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import gradio as gr |
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import torch |
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from fastai.vision.all import * |
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from PIL import ImageFilter, ImageEnhance, ImageDraw |
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from diffusers.utils import make_image_grid |
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from tqdm import tqdm |
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from diffusers import AutoPipelineForInpainting, LCMScheduler, DDIMScheduler |
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from diffusers import StableDiffusionControlNetInpaintPipeline, ControlNetModel |
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import numpy as np |
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from PIL import Image |
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from datetime import datetime |
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preferred_dtype = torch.float32 |
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preferred_device = "cuda" if torch.cuda.is_available() else "cpu" |
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def label_func(fn): return path/"labels"/f"{fn.stem}_P{fn.suffix}" |
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segmodel = load_learner("camvid-512.pkl") |
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working_size = (512, 512) |
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seg_vocabulary = ['Animal', 'Archway', 'Bicyclist', 'Bridge', 'Building', 'Car', |
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'CartLuggagePram', 'Child', 'Column_Pole', 'Fence', 'LaneMkgsDriv', |
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'LaneMkgsNonDriv', 'Misc_Text', 'MotorcycleScooter', 'OtherMoving', |
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'ParkingBlock', 'Pedestrian', 'Road', 'RoadShoulder', 'Sidewalk', |
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'SignSymbol', 'Sky', 'SUVPickupTruck', 'TrafficCone', |
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'TrafficLight', 'Train', 'Tree', 'Truck_Bus', 'Tunnel', |
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'VegetationMisc', 'Void', 'Wall'] |
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ban_cars_mask = np.array([0, 0, 0, 0, 0, 1, |
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0, 0, 1, 0, 1, |
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1, 1, 0, 0, |
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1, 0, 1, 1, 1, |
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1, 0, 1, 1, |
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1, 0, 0, 0, 1, |
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0, 1, 0], dtype=np.uint8) |
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def get_seg_mask(img): |
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mask = segmodel.predict(img)[0] |
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return mask |
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def display_mask(img, mask): |
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mask_rgb = np.stack([np.zeros_like(mask), mask, np.zeros_like(mask)], axis=-1) |
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img_pil = Image.fromarray(img) |
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mask_pil = Image.fromarray((mask_rgb * 255).astype(np.uint8)) |
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overlaid_img = Image.blend(img_pil, mask_pil, alpha=0.5) |
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return overlaid_img |
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def redact_image(img): |
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img = img.resize((256, 256)) |
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mask = get_seg_mask(img) |
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car_mask = ban_cars_mask[mask] |
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return display_mask(img, car_mask) |
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def flip(img): |
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return np.flipud(img) |
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def app(img): |
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start_time = datetime.now().timestamp() |
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img = img.resize(working_size) |
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mask = ban_cars_mask[get_seg_mask(img)] |
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overlay_img = Image.fromarray(np.stack([img[:, :, 0], mask / 2, img[:,:,2]], axis=-1)) |
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end_time = datetime.now().timestamp() |
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draw = ImageDraw.Draw(overlay_img) |
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draw.text((10, 10), f"Duration: {int(1000 * (end_time - start_time))}ms", fill=(255, 255, 255)) |
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return overlay_img |
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iface = gr.Interface(app, gr.Image(), "image") |
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iface.launch() |
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