range(gray_image.shape[1] - 1):
x = gray_image[i, j]
y = gray_image[i + 1, j + 1]
glcm[x, y] += 1
glcm = glcm / glcm.sum()
contrast = 0.0
for i in range(max_value):
for j in range(max_value):
contrast += (i - j) ** 2 * glcm[i, j]
return contrast
# Função para descrever imagem usando BLIP
def describe_image(image):
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
inputs = processor(image, return_tensors="pt")
out = model.generate(**inputs)
description = processor.decode(out[0], skip_special_tokens=True)
return description
# Função para traduzir descrição para português
def translate_description(description):
model_name = 'Helsinki-NLP/opus-mt-en-pt'
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(description, return_tensors="pt", padding=True))
translated_text = tokenizer.decode(translated[0], skip_special_tokens=True)
return translated_text
# Função principal para processar imagem e gerar saída de voz
def process_image(image):
# Detecção de objetos
results = model(image)
detected_image = results.render()[0]
# Análise de cor (média RGB)
mean_rgb = np.mean(np.array(image), axis=(0, 1))
# Análise de textura
texture_contrast = calculate_glcm_contrast(image)
# Descrição da imagem
description = describe_image(image)
translated_description = translate_description(description)
# Texto para voz
tts = gTTS(text=translated_description, lang='pt')
tts.save("output.mp3")
# Retornar imagem com detecções, descrição e áudio
return Image.fromarray(detected_image), translated_description, "output.mp3"
# Carregar imagem de exemplo diretamente do código
example_image_path = "example1.JPG"
# Interface Gradioo
iface = gr.Interface(
fn=process_image,
inputs=gr.Image(type="pil"),
outputs=[gr.Image(type="pil"), gr.Textbox(), gr.Audio(type="filepath")],
examples=[example_image_path]
)
iface.launch()
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