segmentation

app.py

@@ -1,203 +1,73 @@
 import gradio as gr
-
 from matplotlib import gridspec
 import matplotlib.pyplot as plt
 import numpy as np
 from PIL import Image
-import tensorflow as tf
-from transformers import SegformerFeatureExtractor, TFSegformerForSemanticSegmentation
+import torch
+from transformers import AutoImageProcessor, AutoModelForSemanticSegmentation
 
-feature_extractor = SegformerFeatureExtractor.from_pretrained(
-    "nvidia/segformer-b5-finetuned-ade-640-640"
-)
-model = TFSegformerForSemanticSegmentation.from_pretrained(
-    "nvidia/segformer-b5-finetuned-ade-640-640"
-)
+# ✅ PyTorch 모델로 변경
+MODEL_ID = "nvidia/segformer-b5-finetuned-ade-640-640"
+processor = AutoImageProcessor.from_pretrained(MODEL_ID)
+model = AutoModelForSemanticSegmentation.from_pretrained(MODEL_ID)
 
 def ade_palette():
     """ADE20K palette that maps each class to RGB values."""
     return [
-        [204, 87, 92],
-        [112, 185, 212],
-        [45, 189, 106],
-        [234, 123, 67],
-        [78, 56, 123],
-        [210, 32, 89],
-        [90, 180, 56],
-        [155, 102, 200],
-        [33, 147, 176],
-        [255, 183, 76],
-        [67, 123, 89],
-        [190, 60, 45],
-        [134, 112, 200],
-        [56, 45, 189],
-        [200, 56, 123],
-        [87, 92, 204],
-        [120, 56, 123],
-        [45, 78, 123],
-        [156, 200, 56],
-        [32, 90, 210],
-        [56, 123, 67],
-        [180, 56, 123],
-        [123, 67, 45],
-        [45, 134, 200],
-        [67, 56, 123],
-        [78, 123, 67],
-        [32, 210, 90],
-        [45, 56, 189],
-        [123, 56, 123],
-        [56, 156, 200],
-        [189, 56, 45],
-        [112, 200, 56],
-        [56, 123, 45],
-        [200, 32, 90],
-        [123, 45, 78],
-        [200, 156, 56],
-        [45, 67, 123],
-        [56, 45, 78],
-        [45, 56, 123],
-        [123, 67, 56],
-        [56, 78, 123],
-        [210, 90, 32],
-        [123, 56, 189],
-        [45, 200, 134],
-        [67, 123, 56],
-        [123, 45, 67],
-        [90, 32, 210],
-        [200, 45, 78],
-        [32, 210, 90],
-        [45, 123, 67],
-        [165, 42, 87],
-        [72, 145, 167],
-        [15, 158, 75],
-        [209, 89, 40],
-        [32, 21, 121],
-        [184, 20, 100],
-        [56, 135, 15],
-        [128, 92, 176],
-        [1, 119, 140],
-        [220, 151, 43],
-        [41, 97, 72],
-        [148, 38, 27],
-        [107, 86, 176],
-        [21, 26, 136],
-        [174, 27, 90],
-        [91, 96, 204],
-        [108, 50, 107],
-        [27, 45, 136],
-        [168, 200, 52],
-        [7, 102, 27],
-        [42, 93, 56],
-        [140, 52, 112],
-        [92, 107, 168],
-        [17, 118, 176],
-        [59, 50, 174],
-        [206, 40, 143],
-        [44, 19, 142],
-        [23, 168, 75],
-        [54, 57, 189],
-        [144, 21, 15],
-        [15, 176, 35],
-        [107, 19, 79],
-        [204, 52, 114],
-        [48, 173, 83],
-        [11, 120, 53],
-        [206, 104, 28],
-        [20, 31, 153],
-        [27, 21, 93],
-        [11, 206, 138],
-        [112, 30, 83],
-        [68, 91, 152],
-        [153, 13, 43],
-        [25, 114, 54],
-        [92, 27, 150],
-        [108, 42, 59],
-        [194, 77, 5],
-        [145, 48, 83],
-        [7, 113, 19],
-        [25, 92, 113],
-        [60, 168, 79],
-        [78, 33, 120],
-        [89, 176, 205],
-        [27, 200, 94],
-        [210, 67, 23],
-        [123, 89, 189],
-        [225, 56, 112],
-        [75, 156, 45],
-        [172, 104, 200],
-        [15, 170, 197],
-        [240, 133, 65],
-        [89, 156, 112],
-        [214, 88, 57],
-        [156, 134, 200],
-        [78, 57, 189],
-        [200, 78, 123],
-        [106, 120, 210],
-        [145, 56, 112],
-        [89, 120, 189],
-        [185, 206, 56],
-        [47, 99, 28],
-        [112, 189, 78],
-        [200, 112, 89],
-        [89, 145, 112],
-        [78, 106, 189],
-        [112, 78, 189],
-        [156, 112, 78],
-        [28, 210, 99],
-        [78, 89, 189],
-        [189, 78, 57],
-        [112, 200, 78],
-        [189, 47, 78],
-        [205, 112, 57],
-        [78, 145, 57],
-        [200, 78, 112],
-        [99, 89, 145],
-        [200, 156, 78],
-        [57, 78, 145],
-        [78, 57, 99],
-        [57, 78, 145],
-        [145, 112, 78],
-        [78, 89, 145],
-        [210, 99, 28],
-        [145, 78, 189],
-        [57, 200, 136],
-        [89, 156, 78],
-        [145, 78, 99],
-        [99, 28, 210],
-        [189, 78, 47],
-        [28, 210, 99],
-        [78, 145, 57],
+        [204, 87, 92],[112, 185, 212],[45, 189, 106],[234, 123, 67],[78, 56, 123],[210, 32, 89],
+        [90, 180, 56],[155, 102, 200],[33, 147, 176],[255, 183, 76],[67, 123, 89],[190, 60, 45],
+        [134, 112, 200],[56, 45, 189],[200, 56, 123],[87, 92, 204],[120, 56, 123],[45, 78, 123],
+        [156, 200, 56],[32, 90, 210],[56, 123, 67],[180, 56, 123],[123, 67, 45],[45, 134, 200],
+        [67, 56, 123],[78, 123, 67],[32, 210, 90],[45, 56, 189],[123, 56, 123],[56, 156, 200],
+        [189, 56, 45],[112, 200, 56],[56, 123, 45],[200, 32, 90],[123, 45, 78],[200, 156, 56],
+        [45, 67, 123],[56, 45, 78],[45, 56, 123],[123, 67, 56],[56, 78, 123],[210, 90, 32],
+        [123, 56, 189],[45, 200, 134],[67, 123, 56],[123, 45, 67],[90, 32, 210],[200, 45, 78],
+        [32, 210, 90],[45, 123, 67],[165, 42, 87],[72, 145, 167],[15, 158, 75],[209, 89, 40],
+        [32, 21, 121],[184, 20, 100],[56, 135, 15],[128, 92, 176],[1, 119, 140],[220, 151, 43],
+        [41, 97, 72],[148, 38, 27],[107, 86, 176],[21, 26, 136],[174, 27, 90],[91, 96, 204],
+        [108, 50, 107],[27, 45, 136],[168, 200, 52],[7, 102, 27],[42, 93, 56],[140, 52, 112],
+        [92, 107, 168],[17, 118, 176],[59, 50, 174],[206, 40, 143],[44, 19, 142],[23, 168, 75],
+        [54, 57, 189],[144, 21, 15],[15, 176, 35],[107, 19, 79],[204, 52, 114],[48, 173, 83],
+        [11, 120, 53],[206, 104, 28],[20, 31, 153],[27, 21, 93],[11, 206, 138],[112, 30, 83],
+        [68, 91, 152],[153, 13, 43],[25, 114, 54],[92, 27, 150],[108, 42, 59],[194, 77, 5],
+        [145, 48, 83],[7, 113, 19],[25, 92, 113],[60, 168, 79],[78, 33, 120],[89, 176, 205],
+        [27, 200, 94],[210, 67, 23],[123, 89, 189],[225, 56, 112],[75, 156, 45],[172, 104, 200],
+        [15, 170, 197],[240, 133, 65],[89, 156, 112],[214, 88, 57],[156, 134, 200],[78, 57, 189],
+        [200, 78, 123],[106, 120, 210],[145, 56, 112],[89, 120, 189],[185, 206, 56],[47, 99, 28],
+        [112, 189, 78],[200, 112, 89],[89, 145, 112],[78, 106, 189],[112, 78, 189],[156, 112, 78],
+        [28, 210, 99],[78, 89, 189],[189, 78, 57],[112, 200, 78],[189, 47, 78],[205, 112, 57],
+        [78, 145, 57],[200, 78, 112],[99, 89, 145],[200, 156, 78],[57, 78, 145],[78, 57, 99],
+        [57, 78, 145],[145, 112, 78],[78, 89, 145],[210, 99, 28],[145, 78, 189],[57, 200, 136],
+        [89, 156, 78],[145, 78, 99],[99, 28, 210],[189, 78, 47],[28, 210, 99],[78, 145, 57],
     ]
 
 labels_list = []
-
-with open(r'labels.txt', 'r') as fp:
+with open("labels.txt", "r", encoding="utf-8") as fp:
     for line in fp:
-        labels_list.append(line[:-1])
+        labels_list.append(line.rstrip("\n"))
 
-colormap = np.asarray(ade_palette())
+colormap = np.asarray(ade_palette(), dtype=np.uint8)
 
 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 draw_plot(pred_img, seg):
+def draw_plot(pred_img, seg_np):
     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_np.astype("uint8"))
     ax = plt.subplot(grid_spec[1])
     plt.imshow(FULL_COLOR_MAP[unique_labels].astype(np.uint8), interpolation="nearest")
     ax.yaxis.tick_right()
@@ -206,37 +76,40 @@ def draw_plot(pred_img, seg):
     ax.tick_params(width=0.0, labelsize=25)
     return fig
 
-def sepia(input_img):
-    input_img = Image.fromarray(input_img)
-
-    inputs = feature_extractor(images=input_img, return_tensors="tf")
-    outputs = model(**inputs)
-    logits = outputs.logits
+def run_inference(input_img):
+    # input: numpy array from gradio -> PIL
+    img = Image.fromarray(input_img.astype(np.uint8)) if isinstance(input_img, np.ndarray) else input_img
+    if img.mode != "RGB":
+        img = img.convert("RGB")
 
-    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]
+    inputs = processor(images=img, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits  # (1, C, h/4, w/4)
 
-    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
+    # resize to original
+    upsampled = torch.nn.functional.interpolate(
+        logits, size=img.size[::-1], mode="bilinear", align_corners=False
+    )
+    seg = upsampled.argmax(dim=1)[0].cpu().numpy().astype(np.uint8)  # (H,W)
 
-    # Show image + mask
-    pred_img = np.array(input_img) * 0.5 + color_seg * 0.5
-    pred_img = pred_img.astype(np.uint8)
+    # colorize & overlay
+    color_seg = colormap[seg]                                # (H,W,3)
+    pred_img = (np.array(img) * 0.5 + color_seg * 0.5).astype(np.uint8)
 
     fig = draw_plot(pred_img, seg)
     return fig
 
-demo = gr.Interface(fn=sepia,
-                    inputs=gr.Image(shape=(400, 600)),
-                    outputs=['plot'],
-                    examples=["ADE_val_00000001.jpeg", "ADE_val_00001159.jpg", "ADE_val_00001248.jpg", "ADE_val_00001472.jpg"],
-                    allow_flagging='never')
-
+demo = gr.Interface(
+    fn=run_inference,
+    inputs=gr.Image(shape=(400, 600)),
+    outputs=["plot"],
+    examples=[
+        "ADE_val_00000001.jpeg", "ADE_val_00001159.jpg",
+        "ADE_val_00001248.jpg", "ADE_val_00001472.jpg"
+    ],
+    allow_flagging="never",
+)
 
-demo.launch()
+if __name__ == "__main__":
+    demo.launch()