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app.py

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+import gradio as gr
+from matplotlib import gridspec
+import matplotlib.pyplot as plt
+import numpy as np
+from PIL import Image
+import torch
+from transformers import AutoImageProcessor, AutoModelForSemanticSegmentation
+
+MODEL_ID = "ZhengPeng7/BiRefNet"
+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],[154, 87, 92], [112, 185, 212], [45, 189, 106], [234, 123, 67], [78, 56, 123], [210, 32, 89],
+        [40, 180, 56], [105, 102, 200], [0, 147, 176], [205, 183, 76], [17, 123, 89], [140, 60, 45],
+        [84, 112, 200], [6, 45, 189], [150, 56, 123], [37, 92, 204], [70, 56, 123], [0, 78, 123],
+        [106, 200, 56], [0, 90, 210], [6, 123, 67], [130, 56, 123], [73, 67, 45], [0, 134, 200],
+        [17, 56, 123], [28, 123, 67], [0, 210, 90], [0, 56, 189], [73, 56, 123], [56, 106, 200],
+        [139, 56, 45], [112, 200, 6], [56, 73, 45], [150, 32, 90], [123, 45, 28], [150, 156, 56],
+        [45, 17, 123], [56, 45, 28], [45, 6, 123], [73, 67, 56], [56, 78, 73], [160, 90, 32],
+        [123, 56, 139], [45, 150, 134], [67, 73, 56], [73, 45, 67], [90, 32, 160], [150, 45, 78],
+        [32, 210, 40],
+    ]
+
+labels_list = []
+with open("labels.txt", "r", encoding="utf-8") as fp:
+    for line in fp:
+        labels_list.append(line.rstrip("\n"))
+
+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_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_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()
+    plt.yticks(range(len(unique_labels)), LABEL_NAMES[unique_labels])
+    plt.xticks([], [])
+    ax.tick_params(width=0.0, labelsize=25)
+    return fig
+
+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")
+
+    inputs = processor(images=img, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits  # (1, C, h/4, w/4)
+
+    # 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)
+
+    # 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=run_inference,
+    inputs=gr.Image(type="numpy", label="Input Image"),
+    outputs=gr.Plot(label="Overlay + Legend"),
+    examples=[
+        "person-1.jpg",
+        "person-2.jpg",
+        "person-3.jpg",
+        "person-4.jpg",
+        "person-5.jpg"
+    ],
+    flagging_mode="never",
+    cache_examples=False,
+)
+
+if __name__ == "__main__":
+    demo.launch()

labels.txt

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+Airplane,
+Ant,
+Antenna,
+Archery,
+Axe,
+BabyCarriage,
+Bag,
+BalanceBeam,
+Balcony,
+Balloon,
+Basket,
+BasketballHoop,
+Beatle,
+Bed,
+Bee,
+Bench,
+Bicycle
+BicycleFrame,
+BicycleStand,
+Boat,
+Bonsai,
+BoomLift,
+Bridge,
+BunkBed,
+Butterfly, Button, Cable, CableLift, Cage, Camcorder, Cannon, Canoe, Car,
+CarParkDropArm,
+Carriage, Cart, Caterpillar, CeilingLamp, Centipede, Chair, Clip, Clock, Clothes, CoatHanger, Comb, ConcretePumpTruck, Crack, Crane, '
+Cup,
+DentalChair,
+Desk,
+DeskChair,
+Diagram,
+DishRack,
+DoorHandle,
+Dragonfish,
+Dragonfly,
+Drum,
+Earphone,
+Easel,
+ElectricIron,
+Excavator,
+Eyeglasses, '
+Fan,
+Fence,
+Fencing,
+FerrisWheel,
+FireExtinguisher,
+Fishing,
+Flag,
+FloorLamp,
+Forklift,
+GasStation,
+Gate,
+Gear,
+Goal,
+Golf,
+GymEquipment,
+Hammock, '
+Handcart,
+Handcraft,
+Handrail,
+HangGlider,
+Harp, Harvester,
+Headset,
+Helicopter,
+Helmet,
+Hook,
+HorizontalBar,
+Hydrovalve,
+IroningTable,
+Jewelry,
+Key, '
+KidsPlayground,
+Kitchenware,
+Kite,
+Knife,
+Ladder,
+LaundryRack,
+Lightning,
+Lobster,
+Locust,
+Machine,
+MachineGun,
+MagazineRack,
+Mantis,
+Medal,
+MemorialArchway, '
+Microphone,
+Missile,
+MobileHolder,
+Monitor,
+Mosquito,
+Motorcycle,
+MovingTrolley,
+Mower,
+MusicPlayer,
+MusicStand,
+ObservationTower,
+Octopus,
+OilWell, '
+OlympicLogo,
+OperatingTable,
+OutdoorFitnessEquipment,
+Parachute,
+Pavilion,
+Piano,
+Pipe,
+PlowHarrow,
+PoleVault,
+Punchbag,
+Rack,
+Racket,
+Rifle,
+Ring,
+Robot, '
+RockClimbing,
+Rope,
+Sailboat,
+Satellite,
+Scaffold,
+Scale,
+Scissor,
+Scooter,
+Sculpture,
+Seadragon,
+Seahorse,
+Seal,
+SewingMachine,
+Ship,
+Shoe,
+ShoppingCart, '
+ShoppingTrolley,
+Shower,
+Shrimp,
+Signboard,
+Skateboarding,
+Skeleton,
+Skiing,
+Spade,
+SpeedBoat,
+Spider,
+Spoon,
+Stair,
+Stand,
+Stationary,
+SteeringWheel, '
+Stethoscope,
+Stool,
+Stove,
+StreetLamp,
+SweetStand,
+Swing,
+Sword,
+TV,
+Table,
+TableChair,
+TableLamp,
+TableTennis,
+Tank,
+Tapeline,
+Teapot,
+Telescope,
+Tent, '
+TobaccoPipe,
+Toy,
+Tractor,
+TrafficLight,
+TrafficSign,
+Trampoline,
+TransmissionTower,
+Tree,
+Tricycle,
+TrimmerCover,
+Tripod,
+Trombone,
+Truck,
+Trumpet,
+Tuba, '
+UAV,
+Umbrella,
+UnevenBars,
+UtilityPole,
+VacuumCleaner,
+Violin,
+Wakesurfing,
+Watch,
+WaterTower,
+WateringPot,
+Well,
+WellLid,
+Wheel,
+Wheelchair,
+WindTurbine,
+Windmill,
+WineGlass,
+WireWhisk,
+Yacht'

requirements.txt

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+torch
+transformers>=4.41.0
+gradio>=4.0.0
+Pillow
+numpy
+matplotlib