Add visualization code

visualize.py

@@ -0,0 +1,97 @@
+import os
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+import torch
+import numpy as np
+import os.path as op
+import torch.nn.functional as F
+from datasets import build_dataloader
+from utils.checkpoint import Checkpointer
+from model import build_model
+from utils.metrics import Evaluator
+from utils.iotools import load_train_configs
+import random
+import matplotlib.pyplot as plt
+from PIL import Image
+from datasets.cuhkpedes import CUHKPEDES
+
+
+config_file  = '/xxx/configs.yaml'
+args = load_train_configs(config_file)
+args.batch_size = 1024
+args.training = False
+device = "cuda"
+test_img_loader, test_txt_loader = build_dataloader(args)
+model = build_model(args)
+checkpointer = Checkpointer(model)
+checkpointer.load(f=op.join(args.output_dir, 'best.pth'))
+model.to(device)
+
+evaluator = Evaluator(test_img_loader, test_txt_loader)
+
+qfeats, gfeats, qids, gids = evaluator._compute_embedding(model.eval())
+qfeats = F.normalize(qfeats, p=2, dim=1) # text features
+gfeats = F.normalize(gfeats, p=2, dim=1) # image features
+
+similarity = qfeats @ gfeats.t()
+# acclerate sort with topk
+_, indices = torch.topk(similarity, k=10, dim=1, largest=True, sorted=True)  # q * topk
+
+dataset = CUHKPEDES(root='./data')
+test_dataset = dataset.test
+
+img_paths = test_dataset['img_paths']
+captions = test_dataset['captions']
+gt_img_paths = test_dataset['gt_img_paths']
+
+def get_one_query_caption_and_result_by_id(idx, indices, qids, gids, captions, img_paths, gt_img_paths):
+    query_caption = captions[idx]
+    query_id = qids[idx]
+    image_paths = [img_paths[j] for j in indices[idx]]
+    image_ids = gids[indices[idx]]
+    gt_image_path = gt_img_paths[idx]
+    return query_id, image_ids, query_caption, image_paths, gt_image_path
+
+def plot_retrieval_images(query_id, image_ids, query_caption, image_paths, gt_img_path, fname=None):
+    print(query_id)
+    print(image_ids)
+    print(query_caption)
+    fig = plt.figure()
+    col = len(image_paths)
+
+    # plot ground truth image
+    plt.subplot(1, col+1, 1)
+    img = Image.open(gt_img_path)
+    img = img.resize((128, 256))
+    plt.imshow(img)
+    plt.xticks([])
+    plt.yticks([])
+
+    for i in range(col):
+        plt.subplot(1, col+1, i+2)
+        img = Image.open(image_paths[i])
+
+        bwith = 2  # 边框宽度设置为2
+        ax = plt.gca()  # 获取边框
+        if image_ids[i] == query_id:
+            ax.spines['top'].set_color('lawngreen')
+            ax.spines['right'].set_color('lawngreen')
+            ax.spines['bottom'].set_color('lawngreen')
+            ax.spines['left'].set_color('lawngreen')
+        else:
+            ax.spines['top'].set_color('red')
+            ax.spines['right'].set_color('red')
+            ax.spines['bottom'].set_color('red')
+            ax.spines['left'].set_color('red')
+
+        img = img.resize((128, 256))
+        plt.imshow(img)
+        plt.xticks([])
+        plt.yticks([])
+
+
+    fig.show()
+    if fname:
+        plt.savefig(fname, dpi=300)
+# idx is the index of qids(A list of query ids, range from 0 - len(qids))
+query_id, image_ids, query_caption, image_paths, gt_img_path = get_one_query_caption_and_result_by_id(0, indices, qids, gids, captions, img_paths, gt_img_paths)
+plot_retrieval_images(query_id, image_ids, query_caption, image_paths, gt_img_path)