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Update app.py
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app.py
CHANGED
@@ -8,6 +8,8 @@ import requests
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from io import BytesIO
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from PIL import Image, ImageFilter, ImageEnhance
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import rembg
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import onnxruntime as ort
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app = FastAPI()
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@@ -16,14 +18,16 @@ app = FastAPI()
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options = ort.SessionOptions()
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options.intra_op_num_threads = 2
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def resize_image(image, max_size=1024):
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width, height = image.size
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if width > max_size or height > max_size:
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ratio = min(max_size / width, max_size / height)
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new_size = (int(width * ratio), int(height * ratio))
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image = image.resize(new_size, Image.Resampling.LANCZOS) # Usando LANCZOS
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return image
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def adjust_brightness_contrast(image, brightness=1.2, contrast=1.2):
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enhancer = ImageEnhance.Brightness(image)
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image = enhancer.enhance(brightness)
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@@ -31,6 +35,29 @@ def adjust_brightness_contrast(image, brightness=1.2, contrast=1.2):
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image = enhancer.enhance(contrast)
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return image
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@app.get("/remove-background")
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async def remove_background(image_url: str):
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try:
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@@ -46,7 +73,10 @@ async def remove_background(image_url: str):
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image = adjust_brightness_contrast(image)
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# Remove o fundo da imagem usando rembg
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output = rembg.remove(image, model="
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# Pós-processamento: suaviza as bordas
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output = output.filter(ImageFilter.SMOOTH_MORE)
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from io import BytesIO
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from PIL import Image, ImageFilter, ImageEnhance
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import rembg
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import cv2
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import numpy as np
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import onnxruntime as ort
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app = FastAPI()
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options = ort.SessionOptions()
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options.intra_op_num_threads = 2
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# Função para redimensionar a imagem
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def resize_image(image, max_size=1024):
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width, height = image.size
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if width > max_size or height > max_size:
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ratio = min(max_size / width, max_size / height)
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new_size = (int(width * ratio), int(height * ratio))
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image = image.resize(new_size, Image.Resampling.LANCZOS) # Usando LANCZOS para redimensionamento
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return image
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# Função para ajustar brilho e contraste
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def adjust_brightness_contrast(image, brightness=1.2, contrast=1.2):
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enhancer = ImageEnhance.Brightness(image)
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image = enhancer.enhance(brightness)
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image = enhancer.enhance(contrast)
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return image
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# Função para remover sombras
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def remove_shadows(image):
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# Converte a imagem para um array numpy (compatível com OpenCV)
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image_np = np.array(image)
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# Converte a imagem para escala de cinza
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gray = cv2.cvtColor(image_np, cv2.COLOR_RGBA2GRAY)
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# Aplica limiarização para remover sombras
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_, mask = cv2.threshold(gray, 240, 255, cv2.THRESH_BINARY)
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# Aplica operações morfológicas para melhorar a máscara
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kernel = np.ones((3, 3), np.uint8)
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mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel) # Remove ruídos
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mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel) # Fecha buracos
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# Aplica a máscara na imagem original
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result = cv2.bitwise_and(image_np, image_np, mask=mask)
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# Converte o resultado de volta para uma imagem PIL
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return Image.fromarray(result)
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# Endpoint da API
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@app.get("/remove-background")
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async def remove_background(image_url: str):
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try:
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image = adjust_brightness_contrast(image)
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# Remove o fundo da imagem usando rembg
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output = rembg.remove(image, model="u2net", session_options=options)
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# Remove sombras
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output = remove_shadows(output)
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# Pós-processamento: suaviza as bordas
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output = output.filter(ImageFilter.SMOOTH_MORE)
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