a

app.py

@@ -1,30 +1,126 @@
 import gradio as gr
 #
-from transformers import SegformerFeatureExtractor, TFSegformerForSemanticSegmentation
+from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation
+import matplotlib.pyplot as plt
+from matplotlib import gridspec
 from PIL import Image
+import numpy as np
+import tensorflow as tf
 import requests
+
 #
 
 feature_extractor = SegformerFeatureExtractor.from_pretrained("nvidia/segformer-b0-finetuned-cityscapes-640-1280")
-model = TFSegformerForSemanticSegmentation.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(r"labels.txt", "r") as fp:
+    for line in fp:
+        labels_list.append(line[:-1])
+
+colormap = np.asarray(my_palette())
+
+
+def greet(input_img):
+    inputs = feature_extractor(images=input_img, return_tensors="pt")
+    outputs = model(**inputs)
+    logits = outputs.logits
+
+    logits = tf.transpose(logits, [0, 2, 3, 1])
+
+    logits = tf.image.resize(
+        logits, input_img.size[::-1]
+    )  # We reverse the shape of `image` because `image.size` returns width and height.
+    seg = tf.math.argmax(logits, axis=-1)[0]
+
+    color_seg = np.zeros(
+        (seg.shape[0], seg.shape[1], 3), dtype=np.uint8
+    )  # height, width, 3
+    for label, color in enumerate(colormap):
+        color_seg[seg.numpy() == label, :] = color
+
+    # Show image + mask
+    pred_img = np.array(input_img) * 0.5 + color_seg * 0.5
+    pred_img = pred_img.astype(np.uint8)
+
+    fig = draw_plot(pred_img, seg)
+    return fig
+
 
+def draw_plot(pred_img, seg):
+    fig = plt.figure(figsize=(20, 15))
 
-url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-image = Image.open(requests.get(url, stream=True).raw)
+    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"))
+    ax = plt.subplot(grid_spec[1])
+    plt.imshow(FULL_COLOR_MAP[unique_labels].astype(np.uint8), interpolation="nearest")
+    ax.yaxis.tick_right()
+    plt.yticks(range(len(unique_labels)), LABEL_NAMES[unique_labels])
+    plt.xticks([], [])
+    ax.tick_params(width=0.0, labelsize=25)
+    return fig
 
-inputs = feature_extractor(images=image, return_tensors="pt")
-outputs = model(**inputs)
-logits = outputs.logits  # shape (batch_size, num_labels, height/4, width/4)
 
+def label_to_color_image(label):
+    if label.ndim != 2:
+        raise ValueError("Expect 2-D input label")
 
+    if np.max(label) >= len(colormap):
+        raise ValueError("label value too large.")
+    return colormap[label]
 
-def greet():
-    return outputs
 
 iface = gr.Interface(
-    fn=sepia,
-    inputs="text",
-    outputs=["plot"])
-iface.launch(share=True)
+    fn=greet,
+    inputs=gr.Image(shape=(640, 1280)),
+    outputs=["plot"],
+    examples=[images],
+    allow_flagging="never")
+iface.launch(share=True)

labels.txt

@@ -0,0 +1,18 @@
+sidewalk
+building
+wall
+fence
+pole
+traffic light
+traffic sign
+vegetation
+terrain
+sky
+person
+rider
+car
+truck
+bus
+train
+motorcycle
+bicycle