import gradio as gr import cv2 import gradio as gr import os from PIL import Image import numpy as np import torch from torch.autograd import Variable from torchvision import transforms import torch.nn.functional as F import matplotlib.pyplot as plt import warnings warnings.filterwarnings("ignore") # project imports from data_loader_cache import normalize, im_reader, im_preprocess from models import * #Helpers device = 'cuda' if torch.cuda.is_available() else 'cpu' class GOSNormalize(object): ''' Normalize the Image using torch.transforms ''' def __init__(self, mean=[0.485,0.456,0.406], std=[0.229,0.224,0.225]): self.mean = mean self.std = std def __call__(self,image): image = normalize(image,self.mean,self.std) return image transform = transforms.Compose([GOSNormalize([0.5,0.5,0.5],[1.0,1.0,1.0])]) def load_image(im_path, hypar): im = im_reader(im_path) im, im_shp = im_preprocess(im, hypar["cache_size"]) im = torch.divide(im,255.0) shape = torch.from_numpy(np.array(im_shp)) return transform(im).unsqueeze(0), shape.unsqueeze(0) # make a batch of image, shape def build_model(hypar,device): net = hypar["model"]#GOSNETINC(3,1) # convert to half precision if(hypar["model_digit"]=="half"): net.half() for layer in net.modules(): if isinstance(layer, nn.BatchNorm2d): layer.float() net.to(device) if(hypar["restore_model"]!=""): net.load_state_dict(torch.load(hypar["model_path"]+"/"+hypar["restore_model"], map_location=device)) net.to(device) net.eval() return net def predict(net, inputs_val, shapes_val, hypar, device): ''' Given an Image, predict the mask ''' net.eval() if(hypar["model_digit"]=="full"): inputs_val = inputs_val.type(torch.FloatTensor) else: inputs_val = inputs_val.type(torch.HalfTensor) inputs_val_v = Variable(inputs_val, requires_grad=False).to(device) # wrap inputs in Variable ds_val = net(inputs_val_v)[0] # list of 6 results pred_val = ds_val[0][0,:,:,:] # B x 1 x H x W # we want the first one which is the most accurate prediction ## recover the prediction spatial size to the orignal image size pred_val = torch.squeeze(F.upsample(torch.unsqueeze(pred_val,0),(shapes_val[0][0],shapes_val[0][1]),mode='bilinear')) ma = torch.max(pred_val) mi = torch.min(pred_val) pred_val = (pred_val-mi)/(ma-mi) # max = 1 if device == 'cuda': torch.cuda.empty_cache() return (pred_val.detach().cpu().numpy()*255).astype(np.uint8) # it is the mask we need # Set Parameters hypar = {} # paramters for inferencing hypar["model_path"] ="./saved_models" ## load trained weights from this path hypar["restore_model"] = "isnet.pth" ## name of the to-be-loaded weights hypar["interm_sup"] = False ## indicate if activate intermediate feature supervision ## choose floating point accuracy -- hypar["model_digit"] = "full" ## indicates "half" or "full" accuracy of float number hypar["seed"] = 0 hypar["cache_size"] = [1024, 1024] ## cached input spatial resolution, can be configured into different size ## data augmentation parameters --- hypar["input_size"] = [1024, 1024] ## mdoel input spatial size, usually use the same value hypar["cache_size"], which means we don't further resize the images hypar["crop_size"] = [1024, 1024] ## random crop size from the input, it is usually set as smaller than hypar["cache_size"], e.g., [920,920] for data augmentation hypar["model"] = ISNetDIS() # Build Model net = build_model(hypar, device) def inference(image: Image): image_path = image image_tensor, orig_size = load_image(image_path, hypar) mask = predict(net, image_tensor, orig_size, hypar, device) pil_mask = Image.fromarray(mask).convert('L') im_rgb = Image.open(image).convert("RGB") im_rgba = im_rgb.copy() im_rgba.putalpha(pil_mask) return im_rgba def bw(image_file:Image): img = Image.open(image_file) img = img.convert("L") return img iface = gr.Interface(fn=inference, inputs=gr.Image(type='filepath'), outputs=["image"], title="Remove Background", description="Uses DIS to remove background" ) iface.launch()