init

app.py

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+import gradio as gr
+import imageio
+import numpy as np
+import onnx
+import onnxruntime as rt
+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[0] * r)), int(round(shape[1] * r))
+    dw, dh = new_shape[1] - new_unpad[1], new_shape[0] - new_unpad[0]  # 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)
+    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[0] + top + bottom, new_unpad[1] + left + right, 3), color, dtype=np.float32)
+    im_new[top:new_unpad[0] + top, left:new_unpad[1] + left] = im
+    return im
+
+
+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.img_avg = None
+        self.detector = None
+        self.encoder = None
+        self.g_synthesis = None
+        self.g_mapping = None
+        self.w_avg = None
+        self.detector_stride = None
+        self.detector_imgsz = None
+        self.detector_class_names = None
+        self.load_models("./models/")
+
+    def load_models(self, model_dir):
+        providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
+        g_mapping = onnx.load(model_dir + "g_mapping.onnx")
+        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(model_dir + "g_mapping.onnx", providers=providers)
+        self.g_synthesis = rt.InferenceSession(model_dir + "g_synthesis.onnx", providers=providers)
+        self.encoder = rt.InferenceSession(model_dir + "fb_encoder.onnx", providers=providers)
+        self.detector = rt.InferenceSession(model_dir + "waifu_dect.onnx", 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.img_avg = transform.resize(self.g_synthesis.run(None, {'w': w_avg})[0][0].transpose(1, 2, 0),
+                                        (256, 256)).transpose(2, 0, 1)[np.newaxis, :]
+
+    def get_img(self, w):
+        img = self.g_synthesis.run(None, {'w': w})[0]
+        return (img.transpose(0, 2, 3, 1) * 127.5 + 128).clip(0, 255).astype(np.uint8)[0]
+
+    def get_w(self, z, psi):
+        return self.g_mapping.run(None, {'z': z, 'psi': np.asarray([psi], dtype=np.float32)})[0]
+
+    def encode_img(self, img, iteration=5):
+        target_img = transform.resize(((img / 255 - 0.5) / 0.5), (256, 256)).transpose(2, 0, 1)[np.newaxis, :].astype(
+            np.float32)
+        w = self.w_avg.copy()
+        from_img = self.img_avg.copy()
+        for i in range(iteration):
+            dimg = np.concatenate([target_img, from_img], axis=1)
+            dw = self.encoder.run(None, {'dimg': dimg})[0]
+            w += dw
+            from_img = transform.resize(self.g_synthesis.run(None, {'w': w})[0][0].transpose(1, 2, 0),
+                                        (256, 256)).transpose(2, 0, 1)[np.newaxis, :]
+        return w
+
+    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 = (int(min(waifu_rect[0], hr[0], br[0])),
+                      int(min(waifu_rect[1], hr[1], br[1])),
+                      int(max(waifu_rect[2], hr[2], br[2])),
+                      int(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
+            # 扩展边界
+            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]),
+                      )
+            if bounds[3] - bounds[1] >= (bounds[2] - bounds[0]) * 2:  # 等高度剪裁
+                cx = (bounds[2] + bounds[0]) // 2
+                h = bounds[3] - bounds[1]
+                w = h // 2
+                w2 = w // 2
+                l1 = max(cx - w2, 0)
+                r1 = min(cx + w2, im0.shape[1])
+                bounds = (l1, bounds[1], r1, bounds[3])
+                temp_bound = (w2 - (cx - l1), 0, w2 + (r1 - cx), h)
+            else:  # 等宽度剪裁
+                cy = (bounds[3] + bounds[1]) // 2
+                w = bounds[2] - bounds[0]
+                h = w * 2
+                h2 = h // 2
+                tp1 = max(cy - h2, 0)
+                b1 = min(cy + h2, im0.shape[0])
+                bounds = (bounds[0], tp1, bounds[2], b1)
+                temp_bound = (0, h2 - (cy - tp1), w, h2 + (b1 - cy))
+            temp_img = np.full((h, w, 3), 255, dtype=np.uint8)
+            temp_img[temp_bound[1]:temp_bound[3], temp_bound[0]:temp_bound[2]] = im0[bounds[1]:bounds[3],
+                                                                                 bounds[0]:bounds[2]]
+            temp_img = transform.resize(temp_img, (1024, 512), preserve_range=True).astype(np.uint8)
+            print(temp_img.min(), temp_img.max())
+            imgs.append(temp_img)
+        return imgs
+
+    def gen_video(self, w1, w2, 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))
+            video.append_data(img)
+        video.close()
+
+
+def gen_fn(use_seed, seed, psi):
+    z = RandomState(int(seed) + 2 ** 31).randn(1, 512) if use_seed else np.random.randn(1, 512)
+    w = model.get_w(z.astype(dtype=np.float32), psi)
+    img_out = model.get_img(w)
+    return img_out, w.tolist(), img_out
+
+
+def encode_img_fn(img):
+    imgs = model.detect(img, 0.2, 0.03)
+    if len(imgs) == 0:
+        return "failed to detect waifu", None, None
+    w = model.encode_img(imgs[0])
+    img_out = model.get_img(w)
+    return "success", imgs[0], img_out, w.tolist(), img_out
+
+
+def gen_video_fn(w1, w2, frame):
+    if w1 is None or w2 is None:
+        return None
+    model.gen_video(np.array(w1, dtype=np.float32), np.array(w2, dtype=np.float32), "video.mp4", int(frame))
+    return "video.mp4"
+
+
+if __name__ == '__main__':
+    model = Model()
+
+    app = gr.Blocks()
+    with app:
+        gr.Markdown("# full-body anime\n\n"
+                    "the model is not good, just for fun.")
+        with gr.Tabs():
+            with gr.TabItem("generate image"):
+                with gr.Column():
+                    with gr.Row():
+                        gen_input1 = gr.Checkbox(value=False, label="use seed")
+                        gen_input2 = gr.Number(value=1, label="seed")
+                    gen_input3 = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.6, label="psi")
+                    gen_submit = gr.Button("Run")
+                    gen_output1 = gr.Image()
+                    select_img_input_w1 = gr.Json(visible=False)
+                    select_img_input_img1 = gr.Image(visible=False)
+
+            with gr.TabItem("encode image"):
+                with gr.Column():
+                    encode_img_input = gr.Image()
+                    encode_img_submit = gr.Button("Run")
+                    encode_img_output1 = gr.Textbox(label="message")
+                    with gr.Row():
+                        encode_img_output2 = gr.Image(label="detected")
+                        encode_img_output3 = gr.Image(label="encoded")
+                    select_img_input_w2 = gr.Json(visible=False)
+                    select_img_input_img2 = gr.Image(visible=False)
+
+            with gr.TabItem("generate video"):
+                with gr.Column():
+                    gr.Markdown("## generate video between 2 images")
+                    with gr.Row():
+                        with gr.Column():
+                            gr.Markdown("please select image 1")
+                            select_img1_dropdown = gr.Dropdown(label="source",
+                                                               choices=["current generated image",
+                                                                        "current encoded image"], type="index")
+                            select_img1_button = gr.Button("select")
+                            select_img1_output_img = gr.Image(label="image 1")
+                            select_img1_output_w = gr.Json(visible=False)
+                        with gr.Column():
+                            gr.Markdown("please select image 2")
+                            select_img2_dropdown = gr.Dropdown(label="source",
+                                                               choices=["current generated image",
+                                                                        "current encoded image"], type="index")
+                            select_img2_button = gr.Button("select")
+                            select_img2_output_img = gr.Image(label="image 2")
+                            select_img2_output_w = gr.Json(visible=False)
+                    generate_video_frame = gr.Slider(minimum=10, maximum=30, step=1, label="frame", value=10)
+                    generate_video_button = gr.Button("generate")
+                    generate_video_output = gr.Video()
+
+        gen_submit.click(gen_fn, [gen_input1, gen_input2, gen_input3],
+                         [gen_output1, select_img_input_w1, select_img_input_img1])
+        encode_img_submit.click(encode_img_fn, [encode_img_input],
+                                [encode_img_output1, encode_img_output2, encode_img_output3, select_img_input_w2,
+                                 select_img_input_img2])
+        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, generate_video_frame],
+                                    [generate_video_output])
+
+    app.launch()

models/fb_encoder.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5be6b51e6f5046c6b402f4b0af62903363891571c72fd576afcf8d64f9e71dd9
+size 706131142

models/g_mapping.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7e8b35dc40e6ba4f08cb269b56617814c8406418a76965ae1f9f8ea41813fc30
+size 8411020

models/g_synthesis.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bacba02853729b8c9635c83c6194943158c5b992f742123aa1ce6cbd94c138f7
+size 112794026

models/waifu_dect.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4a5de6949912bf94c3307f2b18ebc7b49f309e713b1799d29805ccd882e327d3
+size 83550422

requirements.txt

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+onnx
+onnxruntime-gpu
+scikit-image
+imageio-ffmpeg