import cv2 as cv import numpy as np import argparse from tqdm import tqdm from functools import partial from copy import deepcopy import os from common import * ## General information on how to use the sample ''' This sample proposes experimental inpainting sample using Latent Diffusion Model (LDM) for inpainting. Most of the script is based on the code from the official repository of the LDM model: https://github.com/CompVis/latent-diffusion Current limitations of the script: - Slow diffusion sampling - Not exact reproduction of the results from the original repository (due to issues related deviation in convolution operation. See issue for more details: https://github.com/opencv/opencv/pull/25973) LDM inpainting model was converted to ONNX graph using following steps: Generate the onnx model using this [repo](https://github.com/Abdurrahheem/latent-diffusion/tree/ash/export2onnx) and follow instructions below - git clone https://github.com/Abdurrahheem/latent-diffusion.git - cd latent-diffusion - conda env create -f environment.yaml - conda activate ldm - wget -O models/ldm/inpainting_big/last.ckpt https://heibox.uni-heidelberg.de/f/4d9ac7ea40c64582b7c9/?dl=1 - python -m scripts.inpaint.py --indir data/inpainting_examples/ --outdir outputs/inpainting_results --export=True 2. Build opencv 3. Run the script - cd opencv/samples/dnn - Download models using `python download_models.py ldm_inpainting` - python ldm_inpainting.py - For more options, use python ldm_inpainting.py -h After running the code you will be promted with image. You can click on left mouse button and start selecting a region you would like to be inpainted (deleted). Once you finish marking the region, click on left mouse button again and press esc button on your keyboard. The inpainting proccess will start. Note: If you are running it on CPU it might take a large chank of time. Also make sure to have abount 15GB of RAM to make proccess faster (other wise swapping will kick in and everything will be slower) ''' def get_args_parser(): backends = ("default", "openvino", "opencv", "vkcom", "cuda") targets = ("cpu", "opencl", "opencl_fp16", "ncs2_vpu", "hddl_vpu", "vulkan", "cuda", "cuda_fp16") parser = argparse.ArgumentParser(add_help=False) parser.add_argument('--zoo', default=os.path.join(os.path.dirname(os.path.abspath(__file__)), 'models.yml'), help='An optional path to file with preprocessing parameters.') parser.add_argument('--input', '-i', default="rubberwhale1.png", help='Path to image file.', required=False) parser.add_argument('--samples', '-s', type=int, help='Number of times to sample the model.', default=50) parser.add_argument('--mask', '-m', type=str, help='Path to mask image. If not provided, interactive mask creation will be used.', default=None) parser.add_argument('--backend', default="default", type=str, choices=backends, help="Choose one of computation backends: " "default: automatically (by default), " "openvino: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), " "opencv: OpenCV implementation, " "vkcom: VKCOM, " "cuda: CUDA, " "webnn: WebNN") parser.add_argument('--target', default="cpu", type=str, choices=targets, help="Choose one of target computation devices: " "cpu: CPU target (by default), " "opencl: OpenCL, " "opencl_fp16: OpenCL fp16 (half-float precision), " "ncs2_vpu: NCS2 VPU, " "hddl_vpu: HDDL VPU, " "vulkan: Vulkan, " "cuda: CUDA, " "cuda_fp16: CUDA fp16 (half-float preprocess)") args, _ = parser.parse_known_args() add_preproc_args(args.zoo, parser, 'ldm_inpainting', prefix="", alias="ldm_inpainting") add_preproc_args(args.zoo, parser, 'ldm_inpainting', prefix="encoder_", alias="ldm_inpainting") add_preproc_args(args.zoo, parser, 'ldm_inpainting', prefix="decoder_", alias="ldm_inpainting") add_preproc_args(args.zoo, parser, 'ldm_inpainting', prefix="diffusor_", alias="ldm_inpainting") parser = argparse.ArgumentParser(parents=[parser], description='Diffusion based image inpainting using OpenCV.', formatter_class=argparse.ArgumentDefaultsHelpFormatter) return parser.parse_args() stdSize = 0.7 stdWeight = 2 stdImgSize = 512 imgWidth = None fontSize = 1.5 fontThickness = 1 def keyboard_shorcuts(): print(''' Keyboard Shorcuts: Press 'i' to increase brush size. Press 'd' to decrease brush size. Press 'r' to reset mask. Press ' ' (space bar) after selecting area to be inpainted. Press ESC to terminate the program. ''' ) def help(): print( ''' Use this script for image inpainting using OpenCV. Firstly, download required models i.e. ldm_inpainting using `download_models.py ldm_inpainting` (if not already done). Set environment variable OPENCV_DOWNLOAD_CACHE_DIR to specify where models should be downloaded. Also, point OPENCV_SAMPLES_DATA_PATH to opencv/samples/data. To run: Example: python ldm_inpainting.py ''' ) def make_batch_blob(image, mask): blob_image = cv.dnn.blobFromImage(image, scalefactor=args.scale, size=(args.width, args.height), mean=args.mean, swapRB=args.rgb, crop=False) blob_mask = cv.dnn.blobFromImage(mask, scalefactor=args.scale, size=(args.width, args.height), mean=args.mean, swapRB=False, crop=False) blob_mask = (blob_mask >= 0.5).astype(np.float32) masked_image = (1 - blob_mask) * blob_image batch = { "image": blob_image, "mask": blob_mask, "masked_image": masked_image } for k in batch: batch[k] = batch[k]*2.0 - 1.0 return batch def noise_like(shape, repeat=False): repeat_noise = lambda: np.random.randn((1, *shape[1:])).repeat(shape[0], *((1,) * (len(shape) - 1))) noise = lambda: np.random.randn(*shape) return repeat_noise() if repeat else noise() def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True): if ddim_discr_method == 'uniform': c = num_ddpm_timesteps // num_ddim_timesteps ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c))) elif ddim_discr_method == 'quad': ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int) else: raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"') # assert ddim_timesteps.shape[0] == num_ddim_timesteps # add one to get the final alpha values right (the ones from first scale to data during sampling) steps_out = ddim_timesteps + 1 if verbose: print(f'Selected timesteps for ddim sampler: {steps_out}') return steps_out def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True): # select alphas for computing the variance schedule alphas = alphacums[ddim_timesteps] alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist()) # according the the formula provided in https://arxiv.org/abs/2010.02502 sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev)) if verbose: print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}') print(f'For the chosen value of eta, which is {eta}, ' f'this results in the following sigma_t schedule for ddim sampler {sigmas}') return sigmas, alphas, alphas_prev def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): if schedule == "linear": betas = ( np.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep).astype(np.float64) ** 2 ) elif schedule == "cosine": timesteps = ( np.arange(n_timestep + 1).astype(np.float64) / n_timestep + cosine_s ) alphas = timesteps / (1 + cosine_s) * np.pi / 2 alphas = np.cos(alphas).pow(2) alphas = alphas / alphas[0] betas = 1 - alphas[1:] / alphas[:-1] betas = np.clip(betas, a_min=0, a_max=0.999) elif schedule == "sqrt_linear": betas = np.linspace(linear_start, linear_end, n_timestep).astype(np.float64) elif schedule == "sqrt": betas = np.linspace(linear_start, linear_end, n_timestep).astype(np.float64) ** 0.5 else: raise ValueError(f"schedule '{schedule}' unknown.") return betas class DDIMSampler(object): def __init__(self, model, schedule="linear", ddpm_num_timesteps=1000): super().__init__() self.model = model self.ddpm_num_timesteps = ddpm_num_timesteps self.schedule = schedule def register_buffer(self, name, attr): setattr(self, name, attr) def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True): self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps, num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose) alphas_cumprod = self.model.alphas_cumprod assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep' to_numpy = partial(np.array, copy=True, dtype=np.float32) self.register_buffer('betas', to_numpy(self.model.betas)) self.register_buffer('alphas_cumprod', to_numpy(alphas_cumprod)) self.register_buffer('alphas_cumprod_prev', to_numpy(self.model.alphas_cumprod_prev)) # calculations for diffusion q(x_t | x_{t-1}) and others self.register_buffer('sqrt_alphas_cumprod', to_numpy(np.sqrt(alphas_cumprod))) self.register_buffer('sqrt_one_minus_alphas_cumprod', to_numpy(np.sqrt(1. - alphas_cumprod))) self.register_buffer('log_one_minus_alphas_cumprod', to_numpy(np.log(1. - alphas_cumprod))) self.register_buffer('sqrt_recip_alphas_cumprod', to_numpy(np.sqrt(1. / alphas_cumprod))) self.register_buffer('sqrt_recipm1_alphas_cumprod', to_numpy(np.sqrt(1. / alphas_cumprod - 1))) # ddim sampling parameters ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod, ddim_timesteps=self.ddim_timesteps, eta=ddim_eta,verbose=verbose) self.register_buffer('ddim_sigmas', ddim_sigmas) self.register_buffer('ddim_alphas', ddim_alphas) self.register_buffer('ddim_alphas_prev', ddim_alphas_prev) self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas)) sigmas_for_original_sampling_steps = ddim_eta * np.sqrt( (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * ( 1 - self.alphas_cumprod / self.alphas_cumprod_prev)) self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps) def sample(self, S, batch_size, shape, conditioning=None, eta=0., temperature=1., verbose=True, x_T=None, log_every_t=100, unconditional_guidance_scale=1., unconditional_conditioning=None, # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... **kwargs ): if conditioning is not None: if isinstance(conditioning, dict): cbs = conditioning[list(conditioning.keys())[0]].shape[0] if cbs != batch_size: print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") else: if conditioning.shape[0] != batch_size: print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose) # sampling C, H, W = shape size = (batch_size, C, H, W) print(f'Data shape for DDIM sampling is {size}, eta {eta}') samples, intermediates = self.ddim_sampling(conditioning, size, ddim_use_original_steps=False, temperature=temperature, x_T=x_T, log_every_t=log_every_t, unconditional_guidance_scale=unconditional_guidance_scale, unconditional_conditioning=unconditional_conditioning, ) return samples, intermediates def ddim_sampling(self, cond, shape, x_T=None, ddim_use_original_steps=False, timesteps=None,log_every_t=100, temperature=1., unconditional_guidance_scale=1., unconditional_conditioning=None,): b = shape[0] if x_T is None: img = np.random.randn(*shape) else: img = x_T if timesteps is None: timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps elif timesteps is not None and not ddim_use_original_steps: subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1 timesteps = self.ddim_timesteps[:subset_end] intermediates = {'x_inter': [img], 'pred_x0': [img]} time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps) total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0] print(f"Running DDIM Sampling with {total_steps} timesteps") iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps) for i, step in enumerate(iterator): index = total_steps - i - 1 ts = np.full((b, ), step, dtype=np.int64) outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps, temperature=temperature, unconditional_guidance_scale=unconditional_guidance_scale, unconditional_conditioning=unconditional_conditioning) img, pred_x0 = outs if index % log_every_t == 0 or index == total_steps - 1: intermediates['x_inter'].append(img) intermediates['pred_x0'].append(pred_x0) return img, intermediates def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, temperature=1., unconditional_guidance_scale=1., unconditional_conditioning=None): b = x.shape[0] if unconditional_conditioning is None or unconditional_guidance_scale == 1.: e_t = self.model.apply_model(x, t, c) alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas # select parameters corresponding to the currently considered timestep a_t = np.full((b, 1, 1, 1), alphas[index]) a_prev = np.full((b, 1, 1, 1), alphas_prev[index]) sigma_t = np.full((b, 1, 1, 1), sigmas[index]) sqrt_one_minus_at = np.full((b, 1, 1, 1), sqrt_one_minus_alphas[index]) # current prediction for x_0 pred_x0 = (x - sqrt_one_minus_at * e_t) / np.sqrt(a_t) # direction pointing to x_t dir_xt = np.sqrt(1. - a_prev - sigma_t**2) * e_t noise = sigma_t * noise_like(x.shape, repeat_noise) * temperature x_prev = np.sqrt(a_prev) * pred_x0 + dir_xt + noise return x_prev, pred_x0 class DDIMInpainter(object): def __init__(self, args, v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta parameterization="eps", # all assuming fixed variance schedules linear_start=0.0015, linear_end=0.0205, conditioning_key="concat", ): super().__init__() self.v_posterior = v_posterior self.parameterization = parameterization self.conditioning_key = conditioning_key self.register_schedule(linear_start=linear_start, linear_end=linear_end) # Initialize models using provided paths or download if necessary encoder_path = findModel(args.encoder_model, args.encoder_sha1) decoder_path = findModel(args.decoder_model, args.decoder_sha1) diffusor_path = findModel(args.diffusor_model, args.diffusor_sha1) engine = cv.dnn.ENGINE_AUTO if args.backend != "default" or args.target != "cpu": engine = cv.dnn.ENGINE_CLASSIC self.encoder = cv.dnn.readNet(encoder_path, "", "", engine) self.diffusor = cv.dnn.readNet(diffusor_path, "", "", engine) self.decoder = cv.dnn.readNet(decoder_path, "", "", engine) self.sampler = DDIMSampler(self, ddpm_num_timesteps=self.num_timesteps) self.set_backend(backend=get_backend_id(args.backend), target=get_target_id(args.target)) def set_backend(self, backend=cv.dnn.DNN_BACKEND_DEFAULT, target=cv.dnn.DNN_TARGET_CPU): self.encoder.setPreferableBackend(backend) self.encoder.setPreferableTarget(target) self.decoder.setPreferableBackend(backend) self.decoder.setPreferableTarget(target) self.diffusor.setPreferableBackend(backend) self.diffusor.setPreferableTarget(target) def apply_diffusor(self, x, timestep, cond): x = np.concatenate([x, cond], axis=1) x = cv.Mat(x.astype(np.float32)) timestep = cv.Mat(timestep.astype(np.int64)) names = ["xc, t", "timesteps"] self.diffusor.setInputsNames(names) self.diffusor.setInput(x, names[0]) self.diffusor.setInput(timestep, names[1]) output = self.diffusor.forward() return output def register_buffer(self, name, attr): setattr(self, name, attr) def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): if given_betas is not None: betas = given_betas else: betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s) alphas = 1. - betas alphas_cumprod = np.cumprod(alphas, axis=0) alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) timesteps, = betas.shape self.num_timesteps = int(timesteps) self.linear_start = linear_start self.linear_end = linear_end assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep' to_numpy = partial(np.array, dtype=np.float32) self.register_buffer('betas', to_numpy(betas)) self.register_buffer('alphas_cumprod', to_numpy(alphas_cumprod)) self.register_buffer('alphas_cumprod_prev', to_numpy(alphas_cumprod_prev)) # calculations for diffusion q(x_t | x_{t-1}) and others self.register_buffer('sqrt_alphas_cumprod', to_numpy(np.sqrt(alphas_cumprod))) self.register_buffer('sqrt_one_minus_alphas_cumprod', to_numpy(np.sqrt(1. - alphas_cumprod))) self.register_buffer('log_one_minus_alphas_cumprod', to_numpy(np.log(1. - alphas_cumprod))) self.register_buffer('sqrt_recip_alphas_cumprod', to_numpy(np.sqrt(1. / alphas_cumprod))) self.register_buffer('sqrt_recipm1_alphas_cumprod', to_numpy(np.sqrt(1. / alphas_cumprod - 1))) # calculations for posterior q(x_{t-1} | x_t, x_0) posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / ( 1. - alphas_cumprod) + self.v_posterior * betas # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t) self.register_buffer('posterior_variance', to_numpy(posterior_variance)) # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain self.register_buffer('posterior_log_variance_clipped', to_numpy(np.log(np.maximum(posterior_variance, 1e-20)))) self.register_buffer('posterior_mean_coef1', to_numpy( betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))) self.register_buffer('posterior_mean_coef2', to_numpy( (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod))) if self.parameterization == "eps": lvlb_weights = self.betas ** 2 / ( 2 * self.posterior_variance * to_numpy(alphas) * (1 - self.alphas_cumprod)) elif self.parameterization == "x0": lvlb_weights = 0.5 * np.sqrt(alphas_cumprod) / (2. * 1 - alphas_cumprod) else: raise NotImplementedError("mu not supported") # TODO how to choose this term lvlb_weights[0] = lvlb_weights[1] self.register_buffer('lvlb_weights', lvlb_weights) assert not np.isnan(self.lvlb_weights).all() def apply_model(self, x_noisy, t, cond, return_ids=False): if isinstance(cond, dict): # hybrid case, cond is exptected to be a dict pass else: # if not isinstance(cond, list): # cond = [cond] key = 'c_concat' if self.conditioning_key == 'concat' else 'c_crossattn' cond = {key: cond} x_recon = self.apply_diffusor(x_noisy, t, cond['c_concat']) if isinstance(x_recon, tuple) and not return_ids: return x_recon[0] else: return x_recon def inpaint(self, image : np.ndarray, mask : np.ndarray, S : int = 50) -> np.ndarray: inpainted = self(image, mask, S) return np.squeeze(inpainted) def __call__(self, image : np.ndarray, mask : np.ndarray, S : int = 50) -> np.ndarray: # Encode the image and mask self.encoder.setInput(image) c = self.encoder.forward() cc = cv.resize(np.squeeze(mask), dsize=(c.shape[3], c.shape[2]), interpolation=cv.INTER_NEAREST) #TODO:check for correcteness of intepolation cc = cc[None,None] c = np.concatenate([c, cc], axis=1) shape = (c.shape[1] - 1,) + c.shape[2:] # Sample from the model samples_ddim, _ = self.sampler.sample( S=S, conditioning=c, batch_size=c.shape[0], shape=shape, verbose=False) ## Decode the sample samples_ddim = samples_ddim.astype(np.float32) samples_ddim = cv.Mat(samples_ddim) self.decoder.setInput(samples_ddim) x_samples_ddim = self.decoder.forward() image = np.clip((image + 1.0) / 2.0, a_min=0.0, a_max=1.0) mask = np.clip((mask + 1.0) / 2.0, a_min=0.0, a_max=1.0) predicted_image = np.clip((x_samples_ddim + 1.0) / 2.0, a_min=0.0, a_max=1.0) inpainted = (1 - mask) * image + mask * predicted_image inpainted = np.transpose(inpainted, (0, 2, 3, 1)) * 255 return inpainted def create_mask(img): drawing = False # True if the mouse is pressed brush_size = 20 # Mouse callback function def draw_circle(event, x, y, flags, param): nonlocal drawing, brush_size if event == cv.EVENT_LBUTTONDOWN: drawing = True elif event == cv.EVENT_MOUSEMOVE: if drawing: cv.circle(mask, (x, y), brush_size, (255), thickness=-1) elif event == cv.EVENT_LBUTTONUP: drawing = False # Create window with instructions window_name = 'Draw Mask' cv.namedWindow(window_name) cv.setMouseCallback(window_name, draw_circle) label = "Press 'i' to increase, 'd' to decrease brush size. And 'r' to reset mask. " labelSize, _ = cv.getTextSize(label, cv.FONT_HERSHEY_SIMPLEX, fontSize, fontThickness) alpha = 0.5 temp_image = img.copy() overlay = img.copy() cv.rectangle(overlay, (0, 0), (labelSize[0]+10, labelSize[1]+int(30*fontSize)), (255, 255, 255), cv.FILLED) cv.addWeighted(overlay, alpha, temp_image, 1 - alpha, 0, temp_image) cv.putText(temp_image, "Draw the mask on the image. Press space bar when done.", (10, int(25*fontSize)), cv.FONT_HERSHEY_SIMPLEX, fontSize, (0, 0, 0), fontThickness) cv.putText(temp_image, label, (10, int(50*fontSize)), cv.FONT_HERSHEY_SIMPLEX, fontSize, (0, 0, 0), fontThickness) mask = np.zeros((img.shape[0], img.shape[1]), np.uint8) display_img = temp_image.copy() while True: display_img[mask > 0] = [255, 255, 255] cv.imshow(window_name, display_img) # Create a copy of the image to show instructions key = cv.waitKey(30) & 0xFF if key == ord('i'): # Increase brush size brush_size += 1 print(f"Brush size increased to {brush_size}") elif key == ord('d'): # Decrease brush size brush_size = max(1, brush_size - 1) print(f"Brush size decreased to {brush_size}") elif key == ord('r'): # clear the mask mask = np.zeros((img.shape[0], img.shape[1]), dtype=np.uint8) display_img = temp_image.copy() print(f"Mask cleared") elif key == ord(' '): # Press space bar to finish drawing break elif key == 27: exit() cv.destroyAllWindows() return mask def prepare_input(args, image): if args.mask: mask = cv.imread(args.mask, cv.IMREAD_GRAYSCALE) if mask is None: raise ValueError(f"Could not read mask file: {args.mask}") if mask.shape[:2] != image.shape[:2]: mask = cv.resize(mask, (image.shape[1], image.shape[0]), interpolation=cv.INTER_NEAREST) else: mask = create_mask(deepcopy(image)) batch = make_batch_blob(image, mask) return batch def main(args): global imgWidth, fontSize, fontThickness keyboard_shorcuts() image = cv.imread(findFile(args.input)) imgWidth = min(image.shape[:2]) fontSize = min(1.5, (stdSize*imgWidth)/stdImgSize) fontThickness = max(1,(stdWeight*imgWidth)//stdImgSize) aspect_ratio = image.shape[0]/image.shape[1] height = int(args.width*aspect_ratio) batch = prepare_input(args, image) model = DDIMInpainter(args) result = model.inpaint(batch["masked_image"], batch["mask"], S=args.samples) result = result.astype(np.uint8) result = cv.resize(result, (args.width, height)) result = cv.cvtColor(result, cv.COLOR_RGB2BGR) cv.imshow("Inpainted Image", result) cv.waitKey(0) cv.destroyAllWindows() if __name__ == '__main__': args = get_args_parser() main(args)