Create app1.py

app1.py

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+# -*- coding: utf-8 -*-
+
+import sys
+import io
+import requests
+import json
+import base64
+from PIL import Image
+import numpy as np
+import gradio as gr
+import mmengine
+from mmengine import Config, get
+
+import argparse
+import os
+import cv2
+import yaml
+import torch
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+import datasets
+import models
+import numpy as np
+
+from torchvision import transforms
+from mmcv.runner import load_checkpoint
+import visual_utils
+from PIL import Image
+from models.utils_prompt import get_prompt_inp, pre_prompt, pre_scatter_prompt, get_prompt_inp_scatter
+
+device = torch.device("cpu")
+
+def batched_predict(model, inp, coord, bsize):
+    with torch.no_grad():
+        model.gen_feat(inp)
+        n = coord.shape[1]
+        ql = 0
+        preds = []
+        while ql < n:
+            qr = min(ql + bsize, n)
+            pred = model.query_rgb(coord[:, ql: qr, :])
+            preds.append(pred)
+            ql = qr
+        pred = torch.cat(preds, dim=1)
+    return pred, preds
+
+
+def tensor2PIL(tensor):
+    toPIL = transforms.ToPILImage()
+    return toPIL(tensor)
+
+
+def Decoder1_optical_instance(image_input):
+    with open('configs/fine_tuning_one_decoder.yaml', 'r') as f:
+        config = yaml.load(f, Loader=yaml.FullLoader)
+    model = models.make(config['model']).cpu()
+    sam_checkpoint = torch.load("./save/model_epoch_last.pth", map_location='cpu')
+    model.load_state_dict(sam_checkpoint, strict=False)
+    model.eval()
+
+    # img = np.array(image_input).copy()
+    label2color = visual_utils.Label2Color(cmap=visual_utils.color_map('Unify_double'))
+    # image_input.save(f'./save/visual_fair1m/input_img.png', quality=5)
+    img = transforms.Resize([1024, 1024])(image_input)
+    transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229,0.224,0.225])])
+    input_img = transform(img)
+    input_img = input_img.unsqueeze(0)
+    image_embedding = model.image_encoder(input_img) # torch.Size([1, 256, 64, 64])
+    sparse_embeddings, dense_embeddings, scatter_embeddings  = model.prompt_encoder(
+        points=None,
+        boxes=None,
+        masks=None,
+    scatter=None)
+    # 目标类预测decoder
+    low_res_masks, iou_predictions = model.mask_decoder(
+        image_embeddings=image_embedding,
+        image_pe=model.prompt_encoder.get_dense_pe(),
+        sparse_prompt_embeddings=sparse_embeddings,
+        dense_prompt_embeddings=dense_embeddings,
+        multimask_output=False
+    )
+    pred = model.postprocess_masks(low_res_masks, model.inp_size, model.inp_size)
+    _, prediction = pred.max(dim=1)
+    prediction_to_save = label2color(prediction.cpu().numpy().astype(np.uint8))[0]
+
+    return prediction_to_save
+
+
+def Decoder1_optical_terrain(image_input):
+    with open('configs/fine_tuning_one_decoder.yaml', 'r') as f:
+        config = yaml.load(f, Loader=yaml.FullLoader)
+    model = models.make(config['model']).cpu()
+    sam_checkpoint = torch.load("./save/model_epoch_last.pth", map_location='cpu')
+    model.load_state_dict(sam_checkpoint, strict=False)
+    model.eval()
+
+    denorm = visual_utils.Denormalize(mean=[0.485, 0.456, 0.406],std=[0.229,0.224,0.225])
+    label2color = visual_utils.Label2Color(cmap=visual_utils.color_map('Unify_Vai'))
+    # image_input.save(f'./save/visual_fair1m/input_img.png', quality=5)
+    img = transforms.Resize([1024, 1024])(image_input)
+    transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229,0.224,0.225])])
+    input_img = transform(img)
+    input_img = torch.unsqueeze(input_img, dim=0)
+    # input_img = transforms.ToTensor()(img).unsqueeze(0)
+    image_embedding = model.image_encoder(input_img) # torch.Size([1, 256, 64, 64])
+    sparse_embeddings, dense_embeddings, scatter_embeddings = model.prompt_encoder(
+        points=None,
+        boxes=None,
+        masks=None,
+        scatter=None)
+    low_res_masks_instanse, iou_predictions = model.mask_decoder(
+        image_embeddings=image_embedding,
+        # image_embeddings=image_embedding.unsqueeze(0),
+        image_pe=model.prompt_encoder.get_dense_pe(),
+        sparse_prompt_embeddings=sparse_embeddings,
+        dense_prompt_embeddings=dense_embeddings,
+        # multimask_output=multimask_output,
+        multimask_output=False
+    )
+    # 地物类预测decoder
+    low_res_masks, iou_predictions_2 = model.mask_decoder_diwu(
+        image_embeddings=image_embedding,
+        image_pe=model.prompt_encoder.get_dense_pe(),
+        sparse_prompt_embeddings=sparse_embeddings,
+        dense_prompt_embeddings=dense_embeddings,
+        # multimask_output=False,
+        multimask_output=True,
+    )  # B*C+1*H*W
+
+    pred_instance = model.postprocess_masks(low_res_masks_instanse, model.inp_size, model.inp_size)
+    pred = model.postprocess_masks(low_res_masks, model.inp_size, model.inp_size)
+    pred = torch.softmax(pred,dim=1)
+    pred_instance = torch.softmax(pred_instance,dim=1)
+    _, prediction = pred.max(dim=1)
+    prediction[prediction==12]=0  #把第二个decoder里得背景变成0
+    print(torch.unique(prediction))
+    _, prediction_instance = pred_instance.max(dim=1)
+    print(torch.unique(prediction_instance))
+    prediction_sum = prediction + prediction_instance #没有冲突的位置就会正常猜测
+    print(torch.unique(prediction_sum))
+    prediction_tmp = prediction_sum.clone()
+    prediction_tmp[prediction_tmp==1] = 255
+    prediction_tmp[prediction_tmp==2] = 255
+    prediction_tmp[prediction_tmp==5] = 255
+    prediction_tmp[prediction_tmp==6] = 255
+    prediction_tmp[prediction_tmp==14] = 255
+    # prediction_tmp[prediction_tmp==0] = 255 #同时是背景
+    # index = prediction_tmp != 255
+    pred[:, 0][prediction_tmp == 255]=100 #把已经决定的像素位置的背景预测概率设置为最大
+    pred_instance[:, 0][prediction_tmp == 255]=100#把已经决定的像素位置的背景预测概率设置为最大
+    buchong = torch.zeros([1,2,1024,1024])
+    pred = torch.cat((pred, buchong),dim=1)
+    # print(torch.unique(torch.argmax(pred,dim=1)))
+    # Decoder1_logits = torch.zeros([1,15,1024,1024]).cuda()
+    Decoder2_logits = torch.zeros([1,15,1024,1024])
+    Decoder2_logits[:,0,...] = pred[:,0,...]
+    Decoder2_logits[:,5,...] = pred_instance[:,5,...]
+    Decoder2_logits[:,14,...] = pred_instance[:,14,...]
+    Decoder2_logits[:,1,...] = pred[:,1,...]
+    Decoder2_logits[:,2,...] = pred[:,2,...]
+    Decoder2_logits[:,6,...] = pred[:,6,...]
+    # Decoder_logits = Decoder1_logits+Decoder2_logits
+    pred_chongtu = torch.argmax(Decoder2_logits, dim=1)
+    # pred_pred = torch.argmax(Decoder1_logits, dim=1)
+    pred_predinstance = torch.argmax(Decoder2_logits, dim=1)
+    print(torch.unique(pred_chongtu))
+    pred_chongtu[prediction_tmp == 255] = 0
+    prediction_sum[prediction_tmp!=255] = 0
+    prediction_final = (pred_chongtu + prediction_sum).cpu().numpy()
+    prediction_to_save = label2color(prediction_final)[0]
+    
+    return prediction_to_save 
+
+
+def Multi_box_prompts(input_prompt):
+    with open('configs/fine_tuning_one_decoder.yaml', 'r') as f:
+        config = yaml.load(f, Loader=yaml.FullLoader)
+    model = models.make(config['model']).cpu()
+    sam_checkpoint = torch.load("./save/model_epoch_last.pth", map_location='cpu')
+    model.load_state_dict(sam_checkpoint, strict=False)
+    model.eval()
+
+    
+    label2color = visual_utils.Label2Color(cmap=visual_utils.color_map('Unify_double'))
+    # image_input.save(f'./save/visual_fair1m/input_img.png', quality=5)
+    img = transforms.Resize([1024, 1024])(input_prompt["image"])
+    input_img = transforms.ToTensor()(img).unsqueeze(0)
+    image_embedding = model.image_encoder(input_img) # torch.Size([1, 256, 64, 64])
+    sparse_embeddings, dense_embeddings, scatter_embeddings  = model.prompt_encoder(
+        points=None,
+        boxes=None,
+        masks=None,
+    scatter=None)
+    # 目标类预测decoder
+    low_res_masks, iou_predictions = model.mask_decoder(
+        image_embeddings=image_embedding,
+        image_pe=model.prompt_encoder.get_dense_pe(),
+        sparse_prompt_embeddings=sparse_embeddings,
+        dense_prompt_embeddings=dense_embeddings,
+        multimask_output=False
+    )
+    pred = model.postprocess_masks(low_res_masks, model.inp_size, model.inp_size)
+    _, prediction = pred.max(dim=1)
+    prediction_to_save = label2color(prediction.cpu().numpy().astype(np.uint8))[0]
+
+    def find_instance(image_map):
+        BACKGROUND = 0
+        steps = [[1, 0], [0, 1], [-1, 0], [0, -1], [1, 1], [1, -1], [-1, 1], [-1, -1]]
+        instances = []
+
+        def bfs(x, y, category_id):
+            nonlocal image_map, steps
+            instance = {(x, y)}
+            q = [(x, y)]
+            image_map[x, y] = BACKGROUND
+            while len(q) > 0:
+                x, y = q.pop(0)
+                # print(x, y, image_map[x][y])
+                for step in steps:
+                    xx = step[0] + x
+                    yy = step[1] + y
+                    if 0 <= xx < len(image_map) and 0 <= yy < len(image_map[0]) \
+                            and image_map[xx][yy] == category_id:  # and (xx, yy) not in q:
+                        q.append((xx, yy))
+                        instance.add((xx, yy))
+                        image_map[xx, yy] = BACKGROUND
+            return instance
+        image_map = image_map[:]
+        for i in range(len(image_map)):
+            for j in range(len(image_map[i])):
+                category_id = image_map[i][j]
+                if category_id == BACKGROUND:
+                    continue
+                instances.append(bfs(i, j, category_id))
+        return instances
+
+    prompts = find_instance(np.uint8(np.array(input_prompt["mask"]).sum(-1) != 0))
+    img_mask = np.array(img).copy()
+    
+    def get_box(prompt):
+        xs = []
+        ys = []
+        for x, y in prompt:
+            xs.append(x)
+            ys.append(y)
+        return [[min(xs), min(ys)], [max(xs), max(ys)]]
+
+    def in_box(point, box):
+        left_up, right_down = box
+        x, y = point
+        return x >= left_up[0] and x <= right_down[0] and y >= left_up[1] and y <= right_down[1]
+        
+    def draw_box(box_outer, img, radius=4):
+        radius -= 1
+        left_up_outer, right_down_outer = box_outer
+        box_inner = [list(np.array(left_up_outer) + radius),
+                                           list(np.array(right_down_outer) - radius)]
+        for x in range(len(img)):
+            for y in range(len(img[x])):
+                if in_box([x, y], box_outer):
+                    img_mask[x, y] = (1, 1, 1)
+                if in_box([x, y], box_outer) and (not in_box([x, y], box_inner)):
+                    img[x, y] = (255, 0, 0)
+        return img
+        
+    for prompt in prompts:
+        box = get_box(prompt)
+        output = draw_box(box, prediction_to_save) * (img_mask==1)
+    
+    return output
+
+
+
+def Decoder2_SAR(SAR_image, SAR_prompt):
+    with open('configs/multi_mo_multi_task_sar_prompt.yaml', 'r') as f:
+        config = yaml.load(f, Loader=yaml.FullLoader)
+    model = models.make(config['model']).cpu()
+    sam_checkpoint = torch.load("./save/SAR/model_epoch_last.pth", map_location='cpu')
+    model.load_state_dict(sam_checkpoint, strict=True)
+    model.eval()
+
+    denorm = visual_utils.Denormalize(mean=[0.485, 0.456, 0.406],std=[0.229,0.224,0.225])
+    label2color = visual_utils.Label2Color(cmap=visual_utils.color_map('Unify_YIJISAR'))
+
+    img = transforms.Resize([1024, 1024])(SAR_image)
+    transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229,0.224,0.225])])
+    input_img = transform(img)
+    input_img = torch.unsqueeze(input_img, dim=0)
+    # input_img = transforms.ToTensor()(img).unsqueeze(0)
+    # input_img = transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229,0.224,0.225])
+    filp_flag = torch.Tensor([False])
+    image_embedding = model.image_encoder(input_img)
+    
+    # scattter_prompt = cv2.imread(scatter_file_, cv2.IMREAD_UNCHANGED)
+    # scattter_prompt = get_prompt_inp_scatter(name[0].replace('gt', 'JIHUAFENJIE'))
+    SAR_prompt = cv2.imread(SAR_prompt, cv2.IMREAD_UNCHANGED)
+    scatter_torch = pre_scatter_prompt(SAR_prompt, filp_flag, device=input_img.device)
+    scatter_torch = scatter_torch.unsqueeze(0)
+    scatter_torch = torch.nn.functional.interpolate(scatter_torch, size=(256, 256))
+    sparse_embeddings, dense_embeddings, scatter_embeddings = model.prompt_encoder(
+        points=None,
+        boxes=None,
+        masks=None,
+        scatter=scatter_torch)
+    # 地物类预测decoder
+    low_res_masks, iou_predictions_2 = model.mask_decoder_diwu(
+        image_embeddings=image_embedding,
+        image_pe=model.prompt_encoder.get_dense_pe(),
+        sparse_prompt_embeddings=sparse_embeddings,
+        dense_prompt_embeddings=dense_embeddings,
+        # multimask_output=False,
+        multimask_output=True,
+    )  # B*C+1*H*W
+    pred = model.postprocess_masks(low_res_masks, model.inp_size, model.inp_size)
+    _, prediction = pred.max(dim=1)
+    prediction = prediction.cpu().numpy()
+    prediction_to_save = label2color(prediction)[0]
+    
+    return prediction_to_save
+
+
+examples1_instance = [
+            ['./images/optical/isaid/_P0007_1065_319_image.png'], 
+            ['./images/optical/isaid/_P0466_1068_420_image.png'], 
+            ['./images/optical/isaid/_P0897_146_34_image.png'],
+            ['./images/optical/isaid/_P1397_844_904_image.png'], 
+            ['./images/optical/isaid/_P2645_883_965_image.png'],
+            ['./images/optical/isaid/_P1398_1290_630_image.png']
+           ]
+
+examples1_terrain = [
+            ['./images/optical/vaihingen/top_mosaic_09cm_area2_105_image.png'],
+            ['./images/optical/vaihingen/top_mosaic_09cm_area4_227_image.png'],
+            ['./images/optical/vaihingen/top_mosaic_09cm_area20_142_image.png'],
+            ['./images/optical/vaihingen/top_mosaic_09cm_area24_128_image.png'],
+            ['./images/optical/vaihingen/top_mosaic_09cm_area27_34_image.png']
+           ]
+
+
+examples1_multi_box = [
+            ['./images/optical/isaid/_P0007_1065_319_image.png'], 
+            ['./images/optical/isaid/_P0466_1068_420_image.png'], 
+            ['./images/optical/isaid/_P0897_146_34_image.png'],
+            ['./images/optical/isaid/_P1397_844_904_image.png'], 
+            ['./images/optical/isaid/_P2645_883_965_image.png'],
+            ['./images/optical/isaid/_P1398_1290_630_image.png']
+           ]
+
+
+examples2 = [
+            ['./images/sar/YIJISARGF3_MYN_QPSI_001269_E113.2_N23.0_20161105_L1A_L10002009158_ampl_4_image.png', './images/sar/YIJISARGF3_MYN_QPSI_001269_E113.2_N23.0_20161105_L1A_L10002009158_ampl_4.png'],
+            ['./images/sar/YIJISARGF3_MYN_QPSI_001269_E113.2_N23.0_20161105_L1A_L10002009158_ampl_15_image.png', './images/sar/YIJISARGF3_MYN_QPSI_001269_E113.2_N23.0_20161105_L1A_L10002009158_ampl_15.png'],
+            ['./images/sar/YIJISARGF3_MYN_QPSI_001269_E113.2_N23.0_20161105_L1A_L10002009158_ampl_24_image.png', './images/sar/YIJISARGF3_MYN_QPSI_001269_E113.2_N23.0_20161105_L1A_L10002009158_ampl_24.png'],
+            ['./images/sar/YIJISARGF3_MYN_QPSI_001269_E113.2_N23.0_20161105_L1A_L10002009158_ampl_41_image.png', './images/sar/YIJISARGF3_MYN_QPSI_001269_E113.2_N23.0_20161105_L1A_L10002009158_ampl_41.png'],
+            ['./images/sar/YIJISARGF3_MYN_QPSI_999996_E121.2_N30.3_20160815_L1A_L10002015572_ampl_150_image.png', './images/sar/YIJISARGF3_MYN_QPSI_999996_E121.2_N30.3_20160815_L1A_L10002015572_ampl_150.png']
+           ]
+
+
+
+# RingMo-SAM designs two new promptable forms based on the characteristics of multimodal remote sensing images: 
+# multi-boxes prompt and SAR polarization scatter prompt.
+
+
+title = "RingMo-SAM:A Foundation Model for Segment Anything in Multimodal Remote Sensing Images<br> \
+<div align='center'> \
+<h2><a href='https://ieeexplore.ieee.org/document/10315957' target='_blank' rel='noopener'>[paper]</a> \
+<br> \
+<image src='file/RingMo-SAM.gif' width='720px' /> \
+<h2>RingMo-SAM can not only segment anything in optical and SAR remote sensing data, but also identify object categories.<h2> \
+</div> \
+"
+
+# <a href='https://github.com/AICyberTeam' target='_blank' rel='noopener'>[code]</a></h2> \
+# with gr.Blocks() as demo:
+#     image_input = gr.Image(type='pil', label='Input Img')
+#     image_output = gr.Image(label='Segment Result', type='numpy')
+    
+        
+Decoder_optical_instance_io = gr.Interface(fn=Decoder1_optical_instance,
+                  inputs=[gr.Image(type='pil', label='optical_instance_img(光学图像)')],
+                  outputs=[gr.Image(label='segment_result', type='numpy')],
+                  # title=title,
+                  description="<p> \
+                    Instance_Decoder:<br>\
+                    Instance-type objects (such as vehicle, aircraft, ship, etc.) have a smaller proportion. <br>\
+                    Our decoder can decouple the SAM's mask decoder into instance category decoder and terrain category decoder to ensure that the model fits adequately to both types of data. <br>\
+                    Choose an example below, or, upload optical instance images to be tested. <br>\
+                    Examples below were never trained and are randomly selected for testing in the wild. <br>\
+                  </p>",
+                  allow_flagging='auto',
+                  examples=examples1_instance,
+                  cache_examples=False,
+                  )
+
+
+Decoder_optical_terrain_io = gr.Interface(fn=Decoder1_optical_terrain,
+                  inputs=[gr.Image(type='pil', label='optical_terrain_img(光学图像)')],
+                  # inputs=[gr.Image(type='pil', label='optical_img(光学图像)'), gr.Image(type='pil', label='SAR_img(SAR图像)'), gr.Image(type='pil', label='SAR_prompt(偏振散射提示)')],
+                  outputs=[gr.Image(label='segment_result', type='numpy')],
+                  # title=title,
+                  description="<p> \
+                    Terrain_Decoder:<br>\
+                    Terrain-type objects (such as vegetation, land, river, etc.) have a larger proportion. <br>\
+                    Our decoder can decouple the SAM's mask decoder into instance category decoder and terrain category decoder to ensure that the model fits adequately to both types of data. <br>\
+                    Choose an example below, or, upload optical terrain images to be tested. <br>\
+                    Examples below were never trained and are randomly selected for testing in the wild. <br>\
+                  </p>",
+                  allow_flagging='auto',
+                  examples=examples1_terrain,
+                  cache_examples=False,
+                  )
+
+
+
+Decoder_multi_box_prompts_io = gr.Interface(fn=Multi_box_prompts,
+              inputs=[gr.ImageMask(brush_radius=4, type='pil', label='input_img(图像)')],
+              outputs=[gr.Image(label='segment_result', type='numpy')],
+              # title=title,
+              description="<p> \
+                Multi-box Prompts:<br>\
+                Multiple boxes are sequentially encoded as concated sparse high-dimensional feature embedding, \
+                the corresponding multiple high-dimensional features are concated together into a high-dimensional feature vector as part of the sparse embedding. <br>\
+                Choose an example below, or, upload images to be tested, and then draw multi-boxes.  <br>\
+                Examples below were never trained and are randomly selected for testing in the wild. <br>\
+              </p>",
+              allow_flagging='auto',
+              examples=examples1_multi_box,
+              cache_examples=False,
+              )
+
+
+
+Decoder_SAR_io = gr.Interface(fn=Decoder2_SAR,
+                  inputs=[gr.Image(type='pil', label='SAR_img(SAR图像)'), gr.Image(type='filepath', label='SAR_prompt(偏振散射提示)')],
+                  outputs=[gr.Image(label='segment_result', type='numpy')],
+                  description="<p> \
+                    SAR Polarization Scatter Prompts:<br>\
+                    Different terrain categories usually exhibit different scattering properties. \
+                    Therefore, we code network for coded mapping of these SAR polarization scatter prompts to the corresponding SAR images, \
+                    which improves the segmentation results of SAR images. <br>\
+                    Choose an example below, or, upload SAR images and the corresponding polarization scatter prompts to be tested. <br>\
+                    Examples below were never trained and are randomly selected for testing in the wild. <br>\
+                  </p>",           
+                  allow_flagging='auto',
+                  examples=examples2,
+                  cache_examples=False,
+                  )
+
+
+# Decoder1_io.launch(server_name="0.0.0.0", server_port=34311)
+# Decoder1_io.launch(enable_queue=False)
+# demo = gr.TabbedInterface([Decoder1_io, Decoder2_io], ['Instance_Decoder', 'Terrain_Decoder'], title=title)
+demo = gr.TabbedInterface([Decoder_optical_instance_io, Decoder_optical_terrain_io, Decoder_multi_box_prompts_io, Decoder_SAR_io], ['optical_instance_img(光学图像)', 'optical_terrain_img(光学图像)', 'multi_box_prompts(多框提示)', 'SAR_img(偏振散射提示)'], title=title).launch()
+# -