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| import random | |
| import gradio as gr | |
| import imageio | |
| import numpy as np | |
| import onnx | |
| import onnxruntime as rt | |
| import huggingface_hub | |
| from numpy.random import RandomState | |
| from skimage import transform | |
| def get_inter(r1, r2): | |
| h_inter = max(min(r1[3], r2[3]) - max(r1[1], r2[1]), 0) | |
| w_inter = max(min(r1[2], r2[2]) - max(r1[0], r2[0]), 0) | |
| return h_inter * w_inter | |
| def iou(r1, r2): | |
| s1 = (r1[2] - r1[0]) * (r1[3] - r1[1]) | |
| s2 = (r2[2] - r2[0]) * (r2[3] - r2[1]) | |
| i = get_inter(r1, r2) | |
| return i / (s1 + s2 - i) | |
| def letterbox(im, new_shape=(640, 640), color=(0.5, 0.5, 0.5), stride=32): | |
| # Resize and pad image while meeting stride-multiple constraints | |
| shape = im.shape[:2] # current shape [height, width] | |
| # Scale ratio (new / old) | |
| r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) | |
| # Compute padding | |
| new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) | |
| dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding | |
| dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding | |
| dw /= 2 # divide padding into 2 sides | |
| dh /= 2 | |
| if shape != new_unpad: # resize | |
| im = transform.resize(im, (new_unpad[1], new_unpad[0])) | |
| top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) | |
| left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) | |
| im_new = np.full((new_unpad[1] + top + bottom, new_unpad[0] + left + right, 3), color, dtype=np.float32) | |
| im_new[top:new_unpad[1] + top, left:new_unpad[0] + left] = im | |
| return im_new | |
| def nms(pred, conf_thres, iou_thres, max_instance=20): # pred (anchor_num, 5 + cls_num) | |
| nc = pred.shape[1] - 5 | |
| candidates = [list() for x in range(nc)] | |
| for x in pred: | |
| if x[4] < conf_thres: | |
| continue | |
| cls = np.argmax(x[5:]) | |
| p = x[4] * x[5 + cls] | |
| if conf_thres <= p: | |
| box = (x[0] - x[2] / 2, x[1] - x[3] / 2, x[0] + x[2] / 2, x[1] + x[3] / 2) # xywh2xyxy | |
| candidates[cls].append([p, box]) | |
| result = [list() for x in range(nc)] | |
| for i, candidate in enumerate(candidates): | |
| candidate = sorted(candidate, key=lambda a: a[0], reverse=True) | |
| candidate = candidate[:max_instance] | |
| for x in candidate: | |
| ok = True | |
| for r in result[i]: | |
| if iou(r[1], x[1]) > iou_thres: | |
| ok = False | |
| break | |
| if ok: | |
| result[i].append(x) | |
| return result | |
| class Model: | |
| def __init__(self): | |
| self.detector = None | |
| self.encoder = None | |
| self.g_synthesis = None | |
| self.g_mapping = None | |
| self.detector_stride = None | |
| self.detector_imgsz = None | |
| self.detector_class_names = None | |
| self.anime_seg = None | |
| self.w_avg = None | |
| self.load_models() | |
| def load_models(self): | |
| g_mapping_path = huggingface_hub.hf_hub_download("skytnt/fbanime-gan", "g_mapping.onnx") | |
| g_synthesis_path = huggingface_hub.hf_hub_download("skytnt/fbanime-gan", "g_synthesis.onnx") | |
| encoder_path = huggingface_hub.hf_hub_download("skytnt/fbanime-gan", "encoder.onnx") | |
| detector_path = huggingface_hub.hf_hub_download("skytnt/fbanime-gan", "waifu_dect.onnx") | |
| anime_seg_path = huggingface_hub.hf_hub_download("skytnt/anime-seg", "isnetis.onnx") | |
| providers = ['CPUExecutionProvider'] | |
| gpu_providers = ['CUDAExecutionProvider'] | |
| g_mapping = onnx.load(g_mapping_path) | |
| w_avg = [x for x in g_mapping.graph.initializer if x.name == "w_avg"][0] | |
| w_avg = np.frombuffer(w_avg.raw_data, dtype=np.float32)[np.newaxis, :] | |
| w_avg = w_avg.repeat(16, axis=0)[np.newaxis, :] | |
| self.w_avg = w_avg | |
| self.g_mapping = rt.InferenceSession(g_mapping_path, providers=gpu_providers + providers) | |
| self.g_synthesis = rt.InferenceSession(g_synthesis_path, providers=gpu_providers + providers) | |
| self.encoder = rt.InferenceSession(encoder_path, providers=providers) | |
| self.detector = rt.InferenceSession(detector_path, providers=providers) | |
| detector_meta = self.detector.get_modelmeta().custom_metadata_map | |
| self.detector_stride = int(detector_meta['stride']) | |
| self.detector_imgsz = 1088 | |
| self.detector_class_names = eval(detector_meta['names']) | |
| self.anime_seg = rt.InferenceSession(anime_seg_path, providers=providers) | |
| def get_img(self, w, noise=0): | |
| img = self.g_synthesis.run(None, {'w': w, "noise": np.asarray([noise], dtype=np.float32)})[0] | |
| return (img.transpose(0, 2, 3, 1) * 127.5 + 128).clip(0, 255).astype(np.uint8)[0] | |
| def get_w(self, z, psi1, psi2): | |
| return self.g_mapping.run(None, {'z': z, 'psi': np.asarray([psi1, psi2], dtype=np.float32)})[0] | |
| def remove_bg(self, img, s=1024): | |
| img0 = img | |
| img = (img / 255).astype(np.float32) | |
| h, w = h0, w0 = img.shape[:-1] | |
| h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s) | |
| ph, pw = s - h, s - w | |
| img_input = np.zeros([s, s, 3], dtype=np.float32) | |
| img_input[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] = transform.resize(img, (h, w)) | |
| img_input = np.transpose(img_input, (2, 0, 1)) | |
| img_input = img_input[np.newaxis, :] | |
| mask = self.anime_seg.run(None, {'img': img_input})[0][0] | |
| mask = np.transpose(mask, (1, 2, 0)) | |
| mask = mask[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] | |
| mask = transform.resize(mask, (h0, w0)) | |
| img0 = (img0 * mask + 255 * (1 - mask)).astype(np.uint8) | |
| return img0 | |
| def encode_img(self, img): | |
| img = transform.resize(((img / 255 - 0.5) / 0.5), (256, 256)).transpose(2, 0, 1)[np.newaxis, :].astype( | |
| np.float32) | |
| return self.encoder.run(None, {'img': img})[0] + self.w_avg | |
| def detect(self, im0, conf_thres, iou_thres, detail=False): | |
| if im0 is None: | |
| return [] | |
| img = letterbox((im0 / 255).astype(np.float32), (self.detector_imgsz, self.detector_imgsz), | |
| stride=self.detector_stride) | |
| # Convert | |
| img = img.transpose(2, 0, 1) | |
| img = img[np.newaxis, :] | |
| pred = self.detector.run(None, {'images': img})[0][0] | |
| dets = nms(pred, conf_thres, iou_thres) | |
| imgs = [] | |
| # Print results | |
| s = '%gx%g ' % img.shape[2:] # print string | |
| for i, det in enumerate(dets): | |
| n = len(det) | |
| s += f"{n} {self.detector_class_names[i]}{'s' * (n > 1)}, " # add to string | |
| if detail: | |
| print(s) | |
| waifu_rects = [] | |
| head_rects = [] | |
| body_rects = [] | |
| for i, det in enumerate(dets): | |
| for x in det: | |
| # Rescale boxes from img_size to im0 size | |
| wr = im0.shape[1] / img.shape[3] | |
| hr = im0.shape[0] / img.shape[2] | |
| x[1] = (int(x[1][0] * wr), int(x[1][1] * hr), | |
| int(x[1][2] * wr), int(x[1][3] * hr)) | |
| if i == 0: | |
| head_rects.append(x[1]) | |
| elif i == 1: | |
| body_rects.append(x[1]) | |
| elif i == 2: | |
| waifu_rects.append(x[1]) | |
| for j, waifu_rect in enumerate(waifu_rects): | |
| msg = f'waifu {j + 1} ' | |
| head_num = 0 | |
| body_num = 0 | |
| hr, br = None, None | |
| for r in head_rects: | |
| if get_inter(r, waifu_rect) / ((r[2] - r[0]) * (r[3] - r[1])) > 0.75: | |
| hr = r | |
| head_num += 1 | |
| if head_num != 1: | |
| if detail: | |
| print(msg + f'head num error: {head_num}') | |
| continue | |
| for r in body_rects: | |
| if get_inter(r, waifu_rect) / ((r[2] - r[0]) * (r[3] - r[1])) > 0.65: | |
| br = r | |
| body_num += 1 | |
| if body_num != 1: | |
| if detail: | |
| print(msg + f'body num error: {body_num}') | |
| continue | |
| bounds = (min(waifu_rect[0], hr[0], br[0]), | |
| min(waifu_rect[1], hr[1], br[1]), | |
| max(waifu_rect[2], hr[2], br[2]), | |
| max(waifu_rect[3], hr[3], br[3])) | |
| if (bounds[2] - bounds[0]) / (bounds[3] - bounds[1]) > 0.7: | |
| if detail: | |
| print(msg + "ratio out of limit") | |
| continue | |
| expand_pixel = (bounds[3] - bounds[1]) // 20 | |
| bounds = [max(bounds[0] - expand_pixel // 2, 0), | |
| max(bounds[1] - expand_pixel, 0), | |
| min(bounds[2] + expand_pixel // 2, im0.shape[1]), | |
| min(bounds[3] + expand_pixel, im0.shape[0]), | |
| ] | |
| # corp and resize | |
| w = bounds[2] - bounds[0] | |
| h = bounds[3] - bounds[1] | |
| bounds[3] += h % 2 | |
| h += h % 2 | |
| r = min(512 / w, 1024 / h) | |
| pw, ph = int(512 / r - w), int(1024 / r - h) | |
| bounds_tmp = (bounds[0] - pw // 2, bounds[1] - ph // 2, | |
| bounds[2] + pw // 2 + pw % 2, bounds[3] + ph // 2 + ph % 2) | |
| bounds = (max(0, bounds_tmp[0]), max(0, bounds_tmp[1]), | |
| min(im0.shape[1], bounds_tmp[2]), min(im0.shape[0], bounds_tmp[3])) | |
| dl = bounds[0] - bounds_tmp[0] | |
| dr = bounds[2] - bounds_tmp[2] | |
| dt = bounds[1] - bounds_tmp[1] | |
| db = bounds[3] - bounds_tmp[3] | |
| w = bounds_tmp[2] - bounds_tmp[0] | |
| h = bounds_tmp[3] - bounds_tmp[1] | |
| temp_img = np.full((h, w, 3), 255, dtype=np.uint8) | |
| temp_img[dt:h + db, dl:w + dr] = im0[bounds[1]:bounds[3], bounds[0]:bounds[2]] | |
| temp_img = transform.resize(temp_img, (1024, 512), preserve_range=True).astype(np.uint8) | |
| imgs.append(temp_img) | |
| return imgs | |
| def gen_video(self, w1, w2, noise, path, frame_num=10): | |
| video = imageio.get_writer(path, mode='I', fps=frame_num // 2, codec='libx264', bitrate='16M') | |
| lin = np.linspace(0, 1, frame_num) | |
| for i in range(0, frame_num): | |
| img = self.get_img(((1 - lin[i]) * w1) + (lin[i] * w2), noise) | |
| video.append_data(img) | |
| for i in reversed(range(0, frame_num)): | |
| img = self.get_img(((1 - lin[i]) * w1) + (lin[i] * w2), noise) | |
| video.append_data(img) | |
| video.close() | |
| def get_thumbnail(img): | |
| img_new = np.full((256, 384, 3), 200, dtype=np.uint8) | |
| img_new[:, 128:256] = transform.resize(img, (256, 128), preserve_range=True) | |
| return img_new | |
| def gen_fn(seed, random_seed, psi1, psi2, noise): | |
| if random_seed: | |
| seed = random.randint(0, 2 ** 32 - 1) | |
| z = RandomState(int(seed)).randn(1, 1024) | |
| w = model.get_w(z.astype(dtype=np.float32), psi1, psi2) | |
| img_out = model.get_img(w, noise) | |
| return img_out, seed, w, get_thumbnail(img_out) | |
| def encode_img_fn(img, noise): | |
| if img is None: | |
| return "please upload a image", None, None, None, None | |
| img = model.remove_bg(img) | |
| imgs = model.detect(img, 0.2, 0.03) | |
| if len(imgs) == 0: | |
| return "failed to detect anime character", None, None, None, None | |
| w = model.encode_img(imgs[0]) | |
| img_out = model.get_img(w, noise) | |
| return "success", imgs[0], img_out, w, get_thumbnail(img_out) | |
| def gen_video_fn(w1, w2, noise, frame): | |
| if w1 is None or w2 is None: | |
| return None | |
| model.gen_video(w1, w2, noise, "video.mp4", int(frame)) | |
| return "video.mp4" | |
| if __name__ == '__main__': | |
| model = Model() | |
| app = gr.Blocks() | |
| with app: | |
| gr.Markdown("# full-body anime GAN\n\n" | |
| "\n\n") | |
| with gr.Tabs(): | |
| with gr.TabItem("generate image"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown("generate image") | |
| with gr.Row(): | |
| gen_input1 = gr.Slider(minimum=0, maximum=2 ** 32 - 1, step=1, value=0, label="seed") | |
| gen_input2 = gr.Checkbox(label="Random", value=True) | |
| gen_input3 = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.7, label="truncation psi 1") | |
| gen_input4 = gr.Slider(minimum=0, maximum=1, step=0.01, value=1, label="truncation psi 2") | |
| gen_input5 = gr.Slider(minimum=0, maximum=1, step=0.01, value=1, label="noise strength") | |
| with gr.Group(): | |
| gen_submit = gr.Button("Generate", variant="primary") | |
| with gr.Column(): | |
| gen_output1 = gr.Image(label="output image") | |
| select_img_input_w1 = gr.Variable() | |
| select_img_input_img1 = gr.Variable() | |
| with gr.TabItem("encode image"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown("you'd better upload a standing full-body image") | |
| encode_img_input = gr.Image(label="input image") | |
| examples_data = [[f"examples/{x:02d}.jpg"] for x in range(1, 5)] | |
| encode_img_examples = gr.Dataset(components=[encode_img_input], samples=examples_data) | |
| with gr.Group(): | |
| encode_img_submit = gr.Button("Run", variant="primary") | |
| with gr.Column(): | |
| encode_img_output1 = gr.Textbox(label="output message") | |
| with gr.Row(): | |
| encode_img_output2 = gr.Image(label="detected") | |
| encode_img_output3 = gr.Image(label="encoded") | |
| select_img_input_w2 = gr.Variable() | |
| select_img_input_img2 = gr.Variable() | |
| with gr.TabItem("generate video"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown("generate video between 2 images") | |
| with gr.Row(): | |
| with gr.Column(): | |
| select_img1_dropdown = gr.Radio(label="Select image 1", value="current generated image", | |
| choices=["current generated image", | |
| "current encoded image"], type="index") | |
| with gr.Group(): | |
| select_img1_button = gr.Button("Select", variant="primary") | |
| select_img1_output_img = gr.Image(label="selected image 1") | |
| select_img1_output_w = gr.Variable() | |
| with gr.Column(): | |
| select_img2_dropdown = gr.Radio(label="Select image 2", value="current generated image", | |
| choices=["current generated image", | |
| "current encoded image"], type="index") | |
| with gr.Group(): | |
| select_img2_button = gr.Button("Select", variant="primary") | |
| select_img2_output_img = gr.Image(label="selected image 2") | |
| select_img2_output_w = gr.Variable() | |
| generate_video_frame = gr.Slider(minimum=10, maximum=30, step=1, label="frame", value=15) | |
| with gr.Group(): | |
| generate_video_button = gr.Button("Generate", variant="primary") | |
| with gr.Column(): | |
| generate_video_output = gr.Video(label="output video") | |
| gen_submit.click(gen_fn, [gen_input1, gen_input2, gen_input3, gen_input4, gen_input5], | |
| [gen_output1, gen_input1, select_img_input_w1, select_img_input_img1]) | |
| encode_img_submit.click(encode_img_fn, [encode_img_input, gen_input5], | |
| [encode_img_output1, encode_img_output2, encode_img_output3, select_img_input_w2, | |
| select_img_input_img2]) | |
| encode_img_examples.click(lambda x: x[0], [encode_img_examples], [encode_img_input]) | |
| select_img1_button.click(lambda i, img1, img2, w1, w2: (img1, w1) if i == 0 else (img2, w2), | |
| [select_img1_dropdown, select_img_input_img1, select_img_input_img2, | |
| select_img_input_w1, select_img_input_w2], | |
| [select_img1_output_img, select_img1_output_w]) | |
| select_img2_button.click(lambda i, img1, img2, w1, w2: (img1, w1) if i == 0 else (img2, w2), | |
| [select_img2_dropdown, select_img_input_img1, select_img_input_img2, | |
| select_img_input_w1, select_img_input_w2], | |
| [select_img2_output_img, select_img2_output_w]) | |
| generate_video_button.click(gen_video_fn, | |
| [select_img1_output_w, select_img2_output_w, gen_input5, generate_video_frame], | |
| [generate_video_output]) | |
| app.launch() | |