Update app.py

app.py

@@ -14,6 +14,7 @@ import zipfile
 from torchvision import transforms, models
 from get_models import Resnet_with_skip
 
+
 def create_retrieval_figure(res):
     fig = plt.figure(figsize=[10 * 3, 10 * 3])
     cols = 5
@@ -21,22 +22,22 @@ def create_retrieval_figure(res):
     ax_query = fig.add_subplot(rows, 1, 1)
     plt.rcParams['figure.facecolor'] = 'white'
     plt.axis('off')
-    ax_query.set_title('Top 10 most similar scarabs', fontsize=40)
+    ax_query.set_title('Top 10 most similar items', fontsize=40)
     names = ""
     for i, image in zip(range(len(res)), res):
-        current_image_path = "dataset/" + image.split("/")[3]+"/"+image.split("/")[4]
-        if i==0: continue
-        if i < 11:
-            archive = zipfile.ZipFile('dataset.zip', 'r')
-            imgfile = archive.read(current_image_path)
-            image = cv2.imdecode(np.frombuffer(imgfile, np.uint8), 1)
-            # image_resized = cv2.resize(image, (224, 224), interpolation=cv2.INTER_LINEAR)
-            ax = fig.add_subplot(rows, cols, i)
-            plt.axis('off')
-            plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
-            item_uuid = current_image_path.split("/")[2].split("_photoUUID")[0].split("itemUUID_")[1]
-            ax.set_title('Top {}'.format(i), fontsize=40)
-            names = names + "Top " + str(i) + " item UUID is " + item_uuid + "\n"
+        if i >= 10:
+            break
+        current_image_path = image.split("/")[3]+"/"+image.split("/")[4]
+        archive = zipfile.ZipFile('dataset.zip', 'r')
+        imgfile = archive.read(current_image_path)
+        image = cv2.imdecode(np.frombuffer(imgfile, np.uint8), 1)
+        # image_resized = cv2.resize(image, (224, 224), interpolation=cv2.INTER_LINEAR)
+        ax = fig.add_subplot(rows, cols, i + 1)
+        plt.axis('off')
+        plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+        item_uuid = current_image_path.split("/")[1].split("_photoUUID")[0].split("itemUUID_")[1]
+        ax.set_title('Top {}'.format(i), fontsize=40)
+        names = names + "Top " + str(i) + " item UUID is " + item_uuid + "\n"
     return fig, names
 
 def knn_calc(image_name, query_feature, features):
@@ -54,12 +55,13 @@ resnet.fc = nn.Linear(num_ftrs, 13)
 model = Resnet_with_skip(resnet)
 checkpoint = torch.load(checkpoint_path, map_location="cpu")
 model.load_state_dict(checkpoint)
+model.eval()
 embedding_model_test = torch.nn.Sequential(*(list(model.children())[:-1]))
 
-periods_model = models.resnet101(pretrained=True)
-periods_model.fc = nn.Linear(num_ftrs, 5)
-periods_checkpoint = torch.load("periods.pth.tar", map_location="cpu")
-periods_model.load_state_dict(periods_checkpoint)
+# periods_model = models.resnet101(pretrained=True)
+# periods_model.fc = nn.Linear(num_ftrs, 5)
+# periods_checkpoint = torch.load("periods.pth.tar", map_location="cpu")
+# periods_model.load_state_dict(periods_checkpoint)
 
 with open('query_images_paths.pkl', 'rb') as fp:
     query_images_paths = pickle.load(fp)
@@ -71,7 +73,7 @@ with open('features.pkl', 'rb') as fp:
 
 model.eval()
 transform = transforms.Compose([
-            transforms.Resize((112, 112)),
+            transforms.Resize((224, 224)),
             transforms.Grayscale(num_output_channels=3),
             transforms.ToTensor(),
             transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
@@ -82,42 +84,41 @@ invTrans = transforms.Compose([transforms.Normalize(mean=[0., 0., 0.],
                                                     std=[1., 1., 1.]),
                                ])
 
-labels = ['ankh', 'anthropomorphic', 'bands', 'beetle', 'bird', 'circles', 'cross', 'duck', 'head', 'ibex', 'lion', 'sa', 'snake']
-
-periods_labels = ["MB1", "MB2", "LB", "Iron1", 'Iron2']
-periods_model.eval()
+# labels = ['ankh', 'anthropomorphic', 'bands', 'beetle', 'bird', 'circles', 'cross', 'duck', 'head', 'ibex', 'lion', 'sa', 'snake']
+#
+# periods_labels = ["MB1", "MB2", "LB", "Iron1", 'Iron2']
+# periods_model.eval()
 
 def predict(inp):
     image_tensor = transform(inp)
     with torch.no_grad():
-        classification, reconstruction = model(image_tensor.unsqueeze(0))
-        periods_classification = periods_model(image_tensor.unsqueeze(0))
-        recon_tensor = reconstruction[0].repeat(3, 1, 1)
-        recon_tensor = invTrans(kornia.enhance.invert(recon_tensor))
-        plot_recon = recon_tensor.permute(1, 2, 0).detach().numpy()
-        w, h = inp.size
-        # plot_recon = resize(plot_recon, (h, w))
-        m = nn.Sigmoid()
-        y = m(classification)
-        preds = []
-        for sample in y:
-            for i in sample:
-                if i >=0.8:
-                    preds.append(1)
-                else:
-                    preds.append(0)
-        confidences = {}
-        true_labels = ""
-        for i in range(len(labels)):
-            if preds[i]==1:
-                if true_labels=="":
-                    true_labels = true_labels + labels[i]
-                else:
-                    true_labels = true_labels + "&" + labels[i]
-        confidences[true_labels] = torch.tensor(1.0)
-
-        periods_prediction = torch.nn.functional.softmax(periods_classification[0], dim=0)
-        periods_confidences = {periods_labels[i]: periods_prediction[i] for i in range(len(periods_labels))}
+        # classification, reconstruction = model(image_tensor.unsqueeze(0))
+        # periods_classification = periods_model(image_tensor.unsqueeze(0))
+        # recon_tensor = reconstruction[0].repeat(3, 1, 1)
+        # recon_tensor = invTrans(kornia.enhance.invert(recon_tensor))
+        # plot_recon = recon_tensor.permute(1, 2, 0).detach().numpy()
+        # w, h = inp.size
+        # m = nn.Sigmoid()
+        # y = m(classification)
+        # preds = []
+        # for sample in y:
+        #     for i in sample:
+        #         if i >=0.8:
+        #             preds.append(1)
+        #         else:
+        #             preds.append(0)
+        # confidences = {}
+        # true_labels = ""
+        # for i in range(len(labels)):
+        #     if preds[i]==1:
+        #         if true_labels=="":
+        #             true_labels = true_labels + labels[i]
+        #         else:
+        #             true_labels = true_labels + "&" + labels[i]
+        # confidences[true_labels] = torch.tensor(1.0)
+        #
+        # periods_prediction = torch.nn.functional.softmax(periods_classification[0], dim=0)
+        # periods_confidences = {periods_labels[i]: periods_prediction[i] for i in range(len(periods_labels))}
         feature = embedding_model_test(image_tensor.unsqueeze(0))
         dists = dict()
         with torch.no_grad():
@@ -126,7 +127,7 @@ def predict(inp):
                 dists[image_name] = dist
         res = dict(sorted(dists.items(), key=itemgetter(1)))
     fig, names = create_retrieval_figure(res)
-    return confidences, periods_confidences, fig, names, plot_recon
+    return fig, names
 
 
 a = gr.Interface(fn=predict,
@@ -134,6 +135,7 @@ a = gr.Interface(fn=predict,
              title="ArcAid: Analysis of Archaeological Artifacts using Drawings",
              description="Easily classify artifacs, retrieve similar ones and generate drawings. "
                          "https://arxiv.org/abs/2211.09480.",
-             # examples=['anth.jpg', 'beetle_snakes.jpg', 'bird.jpg', 'cross.jpg', 'ibex.jpg',
-             #           'lion.jpg', 'lion2.jpg', 'sa.jpg'],
-             outputs=[gr.Label(num_top_classes=3), gr.Label(num_top_classes=1), 'plot', 'text', "image"], ).launch()
+             examples=['anth.jpg', 'beetle_snakes.jpg', 'bird.jpg', 'cross.jpg', 'ibex.jpg',
+                       'lion.jpg', 'lion2.jpg', 'sa.jpg'],
+             outputs=['plot', 'text'], ).launch(share=True, enable_queue=True)
+             # outputs=[gr.Label(num_top_classes=3), gr.Label(num_top_classes=1), "image", 'plot', 'text'], ).launch(share=True, enable_queue=True)