init

app.py

@@ -0,0 +1,250 @@
+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(
+    "nvidia/segformer-b0-finetuned-ade-512-512"
+)
+model = TFSegformerForSemanticSegmentation.from_pretrained(
+    "nvidia/segformer-b0-finetuned-ade-512-512"
+)
+
+
+def ade_palette():
+    """ADE20K palette that maps each class to RGB values."""
+    return [
+        [111, 214, 93],
+        [18, 181, 57],
+        [72, 152, 135],
+        [240, 74, 253],
+        [211, 22, 184],
+        [68, 111, 215],
+        [120, 212, 135],
+        [185, 244, 20],
+        [190, 90, 92],
+        [53, 18, 220],
+        [251, 56, 67],
+        [141, 248, 248],
+        [226, 38, 196],
+        [153, 75, 248],
+        [158, 166, 127],
+        [240, 254, 73],
+        [157, 99, 218],
+        [85, 243, 54],
+        [38, 71, 123],
+        [207, 188, 66],
+        [145, 24, 6],
+        [187, 252, 239],
+        [240, 181, 229],
+        [137, 187, 112],
+        [104, 219, 158],
+        [234, 56, 176],
+        [23, 141, 13],
+        [28, 22, 88],
+        [83, 169, 127],
+        [1, 236, 221],
+        [61, 88, 81],
+        [102, 94, 10],
+        [116, 233, 66],
+        [147, 247, 143],
+        [241, 72, 39],
+        [229, 165, 195],
+        [22, 247, 217],
+        [110, 208, 164],
+        [236, 236, 6],
+        [163, 31, 15],
+        [78, 148, 190],
+        [92, 222, 66],
+        [198, 120, 99],
+        [161, 201, 28],
+        [235, 88, 53],
+        [249, 233, 102],
+        [235, 115, 89],
+        [51, 135, 171],
+        [37, 162, 46],
+        [11, 200, 171],
+        [192, 186, 65],
+        [173, 208, 139],
+        [240, 124, 1],
+        [106, 209, 96],
+        [174, 126, 239],
+        [221, 234, 164],
+        [140, 46, 109],
+        [135, 62, 174],
+        [130, 51, 242],
+        [229, 28, 133],
+        [30, 157, 217],
+        [154, 195, 123],
+        [157, 115, 35],
+        [199, 218, 59],
+        [144, 47, 157],
+        [253, 185, 226],
+        [8, 62, 238],
+        [71, 191, 146],
+        [217, 227, 170],
+        [169, 195, 73],
+        [253, 60, 179],
+        [42, 239, 174],
+        [67, 221, 248],
+        [163, 179, 218],
+        [250, 30, 153],
+        [154, 66, 181],
+        [109, 228, 192],
+        [213, 212, 73],
+        [125, 186, 185],
+        [12, 80, 88],
+        [188, 90, 227],
+        [38, 131, 95],
+        [105, 56, 175],
+        [230, 72, 244],
+        [212, 98, 68],
+        [5, 14, 131],
+        [136, 150, 164],
+        [72, 70, 198],
+        [160, 124, 189],
+        [255, 132, 160],
+        [199, 71, 86],
+        [32, 209, 66],
+        [167, 50, 228],
+        [163, 72, 61],
+        [53, 24, 145],
+        [132, 27, 124],
+        [72, 143, 166],
+        [54, 156, 177],
+        [197, 26, 37],
+        [230, 92, 201],
+        [31, 47, 165],
+        [133, 215, 89],
+        [190, 51, 145],
+        [162, 3, 41],
+        [37, 197, 236],
+        [247, 19, 29],
+        [105, 12, 99],
+        [130, 235, 57],
+        [112, 224, 59],
+        [6, 253, 14],
+        [205, 176, 152],
+        [110, 202, 51],
+        [94, 74, 61],
+        [108, 86, 56],
+        [148, 184, 162],
+        [125, 0, 195],
+        [143, 211, 60],
+        [108, 240, 95],
+        [106, 211, 59],
+        [12, 1, 158],
+        [46, 53, 36],
+        [130, 192, 113],
+        [204, 224, 85],
+        [162, 86, 98],
+        [10, 155, 230],
+        [76, 105, 166],
+        [157, 34, 206],
+        [3, 230, 115],
+        [115, 172, 117],
+        [98, 2, 191],
+        [173, 132, 102],
+        [3, 47, 51],
+        [60, 7, 102],
+        [70, 47, 237],
+        [10, 145, 167],
+        [235, 156, 244],
+        [142, 188, 86],
+        [137, 45, 182],
+        [110, 37, 249],
+        [21, 108, 156],
+        [51, 19, 187],
+        [66, 99, 230],
+        [249, 153, 221],
+        [231, 146, 194],
+        [153, 115, 50],
+        [25, 15, 226],
+        [126, 9, 119],
+        [241, 114, 28],
+        [134, 156, 64],
+        [111, 215, 120],
+    ]
+
+
+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=(800, 600)),
+    outputs=["plot"],
+    examples=[
+        "image (1).jpg"],
+    allow_flagging="never",
+)
+
+demo.launch()

edittxt.py

@@ -0,0 +1,153 @@
+dic = {"0": "wall",
+       "1": "building",
+       "2": "sky",
+       "3": "floor",
+       "4": "tree",
+       "5": "ceiling",
+       "6": "road",
+       "7": "bed ",
+       "8": "windowpane",
+       "9": "grass",
+       "10": "cabinet",
+       "11": "sidewalk",
+       "12": "person",
+       "13": "earth",
+       "14": "door",
+       "15": "table",
+       "16": "mountain",
+       "17": "plant",
+       "18": "curtain",
+       "19": "chair",
+       "20": "car",
+       "21": "water",
+       "22": "painting",
+       "23": "sofa",
+       "24": "shelf",
+       "25": "house",
+       "26": "sea",
+       "27": "mirror",
+       "28": "rug",
+       "29": "field",
+       "30": "armchair",
+       "31": "seat",
+       "32": "fence",
+       "33": "desk",
+       "34": "rock",
+       "35": "wardrobe",
+       "36": "lamp",
+       "37": "bathtub",
+       "38": "railing",
+       "39": "cushion",
+       "40": "base",
+       "41": "box",
+       "42": "column",
+       "43": "signboard",
+       "44": "chest of drawers",
+       "45": "counter",
+       "46": "sand",
+       "47": "sink",
+       "48": "skyscraper",
+       "49": "fireplace",
+       "50": "refrigerator",
+       "51": "grandstand",
+       "52": "path",
+       "53": "stairs",
+       "54": "runway",
+       "55": "case",
+       "56": "pool table",
+       "57": "pillow",
+       "58": "screen door",
+       "59": "stairway",
+       "60": "river",
+       "61": "bridge",
+       "62": "bookcase",
+       "63": "blind",
+       "64": "coffee table",
+       "65": "toilet",
+       "66": "flower",
+       "67": "book",
+       "68": "hill",
+       "69": "bench",
+       "70": "countertop",
+       "71": "stove",
+       "72": "palm",
+       "73": "kitchen island",
+       "74": "computer",
+       "75": "swivel chair",
+       "76": "boat",
+       "77": "bar",
+       "78": "arcade machine",
+       "79": "hovel",
+       "80": "bus",
+       "81": "towel",
+       "82": "light",
+       "83": "truck",
+       "84": "tower",
+       "85": "chandelier",
+       "86": "awning",
+       "87": "streetlight",
+       "88": "booth",
+       "89": "television receiver",
+       "90": "airplane",
+       "91": "dirt track",
+       "92": "apparel",
+       "93": "pole",
+       "94": "land",
+       "95": "bannister",
+       "96": "escalator",
+       "97": "ottoman",
+       "98": "bottle",
+       "99": "buffet",
+       "100": "poster",
+       "101": "stage",
+       "102": "van",
+       "103": "ship",
+       "104": "fountain",
+       "105": "conveyer belt",
+       "106": "canopy",
+       "107": "washer",
+       "108": "plaything",
+       "109": "swimming pool",
+       "110": "stool",
+       "111": "barrel",
+       "112": "basket",
+       "113": "waterfall",
+       "114": "tent",
+       "115": "bag",
+       "116": "minibike",
+       "117": "cradle",
+       "118": "oven",
+       "119": "ball",
+       "120": "food",
+       "121": "step",
+       "122": "tank",
+       "123": "trade name",
+       "124": "microwave",
+       "125": "pot",
+       "126": "animal",
+       "127": "bicycle",
+       "128": "lake",
+       "129": "dishwasher",
+       "130": "screen",
+       "131": "blanket",
+       "132": "sculpture",
+       "133": "hood",
+       "134": "sconce",
+       "135": "vase",
+       "136": "traffic light",
+       "137": "tray",
+       "138": "ashcan",
+       "139": "fan",
+       "140": "pier",
+       "141": "crt screen",
+       "142": "plate",
+       "143": "monitor",
+       "144": "bulletin board",
+       "145": "shower",
+       "146": "radiator",
+       "147": "glass",
+       "148": "clock",
+       "149": "flag"}
+
+for i in dic.values():
+    print(i)

initcolor.py

@@ -0,0 +1,8 @@
+import random
+
+print(random.randint(0, 255))
+
+for i in list(range(150)):
+    print("[" + str(random.randint(0, 255)) + ", "
+          + str(random.randint(0, 255)) + ", "
+          + str(random.randint(0, 255)) + "], ")

labels.txt

@@ -0,0 +1,150 @@
+wall
+building
+sky
+floor
+tree
+ceiling
+road
+bed
+windowpane
+grass
+cabinet
+sidewalk
+person
+earth
+door
+table
+mountain
+plant
+curtain
+chair
+car
+water
+painting
+sofa
+shelf
+house
+sea
+mirror
+rug
+field
+armchair
+seat
+fence
+desk
+rock
+wardrobe
+lamp
+bathtub
+railing
+cushion
+base
+box
+column
+signboard
+chest of drawers
+counter
+sand
+sink
+skyscraper
+fireplace
+refrigerator
+grandstand
+path
+stairs
+runway
+case
+pool table
+pillow
+screen door
+stairway
+river
+bridge
+bookcase
+blind
+coffee table
+toilet
+flower
+book
+hill
+bench
+countertop
+stove
+palm
+kitchen island
+computer
+swivel chair
+boat
+bar
+arcade machine
+hovel
+bus
+towel
+light
+truck
+tower
+chandelier
+awning
+streetlight
+booth
+television receiver
+airplane
+dirt track
+apparel
+pole
+land
+bannister
+escalator
+ottoman
+bottle
+buffet
+poster
+stage
+van
+ship
+fountain
+conveyer belt
+canopy
+washer
+plaything
+swimming pool
+stool
+barrel
+basket
+waterfall
+tent
+bag
+minibike
+cradle
+oven
+ball
+food
+step
+tank
+trade name
+microwave
+pot
+animal
+bicycle
+lake
+dishwasher
+screen
+blanket
+sculpture
+hood
+sconce
+vase
+traffic light
+tray
+ashcan
+fan
+pier
+crt screen
+plate
+monitor
+bulletin board
+shower
+radiator
+glass
+clock
+flag