Upload dis.py

dis.py

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+# -*- coding: utf-8 -*-
+"""DIS.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1MI9utM7GJbz0w_zw1GJNU-ay15SzZHIN
+
+# Clone official repo
+"""
+
+# Commented out IPython magic to ensure Python compatibility.
+! git clone https://github.com/xuebinqin/DIS
+
+# %cd ./DIS/IS-Net
+
+!pip install gdown
+
+!mkdir ./saved_models
+
+"""# Imports"""
+
+import numpy as np
+from PIL import Image
+import torch
+from torch.autograd import Variable
+from torchvision import transforms
+import torch.nn.functional as F
+import gdown
+import os
+
+import requests
+import matplotlib.pyplot as plt
+from io import BytesIO
+
+# project imports
+from data_loader_cache import normalize, im_reader, im_preprocess
+from models import *
+
+"""# Helpers"""
+
+drive_link = "https://drive.google.com/uc?id=1XHIzgTzY5BQHw140EDIgwIb53K659ENH"
+
+# Specify the local path and filename
+local_path = "/content/DIS/IS-Net/saved_models/isnet.pth"
+
+# Download the file
+gdown.download(drive_link, local_path, quiet=False)
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+# Download official weights
+
+
+
+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):
+    if im_path.startswith("http"):
+        im_path = BytesIO(requests.get(im_path).content)
+
+    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)
+
+"""# Predict Mask"""
+
+gsheetid = "1n9kk7IHyBzkw5e08wpjjt-Ry5aE_thqGrJ97rMeN-K4"
+sheet_name = "sarvm"
+
+gsheet_url = "https://docs.google.com/spreadsheets/d/{}/gviz/tq?tqx=out:csv&sheet={}".format(gsheetid, sheet_name)
+
+gsheet_url
+
+import pandas as pd
+df = pd.read_csv(gsheet_url)
+
+image_path = df.iloc[-1]['Image']
+
+drive_link = image_path
+
+# Specify the local path and filename
+local_path = "/content/DIS/IS-Net/saved_models/input2.jpg"
+
+# Download the file
+gdown.download(drive_link, local_path, quiet=False)
+
+from google.colab.patches import cv2_imshow
+from PIL import Image
+image_path = "/content/DIS/IS-Net/saved_models/input2.jpg"
+# image_bytes = BytesIO(requests.get(image_path).content)
+# print(image_bytes)
+image_tensor, orig_size = load_image(image_path, hypar)
+mask = predict(net,image_tensor,orig_size, hypar, device)
+image = Image.open(image_path)
+
+f, ax = plt.subplots(1,2, figsize = (35,20))
+
+# ax[0].imshow(np.array(Image.open(image_bytes))) # Original image
+# cv2_imshow(image_path)
+
+ax[0].imshow(mask, cmap = 'gray') # retouched image
+
+# ax[0].set_title("Original Image")
+ax[0].set_title("Mask")
+
+plt.show()
+
+import cv2
+image = cv2.imread(image_path)
+h, w , _ = image.shape
+# print(h)
+# print(w)
+# print(_)
+# print(image)
+h, w , _ = image.shape
+# print(h)
+# print(w)
+# print(_)
+# new_image = np.zeros_like(image)
+# new_image[mask] = image[mask]
+new_image = cv2.bitwise_and(image, image, mask=mask)
+transparent_bg = np.zeros((new_image.shape[0],new_image.shape[1], new_image.shape[2]+1) , dtype=np.uint8)
+
+# Apply the mask to the transparent background
+transparent_bg[:, :, :3] = new_image
+
+# Set the alpha channel using the mask
+transparent_bg[:, :, 3] = mask
+
+# Save the new image with a transparent background
+output_path = "/content/output.png"
+cv2.imwrite(output_path, transparent_bg)
+# Save the new image
+# output_path = "/content/output.jpg"
+# cv2.imwrite(output_path, new_image)
+