Spaces:
Running
on
L4
Running
on
L4
File size: 4,953 Bytes
d6ff06e ca815e1 d6ff06e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 |
from io import BytesIO
import base64
import numpy as np
import matplotlib.pyplot as plt
import torch
def fig_to_base64(fig):
buf = BytesIO()
fig.savefig(buf, format='png', bbox_inches='tight')
plt.close(fig)
buf.seek(0)
return base64.b64encode(buf.getvalue()).decode()
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, 0.6])
h, w = mask.shape[-2:]
mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
ax.imshow(mask_image)
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))
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_boxes_on_image_base64(raw_image, boxes):
fig, ax = plt.subplots(figsize=(10,10))
ax.imshow(raw_image)
for box in boxes:
show_box(box, ax)
ax.axis('off')
return fig_to_base64(fig)
def show_points_on_image_base64(raw_image, input_points, input_labels=None):
fig, ax = plt.subplots(figsize=(10,10))
ax.imshow(raw_image)
input_points = np.array(input_points)
labels = np.ones_like(input_points[:, 0]) if input_labels is None else np.array(input_labels)
show_points(input_points, labels, ax)
ax.axis('off')
return fig_to_base64(fig)
def show_points_and_boxes_on_image_base64(raw_image, boxes, input_points, input_labels=None):
fig, ax = plt.subplots(figsize=(10,10))
ax.imshow(raw_image)
input_points = np.array(input_points)
labels = np.ones_like(input_points[:, 0]) if input_labels is None else np.array(input_labels)
show_points(input_points, labels, ax)
for box in boxes:
show_box(box, ax)
ax.axis('off')
return fig_to_base64(fig)
def show_masks_on_image_base64(raw_image, masks, scores):
if len(masks.shape) == 4:
masks = masks.squeeze()
if scores.shape[0] == 1:
scores = scores.squeeze()
nb_predictions = scores.shape[-1]
print(f"Number of predictions: {nb_predictions}")
fig, axes = plt.subplots(1, nb_predictions, figsize=(5 * nb_predictions, 5))
if nb_predictions == 1:
axes = [axes]
for i, (mask, score) in enumerate(zip(masks, scores)):
print(i)
mask = mask.cpu().detach().numpy()
axes[i].imshow(np.array(raw_image))
show_mask(mask, axes[i])
axes[i].title.set_text(f"Mask {i+1}, Score: {score.item():.3f}")
axes[i].axis("off")
return fig_to_base64(fig)
def show_first_mask_on_image_base64(raw_image, masks, scores):
if masks.ndim == 4:
mask = masks[0, 0]
elif masks.ndim == 3:
mask = masks[0]
else:
mask = masks
if isinstance(mask, torch.Tensor):
mask = mask.cpu().detach().numpy()
score_text = ""
if scores is not None:
if isinstance(scores, torch.Tensor):
scores = scores.flatten()
score = scores[0].item()
else:
score = float(np.array(scores).flatten()[0])
score_text = f"Score: {score:.3f}"
fig, ax = plt.subplots(figsize=(5, 5))
ax.imshow(np.array(raw_image))
show_mask(mask, ax)
ax.set_title(score_text)
ax.axis("off")
return fig_to_base64(fig)
def show_all_annotations_on_image_base64(raw_image, masks=None, scores=None, boxes=None, input_points=None, input_labels=None, model_name=None):
fig, ax = plt.subplots(figsize=(10, 10))
ax.imshow(np.array(raw_image))
if masks is not None:
if masks.ndim == 4:
mask = masks[0, 0]
elif masks.ndim == 3:
mask = masks[0]
else:
mask = masks
if isinstance(mask, torch.Tensor):
mask = mask.cpu().detach().numpy()
show_mask(mask, ax)
if scores is not None:
if isinstance(scores, torch.Tensor):
scores = scores.flatten()
score = scores[0].item()
else:
score = float(np.array(scores).flatten()[0])
#ax.set_title(f"{model_name} - Score: {score:.3f}")
ax.set_title(f"{model_name}")
if input_points is not None:
input_points = np.array(input_points)
labels = np.ones_like(input_points[:, 0]) if input_labels is None else np.array(input_labels)
show_points(input_points, labels, ax)
if boxes is not None:
for box in boxes:
show_box(box, ax)
ax.axis("off")
return fig_to_base64(fig)
|