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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+food-5.jpg filter=lfs diff=lfs merge=lfs -text

README.md

@@ -4,7 +4,7 @@ emoji: 🏃
 colorFrom: purple
 colorTo: indigo
 sdk: gradio
-sdk_version: 4.2.0
+sdk_version: 3.44.4
 app_file: app.py
 pinned: false
 ---

app.py

@@ -0,0 +1,197 @@
+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
+
+feature_extractor = SegformerFeatureExtractor.from_pretrained(
+    "prem-timsina/segformer-b0-finetuned-food"
+)
+model = TFSegformerForSemanticSegmentation.from_pretrained(
+    "prem-timsina/segformer-b0-finetuned-food"
+)
+
+def ade_palette():
+    """ADE20K palette that maps each class to RGB values."""
+    return [
+        [93, 93, 93],
+        [43, 240, 132],
+        [139, 136, 240],
+        [158, 83, 109],
+        [6, 76, 151],
+        [95, 170, 87],
+        [273, 236, 139],
+        [21, 155, 160],
+        [188, 220, 166],
+        [238, 96, 247],
+        [223, 180, 221],
+        [29, 97, 24],
+        [3, 233, 248],
+        [105, 118, 44],
+        [203, 237, 63],
+        [234, 100, 240],
+        [19, 179, 164],
+        [65, 22, 115],
+        [111, 128, 194],
+        [232, 41, 17],
+        [11, 250, 159],
+        [137, 163, 129],
+        [212, 223, 210],
+        [51, 37, 4],
+        [37, 63, 239],
+        [257, 180, 163],
+        [172, 53, 105],
+        [104, 150, 99],
+        [80, 157, 133],
+        [195, 104, 202],
+        [42, 187, 110],
+        [133, 225, 66],
+        [132, 99, 213],
+        [178, 248, 209],
+        [93, 147, 60],
+        [105, 109, 115],
+        [26, 65, 115],
+        [239, 52, 182],
+        [242, 19, 204],
+        [157, 101, 214],
+        [248, 85, 198],
+        [103, 198, 171],
+        [44, 129, 75],
+        [159, 32, 120],
+        [155, 77, 71],
+        [233, 231, 155],
+        [135, 196, 206],
+        [81, 53, 51],
+        [134, 221, 213],
+        [192, 27, 152],
+        [127, 127, 194],
+        [82, 161, 1],
+        [71, 80, 161],
+        [148, 9, 159],
+        [91, 110, 124],
+        [127, 157, 223],
+        [25, 210, 232],
+        [129, 0, 114],
+        [231, 187, 138],
+        [23, 17, 224],
+        [25, 255, 29],
+        [158, 19, 53],
+        [157, 190, 176],
+        [114, 140, 221],
+        [46, 104, 87],
+        [17, 114, 122],
+        [221, 12, 229],
+        [54, 20, 92],
+        [215, 191, 252],
+        [144, 127, 146],
+        [141, 116, 77],
+        [100, 89, 89],
+        [104, 115, 249],
+        [179, 212, 38],
+        [140, 248, 179],
+        [177, 230, 240],
+        [219, 98, 8],
+        [74, 219, 53],
+        [161, 28, 243],
+        [64, 57, 184],
+        [147, 193, 113],
+        [182, 15, 30],
+        [151, 204, 109],
+        [187, 76, 21],
+        [118, 163, 155],
+        [158, 30, 220],
+        [227, 170, 63],
+        [199, 186, 72],
+        [0, 241, 168],
+        [80, 150, 225],
+        [237, 250, 4],
+        [29, 210, 181],
+        [176, 120, 81],
+        [134, 47, 123],
+        [240, 141, 130],
+        [250, 41, 115],
+        [29, 88, 143],
+        [66, 151, 87],
+        [241, 231, 144],
+        [238, 107, 153],
+        [181, 96, 220],
+        [239, 122, 133],
+        [205, 120, 21],
+        [168, 12, 77],
+
+    ]
+
+labels_list = []
+
+with open(r'labels.txt', 'r') as fp:
+    for line in fp:
+        labels_list.append(line[:-1])
+
+colormap = np.asarray(ade_palette())
+
+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):
+    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"))
+    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
+
+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
+
+    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
+
+demo = gr.Interface(fn=sepia,
+                    inputs=gr.Image(shape=(400, 600)),
+                    outputs=['plot'],
+                    examples=["food-1.jpg","food-2.jpg", "food-3.jpg", "food-4.jpg", "food-5.jpg", "food-6.jpg"],
+                    allow_flagging='never')
+
+
+demo.launch()

labels.txt

@@ -0,0 +1,104 @@
+background
+candy
+egg tart
+french fries
+chocolate
+biscuit
+popcorn
+pudding
+ice cream
+cheese butter
+cake
+wine
+milkshake
+coffee
+juice
+milk
+tea
+almond
+red beans
+cashew
+dried cranberries
+soy
+walnut
+peanut
+egg
+apple
+date
+apricot
+avocado
+banana
+strawberry
+cherry
+blueberry
+raspberry
+mango
+olives
+peach
+lemon
+pear
+fig
+pineapple
+grape
+kiwi
+melon
+orange
+watermelon
+steak
+pork
+chicken duck
+sausage
+fried meat
+lamb
+sauce
+crab
+fish
+shellfish
+shrimp
+soup
+bread
+corn
+hamburg
+pizza
+hanamaki baozi
+wonton dumplings
+pasta
+noodles
+rice
+pie
+tofu
+eggplant
+potato
+garlic
+cauliflower
+tomato
+kelp
+seaweed
+spring onion
+rape
+ginger
+okra
+lettuce
+pumpkin
+cucumber
+white radish
+carrot
+asparagus
+bamboo shoots
+broccoli
+celery stick
+cilantro mint
+snow peas
+cabbage
+bean sprouts
+onion
+pepper
+green beans
+French beans
+king oyster mushroom
+shiitake
+enoki mushroom
+oyster mushroom
+white button mushroom
+salad
+other ingredients

requirements.txt

@@ -0,0 +1,6 @@
+torch
+transformers
+tensorflow
+numpy
+Image
+matplotlib