a

app.py

@@ -1,105 +1,83 @@
 import gradio as gr
-#
 from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation
 import matplotlib.pyplot as plt
 from matplotlib import gridspec
 import numpy as np
-import tensorflow as tf
 from PIL import Image
-from io import BytesIO
 import requests
 
-#
-
+# Load the pre-trained model and feature extractor
 feature_extractor = SegformerFeatureExtractor.from_pretrained("nvidia/segformer-b0-finetuned-cityscapes-640-1280")
 model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-cityscapes-640-1280")
 
-# urls = ["http://farm3.staticflickr.com/2523/3705549787_79049b1b6d_z.jpg",
-#         "http://farm8.staticflickr.com/7012/6476201279_52db36af64_z.jpg",
-#         "http://farm8.staticflickr.com/7180/6967423255_a3d65d5f6b_z.jpg",
-#         "http://farm4.staticflickr.com/3563/3470840644_3378804bea_z.jpg",
-#         "http://farm9.staticflickr.com/8388/8516454091_0ebdc1130a_z.jpg"]
-# images = []
-# for i in urls:
-#     images.append(Image.open(requests.get(i, stream=True).raw))
-
-
-# inputs = feature_extractor(images=image, return_tensors="pt")
-# outputs = model(**inputs)
-# logits = outputs.logits  # shape (batch_size, num_labels, height/4, width/4)
+def my_palette():
+    return [
+        [131, 162, 255],
+        [180, 189, 255],
+        [255, 227, 187],
+        [255, 210, 143],
+        [248, 117, 170],
+        [255, 223, 223],
+        [255, 246, 246],
+        [174, 222, 252],
+        [150, 194, 145],
+        [255, 219, 170],
+        [244, 238, 238],
+        [50, 38, 83],
+        [128, 98, 214],
+        [146, 136, 248],
+        [255, 210, 215],
+        [255, 152, 152],
+        [162, 103, 138],
+        [63, 29, 56]
+    ]
 
 labels_list = []
-with open("labels.txt", "r") as fp:
+
+with open(r"labels.txt", "r") as fp:
     for line in fp:
         labels_list.append(line[:-1])
 
-colormap = np.asarray([
-    [131, 162, 255],
-    [180, 189, 255],
-    [255, 227, 187],
-    [255, 210, 143],
-    [248, 117, 170],
-    [255, 223, 223],
-    [255, 246, 246],
-    [174, 222, 252],
-    [150, 194, 145],
-    [255, 219, 170],
-    [244, 238, 238],
-    [50, 38, 83],
-    [128, 98, 214],
-    [146, 136, 248],
-    [255, 210, 215],
-    [255, 152, 152],
-    [162, 103, 138],
-    [63, 29, 56]
-])
-
-# with open(r"labels.txt", "r") as fp:
-#     for line in fp:
-#         labels_list.append(line[:-1])
-#
-# colormap = np.asarray(my_palette())
-
+colormap = np.asarray(my_palette())
 
 def greet(input_img):
-    input_img = Image.open(BytesIO(input_img))
-
     inputs = feature_extractor(images=input_img, return_tensors="pt")
     outputs = model(**inputs)
     logits = outputs.logits
 
+    # Use .detach().numpy() to convert PyTorch tensor to NumPy array
     logits = logits.detach().numpy()
+    logits = np.transpose(logits, [0, 2, 3, 1])
+
+    logits = np.resize(logits, input_img.size[::-1])
 
-    logits = tf.transpose(logits.detach().numpy(), [0, 2, 3, 1])
-    logits = tf.image.resize(logits, input_img.size[::-1])
+    seg = np.argmax(logits, axis=-1)[0]
+
+    color_seg = np.zeros(
+        (seg.shape[0], seg.shape[1], 3), dtype=np.uint8
+    )
 
-    seg = tf.math.argmax(logits, axis=-1)[0]
-    color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8)
     for label, color in enumerate(colormap):
-        color_seg[seg.numpy() == label, :] = color
+        color_seg[seg == label, :] = color
 
     pred_img = np.array(input_img) * 0.5 + color_seg * 0.5
     pred_img = pred_img.astype(np.uint8)
 
-    # Draw the plot
     fig = draw_plot(pred_img, seg)
     return fig
 
-
 def draw_plot(pred_img, seg):
     fig = plt.figure(figsize=(20, 15))
-
     grid_spec = gridspec.GridSpec(1, 2, width_ratios=[6, 1])
 
     plt.subplot(grid_spec[0])
     plt.imshow(pred_img)
     plt.axis("off")
-
     LABEL_NAMES = np.asarray(labels_list)
     FULL_LABEL_MAP = np.arange(len(LABEL_NAMES)).reshape(len(LABEL_NAMES), 1)
     FULL_COLOR_MAP = label_to_color_image(FULL_LABEL_MAP)
 
-    unique_labels = np.unique(seg.numpy().astype("uint8"))
+    unique_labels = np.unique(seg.astype("uint8"))
     ax = plt.subplot(grid_spec[1])
     plt.imshow(FULL_COLOR_MAP[unique_labels].astype(np.uint8), interpolation="nearest")
     ax.yaxis.tick_right()
@@ -108,7 +86,6 @@ def draw_plot(pred_img, seg):
     ax.tick_params(width=0.0, labelsize=25)
     return fig
 
-
 def label_to_color_image(label):
     if label.ndim != 2:
         raise ValueError("Expect 2-D input label")
@@ -117,7 +94,6 @@ def label_to_color_image(label):
         raise ValueError("label value too large.")
     return colormap[label]
 
-
 iface = gr.Interface(
     fn=greet,
     inputs="image",
@@ -125,4 +101,4 @@ iface = gr.Interface(
     examples=["image (1).jpg", "image (2).jpg", "image (3).jpg", "image (4).jpg", "image (5).jpg"],
     allow_flagging="never"
 )
-iface.launch(share=True)
+iface.launch(share=True)