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app.py

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+# --------------------------------------------------------
+# PersonalizeSAM -- Personalize Segment Anything Model with One Shot
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------
+from PIL import Image
+import torch
+import torch.nn as nn
+import gradio as gr
+import numpy as np
+from torch.nn import functional as F
+
+from show import *
+from per_segment_anything import sam_model_registry, SamPredictor
+
+
+class ImageMask(gr.components.Image):
+    """
+    Sets: source="canvas", tool="sketch"
+    """
+
+    is_template = True
+
+    def __init__(self, **kwargs):
+        super().__init__(source="upload", tool="sketch", interactive=True, **kwargs)
+
+    def preprocess(self, x):
+        return super().preprocess(x)
+
+
+class Mask_Weights(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.weights = nn.Parameter(torch.ones(2, 1, requires_grad=True) / 3)
+
+
+def point_selection(mask_sim, topk=1):
+    # Top-1 point selection
+    w, h = mask_sim.shape
+    topk_xy = mask_sim.flatten(0).topk(topk)[1]
+    topk_x = (topk_xy // h).unsqueeze(0)
+    topk_y = (topk_xy - topk_x * h)
+    topk_xy = torch.cat((topk_y, topk_x), dim=0).permute(1, 0)
+    topk_label = np.array([1] * topk)
+    topk_xy = topk_xy.cpu().numpy()
+
+    # Top-last point selection
+    last_xy = mask_sim.flatten(0).topk(topk, largest=False)[1]
+    last_x = (last_xy // h).unsqueeze(0)
+    last_y = (last_xy - last_x * h)
+    last_xy = torch.cat((last_y, last_x), dim=0).permute(1, 0)
+    last_label = np.array([0] * topk)
+    last_xy = last_xy.cpu().numpy()
+
+    return topk_xy, topk_label, last_xy, last_label
+
+
+def calculate_dice_loss(inputs, targets, num_masks = 1):
+    """
+    Compute the DICE loss, similar to generalized IOU for masks
+    Args:
+        inputs: A float tensor of arbitrary shape.
+                The predictions for each example.
+        targets: A float tensor with the same shape as inputs. Stores the binary
+                 classification label for each element in inputs
+                (0 for the negative class and 1 for the positive class).
+    """
+    inputs = inputs.sigmoid()
+    inputs = inputs.flatten(1)
+    numerator = 2 * (inputs * targets).sum(-1)
+    denominator = inputs.sum(-1) + targets.sum(-1)
+    loss = 1 - (numerator + 1) / (denominator + 1)
+    return loss.sum() / num_masks
+
+
+def calculate_sigmoid_focal_loss(inputs, targets, num_masks = 1, alpha: float = 0.25, gamma: float = 2):
+    """
+    Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.
+    Args:
+        inputs: A float tensor of arbitrary shape.
+                The predictions for each example.
+        targets: A float tensor with the same shape as inputs. Stores the binary
+                 classification label for each element in inputs
+                (0 for the negative class and 1 for the positive class).
+        alpha: (optional) Weighting factor in range (0,1) to balance
+                positive vs negative examples. Default = -1 (no weighting).
+        gamma: Exponent of the modulating factor (1 - p_t) to
+               balance easy vs hard examples.
+    Returns:
+        Loss tensor
+    """
+    prob = inputs.sigmoid()
+    ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none")
+    p_t = prob * targets + (1 - prob) * (1 - targets)
+    loss = ce_loss * ((1 - p_t) ** gamma)
+
+    if alpha >= 0:
+        alpha_t = alpha * targets + (1 - alpha) * (1 - targets)
+        loss = alpha_t * loss
+
+    return loss.mean(1).sum() / num_masks
+
+
+def inference(ic_image, ic_mask, image1, image2):
+    # in context image and mask
+    ic_image = np.array(ic_image.convert("RGB"))
+    ic_mask = np.array(ic_mask.convert("RGB"))
+
+    sam_type, sam_ckpt = 'vit_h', 'sam_vit_h_4b8939.pth'
+    sam = sam_model_registry[sam_type](checkpoint=sam_ckpt).cuda()
+    # sam = sam_model_registry[sam_type](checkpoint=sam_ckpt)
+    predictor = SamPredictor(sam)
+
+    # Image features encoding
+    ref_mask = predictor.set_image(ic_image, ic_mask)
+    ref_feat = predictor.features.squeeze().permute(1, 2, 0)
+
+    ref_mask = F.interpolate(ref_mask, size=ref_feat.shape[0: 2], mode="bilinear")
+    ref_mask = ref_mask.squeeze()[0]
+
+    # Target feature extraction
+    print("======> Obtain Location Prior" )
+    target_feat = ref_feat[ref_mask > 0]
+    target_embedding = target_feat.mean(0).unsqueeze(0)
+    target_feat = target_embedding / target_embedding.norm(dim=-1, keepdim=True)
+    target_embedding = target_embedding.unsqueeze(0)
+
+    output_image = []
+
+    for test_image in [image1, image2]:
+        print("======> Testing Image" )
+        test_image = np.array(test_image.convert("RGB"))
+
+        # Image feature encoding
+        predictor.set_image(test_image)
+        test_feat = predictor.features.squeeze()
+
+        # Cosine similarity
+        C, h, w = test_feat.shape
+        test_feat = test_feat / test_feat.norm(dim=0, keepdim=True)
+        test_feat = test_feat.reshape(C, h * w)
+        sim = target_feat @ test_feat
+
+        sim = sim.reshape(1, 1, h, w)
+        sim = F.interpolate(sim, scale_factor=4, mode="bilinear")
+        sim = predictor.model.postprocess_masks(
+            sim,
+            input_size=predictor.input_size,
+            original_size=predictor.original_size).squeeze()
+
+        # Positive-negative location prior
+        topk_xy_i, topk_label_i, last_xy_i, last_label_i = point_selection(sim, topk=1)
+        topk_xy = np.concatenate([topk_xy_i, last_xy_i], axis=0)
+        topk_label = np.concatenate([topk_label_i, last_label_i], axis=0)
+
+        # Obtain the target guidance for cross-attention layers
+        sim = (sim - sim.mean()) / torch.std(sim)
+        sim = F.interpolate(sim.unsqueeze(0).unsqueeze(0), size=(64, 64), mode="bilinear")
+        attn_sim = sim.sigmoid_().unsqueeze(0).flatten(3)
+
+        # First-step prediction
+        masks, scores, logits, _ = predictor.predict(
+            point_coords=topk_xy,
+            point_labels=topk_label,
+            multimask_output=False,
+            attn_sim=attn_sim,  # Target-guided Attention
+            target_embedding=target_embedding  # Target-semantic Prompting
+        )
+        best_idx = 0
+
+        # Cascaded Post-refinement-1
+        masks, scores, logits, _ = predictor.predict(
+                    point_coords=topk_xy,
+                    point_labels=topk_label,
+                    mask_input=logits[best_idx: best_idx + 1, :, :],
+                    multimask_output=True)
+        best_idx = np.argmax(scores)
+
+        # Cascaded Post-refinement-2
+        y, x = np.nonzero(masks[best_idx])
+        x_min = x.min()
+        x_max = x.max()
+        y_min = y.min()
+        y_max = y.max()
+        input_box = np.array([x_min, y_min, x_max, y_max])
+        masks, scores, logits, _ = predictor.predict(
+            point_coords=topk_xy,
+            point_labels=topk_label,
+            box=input_box[None, :],
+            mask_input=logits[best_idx: best_idx + 1, :, :],
+            multimask_output=True)
+        best_idx = np.argmax(scores)
+
+        final_mask = masks[best_idx]
+        mask_colors = np.zeros((final_mask.shape[0], final_mask.shape[1], 3), dtype=np.uint8)
+        mask_colors[final_mask, :] = np.array([[128, 0, 0]])
+        output_image.append(Image.fromarray((mask_colors * 0.6 + test_image * 0.4).astype('uint8'), 'RGB'))
+
+    return output_image[0].resize((224, 224)), output_image[1].resize((224, 224))
+
+
+def inference_scribble(image, image1, image2):
+    # in context image and mask
+    ic_image = image["image"]
+    ic_mask = image["mask"]
+    ic_image = np.array(ic_image.convert("RGB"))
+    ic_mask = np.array(ic_mask.convert("RGB"))
+
+    sam_type, sam_ckpt = 'vit_h', 'sam_vit_h_4b8939.pth'
+    sam = sam_model_registry[sam_type](checkpoint=sam_ckpt).cuda()
+    # sam = sam_model_registry[sam_type](checkpoint=sam_ckpt)
+    predictor = SamPredictor(sam)
+
+    # Image features encoding
+    ref_mask = predictor.set_image(ic_image, ic_mask)
+    ref_feat = predictor.features.squeeze().permute(1, 2, 0)
+
+    ref_mask = F.interpolate(ref_mask, size=ref_feat.shape[0: 2], mode="bilinear")
+    ref_mask = ref_mask.squeeze()[0]
+
+    # Target feature extraction
+    print("======> Obtain Location Prior" )
+    target_feat = ref_feat[ref_mask > 0]
+    target_embedding = target_feat.mean(0).unsqueeze(0)
+    target_feat = target_embedding / target_embedding.norm(dim=-1, keepdim=True)
+    target_embedding = target_embedding.unsqueeze(0)
+
+    output_image = []
+
+    for test_image in [image1, image2]:
+        print("======> Testing Image" )
+        test_image = np.array(test_image.convert("RGB"))
+
+        # Image feature encoding
+        predictor.set_image(test_image)
+        test_feat = predictor.features.squeeze()
+
+        # Cosine similarity
+        C, h, w = test_feat.shape
+        test_feat = test_feat / test_feat.norm(dim=0, keepdim=True)
+        test_feat = test_feat.reshape(C, h * w)
+        sim = target_feat @ test_feat
+
+        sim = sim.reshape(1, 1, h, w)
+        sim = F.interpolate(sim, scale_factor=4, mode="bilinear")
+        sim = predictor.model.postprocess_masks(
+            sim,
+            input_size=predictor.input_size,
+            original_size=predictor.original_size).squeeze()
+
+        # Positive-negative location prior
+        topk_xy_i, topk_label_i, last_xy_i, last_label_i = point_selection(sim, topk=1)
+        topk_xy = np.concatenate([topk_xy_i, last_xy_i], axis=0)
+        topk_label = np.concatenate([topk_label_i, last_label_i], axis=0)
+
+        # Obtain the target guidance for cross-attention layers
+        sim = (sim - sim.mean()) / torch.std(sim)
+        sim = F.interpolate(sim.unsqueeze(0).unsqueeze(0), size=(64, 64), mode="bilinear")
+        attn_sim = sim.sigmoid_().unsqueeze(0).flatten(3)
+
+        # First-step prediction
+        masks, scores, logits, _ = predictor.predict(
+            point_coords=topk_xy,
+            point_labels=topk_label,
+            multimask_output=False,
+            attn_sim=attn_sim,  # Target-guided Attention
+            target_embedding=target_embedding  # Target-semantic Prompting
+        )
+        best_idx = 0
+
+        # Cascaded Post-refinement-1
+        masks, scores, logits, _ = predictor.predict(
+                    point_coords=topk_xy,
+                    point_labels=topk_label,
+                    mask_input=logits[best_idx: best_idx + 1, :, :],
+                    multimask_output=True)
+        best_idx = np.argmax(scores)
+
+        # Cascaded Post-refinement-2
+        y, x = np.nonzero(masks[best_idx])
+        x_min = x.min()
+        x_max = x.max()
+        y_min = y.min()
+        y_max = y.max()
+        input_box = np.array([x_min, y_min, x_max, y_max])
+        masks, scores, logits, _ = predictor.predict(
+            point_coords=topk_xy,
+            point_labels=topk_label,
+            box=input_box[None, :],
+            mask_input=logits[best_idx: best_idx + 1, :, :],
+            multimask_output=True)
+        best_idx = np.argmax(scores)
+
+        final_mask = masks[best_idx]
+        mask_colors = np.zeros((final_mask.shape[0], final_mask.shape[1], 3), dtype=np.uint8)
+        mask_colors[final_mask, :] = np.array([[128, 0, 0]])
+        output_image.append(Image.fromarray((mask_colors * 0.6 + test_image * 0.4).astype('uint8'), 'RGB'))
+
+    return output_image[0].resize((224, 224)), output_image[1].resize((224, 224))
+
+
+def inference_finetune(ic_image, ic_mask, image1, image2):
+    # in context image and mask
+    ic_image = np.array(ic_image.convert("RGB"))
+    ic_mask = np.array(ic_mask.convert("RGB"))
+
+    gt_mask = torch.tensor(ic_mask)[:, :, 0] > 0
+    gt_mask = gt_mask.float().unsqueeze(0).flatten(1).cuda()
+    # gt_mask = gt_mask.float().unsqueeze(0).flatten(1)
+
+    sam_type, sam_ckpt = 'vit_h', 'sam_vit_h_4b8939.pth'
+    sam = sam_model_registry[sam_type](checkpoint=sam_ckpt).cuda()
+    # sam = sam_model_registry[sam_type](checkpoint=sam_ckpt)
+    for name, param in sam.named_parameters():
+        param.requires_grad = False
+    predictor = SamPredictor(sam)
+
+    print("======> Obtain Self Location Prior" )
+    # Image features encoding
+    ref_mask = predictor.set_image(ic_image, ic_mask)
+    ref_feat = predictor.features.squeeze().permute(1, 2, 0)
+
+    ref_mask = F.interpolate(ref_mask, size=ref_feat.shape[0: 2], mode="bilinear")
+    ref_mask = ref_mask.squeeze()[0]
+
+    # Target feature extraction
+    target_feat = ref_feat[ref_mask > 0]
+    target_feat_mean = target_feat.mean(0)
+    target_feat_max = torch.max(target_feat, dim=0)[0]
+    target_feat = (target_feat_max / 2 + target_feat_mean / 2).unsqueeze(0)
+
+    # Cosine similarity
+    h, w, C = ref_feat.shape
+    target_feat = target_feat / target_feat.norm(dim=-1, keepdim=True)
+    ref_feat = ref_feat / ref_feat.norm(dim=-1, keepdim=True)
+    ref_feat = ref_feat.permute(2, 0, 1).reshape(C, h * w)
+    sim = target_feat @ ref_feat
+
+    sim = sim.reshape(1, 1, h, w)
+    sim = F.interpolate(sim, scale_factor=4, mode="bilinear")
+    sim = predictor.model.postprocess_masks(
+                    sim,
+                    input_size=predictor.input_size,
+                    original_size=predictor.original_size).squeeze()
+
+    # Positive location prior
+    topk_xy, topk_label, _, _ = point_selection(sim, topk=1)
+
+    print('======> Start Training')
+    # Learnable mask weights
+    mask_weights = Mask_Weights().cuda()
+    # mask_weights = Mask_Weights()
+    mask_weights.train()
+    train_epoch = 1000
+    optimizer = torch.optim.AdamW(mask_weights.parameters(), lr=1e-3, eps=1e-4)
+    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, train_epoch)
+
+    for train_idx in range(train_epoch):
+        # Run the decoder
+        masks, scores, logits, logits_high = predictor.predict(
+            point_coords=topk_xy,
+            point_labels=topk_label,
+            multimask_output=True)
+        logits_high = logits_high.flatten(1)
+
+        # Weighted sum three-scale masks
+        weights = torch.cat((1 - mask_weights.weights.sum(0).unsqueeze(0), mask_weights.weights), dim=0)
+        logits_high = logits_high * weights
+        logits_high = logits_high.sum(0).unsqueeze(0)
+
+        dice_loss = calculate_dice_loss(logits_high, gt_mask)
+        focal_loss = calculate_sigmoid_focal_loss(logits_high, gt_mask)
+        loss = dice_loss + focal_loss
+
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+        scheduler.step()
+
+        if train_idx % 10 == 0:
+            print('Train Epoch: {:} / {:}'.format(train_idx, train_epoch))
+            current_lr = scheduler.get_last_lr()[0]
+            print('LR: {:.6f}, Dice_Loss: {:.4f}, Focal_Loss: {:.4f}'.format(current_lr, dice_loss.item(), focal_loss.item()))
+
+
+    mask_weights.eval()
+    weights = torch.cat((1 - mask_weights.weights.sum(0).unsqueeze(0), mask_weights.weights), dim=0)
+    weights_np = weights.detach().cpu().numpy()
+    print('======> Mask weights:\n', weights_np)
+
+    print('======> Start Testing')
+    output_image = []
+
+    for test_image in [image1, image2]:
+        test_image = np.array(test_image.convert("RGB"))
+
+        # Image feature encoding
+        predictor.set_image(test_image)
+        test_feat = predictor.features.squeeze()
+         # Image feature encoding
+        predictor.set_image(test_image)
+        test_feat = predictor.features.squeeze()
+
+        # Cosine similarity
+        C, h, w = test_feat.shape
+        test_feat = test_feat / test_feat.norm(dim=0, keepdim=True)
+        test_feat = test_feat.reshape(C, h * w)
+        sim = target_feat @ test_feat
+
+        sim = sim.reshape(1, 1, h, w)
+        sim = F.interpolate(sim, scale_factor=4, mode="bilinear")
+        sim = predictor.model.postprocess_masks(
+                        sim,
+                        input_size=predictor.input_size,
+                        original_size=predictor.original_size).squeeze()
+
+        # Positive location prior
+        topk_xy, topk_label, _, _ = point_selection(sim, topk=1)
+
+        # First-step prediction
+        masks, scores, logits, logits_high = predictor.predict(
+                    point_coords=topk_xy,
+                    point_labels=topk_label,
+                    multimask_output=True)
+
+        # Weighted sum three-scale masks
+        logits_high = logits_high * weights.unsqueeze(-1)
+        logit_high = logits_high.sum(0)
+        mask = (logit_high > 0).detach().cpu().numpy()
+
+        logits = logits * weights_np[..., None]
+        logit = logits.sum(0)
+
+        # Cascaded Post-refinement-1
+        y, x = np.nonzero(mask)
+        x_min = x.min()
+        x_max = x.max()
+        y_min = y.min()
+        y_max = y.max()
+        input_box = np.array([x_min, y_min, x_max, y_max])
+        masks, scores, logits, _ = predictor.predict(
+            point_coords=topk_xy,
+            point_labels=topk_label,
+            box=input_box[None, :],
+            mask_input=logit[None, :, :],
+            multimask_output=True)
+        best_idx = np.argmax(scores)
+
+        # Cascaded Post-refinement-2
+        y, x = np.nonzero(masks[best_idx])
+        x_min = x.min()
+        x_max = x.max()
+        y_min = y.min()
+        y_max = y.max()
+        input_box = np.array([x_min, y_min, x_max, y_max])
+        masks, scores, logits, _ = predictor.predict(
+            point_coords=topk_xy,
+            point_labels=topk_label,
+            box=input_box[None, :],
+            mask_input=logits[best_idx: best_idx + 1, :, :],
+            multimask_output=True)
+        best_idx = np.argmax(scores)
+
+        final_mask = masks[best_idx]
+        mask_colors = np.zeros((final_mask.shape[0], final_mask.shape[1], 3), dtype=np.uint8)
+        mask_colors[final_mask, :] = np.array([[128, 0, 0]])
+        output_image.append(Image.fromarray((mask_colors * 0.6 + test_image * 0.4).astype('uint8'), 'RGB'))
+
+    return output_image[0].resize((224, 224)), output_image[1].resize((224, 224))
+
+
+description = """
+<div style="text-align: center; font-weight: bold;">
+    <span style="font-size: 18px" id="paper-info">
+        [<a href="https://github.com/ZrrSkywalker/Personalize-SAM" target="_blank">GitHub</a>]
+        [<a href="https://arxiv.org/pdf/2305.03048.pdf" target="_blank">Paper</a>]
+    </span>
+</div>
+"""
+
+main = gr.Interface(
+    fn=inference,
+    inputs=[
+        gr.Image(type="pil", label="in context image",),
+        gr.Image(type="pil", label="in context mask"),
+        gr.Image(type="pil", label="test image1"),
+        gr.Image(type="pil", label="test image2"),
+    ],
+    outputs=[
+        gr.outputs.Image(type="pil", label="output image1"),
+        gr.outputs.Image(type="pil", label="output image2"),
+    ],
+    allow_flagging="never",
+    title="Personalize Segment Anything Model with 1 Shot",
+    description=description,
+    examples=[
+        ["./examples/cat_00.jpg", "./examples/cat_00.png", "./examples/cat_01.jpg", "./examples/cat_02.jpg"],
+        ["./examples/colorful_sneaker_00.jpg", "./examples/colorful_sneaker_00.png", "./examples/colorful_sneaker_01.jpg", "./examples/colorful_sneaker_02.jpg"],
+        ["./examples/duck_toy_00.jpg", "./examples/duck_toy_00.png", "./examples/duck_toy_01.jpg", "./examples/duck_toy_02.jpg"],
+    ]
+)
+
+main_scribble = gr.Interface(
+    fn=inference_scribble,
+    inputs=[
+        gr.ImageMask(label="[Stroke] Draw on Image", type="pil"),
+        gr.Image(type="pil", label="test image1"),
+        gr.Image(type="pil", label="test image2"),
+    ],
+    outputs=[
+        gr.outputs.Image(type="pil", label="output image1"),
+        gr.outputs.Image(type="pil", label="output image2"),
+    ],
+    allow_flagging="never",
+    title="Personalize Segment Anything Model with 1 Shot",
+    description=description,
+    examples=[
+        ["./examples/cat_00.jpg", "./examples/cat_01.jpg", "./examples/cat_02.jpg"],
+        ["./examples/colorful_sneaker_00.jpg", "./examples/colorful_sneaker_01.jpg", "./examples/colorful_sneaker_02.jpg"],
+        ["./examples/duck_toy_00.jpg", "./examples/duck_toy_01.jpg", "./examples/duck_toy_02.jpg"],
+    ]
+)
+
+main_finetune = gr.Interface(
+    fn=inference_finetune,
+    inputs=[
+        gr.Image(type="pil", label="in context image"),
+        gr.Image(type="pil", label="in context mask"),
+        gr.Image(type="pil", label="test image1"),
+        gr.Image(type="pil", label="test image2"),
+    ],
+    outputs=[
+        gr.components.Image(type="pil", label="output image1"),
+        gr.components.Image(type="pil", label="output image2"),
+    ],
+    allow_flagging="never",
+    title="Personalize Segment Anything Model with 1 Shot",
+    description=description,
+    examples=[
+        ["./examples/cat_00.jpg", "./examples/cat_00.png", "./examples/cat_01.jpg", "./examples/cat_02.jpg"],
+        ["./examples/colorful_sneaker_00.jpg", "./examples/colorful_sneaker_00.png", "./examples/colorful_sneaker_01.jpg", "./examples/colorful_sneaker_02.jpg"],
+        ["./examples/duck_toy_00.jpg", "./examples/duck_toy_00.png", "./examples/duck_toy_01.jpg", "./examples/duck_toy_02.jpg"],
+    ]
+)
+
+
+demo = gr.Blocks()
+with demo:
+    gr.TabbedInterface(
+        [main, main_scribble, main_finetune],
+        ["Personalize-SAM", "Personalize-SAM-Scribble", "Personalize-SAM-F"],
+    )
+
+demo.launch(share=True)

requirements.txt

@@ -0,0 +1,7 @@
+matplotlib
+tqdm
+os
+numpy
+warnings
+argparse
+opencv-python

show.py

@@ -0,0 +1,28 @@
+import numpy as np
+import torch
+import matplotlib.pyplot as plt
+import cv2
+
+
+
+def show_mask(mask, ax, random_color=False):
+    if random_color:
+        color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)
+    else:
+        color = np.array([30/255, 144/255, 255/255, 0.4])
+    h, w = mask.shape[-2:]
+    mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
+    ax.imshow(mask_image)
+    
+
+def show_points(coords, labels, ax, marker_size=375):
+    pos_points = coords[labels==1]
+    neg_points = coords[labels==0]
+    ax.scatter(pos_points[:, 0], pos_points[:, 1], color='green', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)
+    ax.scatter(neg_points[:, 0], neg_points[:, 1], color='red', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)   
+    
+
+def show_box(box, ax):
+    x0, y0 = box[0], box[1]
+    w, h = box[2] - box[0], box[3] - box[1]
+    ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0,0,0,0), lw=2))