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import os
# if using Apple MPS, fall back to CPU for unsupported ops
os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
import numpy as np
import torch
import matplotlib.pyplot as plt
from PIL import Image
# select the device for computation
if torch.cuda.is_available():
device = torch.device("cuda")
elif torch.backends.mps.is_available():
device = torch.device("mps")
else:
device = torch.device("cpu")
print(f"using device: {device}")
if device.type == "cuda":
# use bfloat16 for the entire notebook
torch.autocast("cuda", dtype=torch.bfloat16).__enter__()
# turn on tfloat32 for Ampere GPUs (https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices)
if torch.cuda.get_device_properties(0).major >= 8:
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
elif device.type == "mps":
print(
"\nSupport for MPS devices is preliminary. SAM 2 is trained with CUDA and might "
"give numerically different outputs and sometimes degraded performance on MPS. "
"See e.g. https://github.com/pytorch/pytorch/issues/84936 for a discussion."
)
np.random.seed(3)
def show_mask(mask, ax, random_color=False, borders = True):
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 = mask.astype(np.uint8)
mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
if borders:
import cv2
contours, _ = cv2.findContours(mask,cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
# Try to smooth contours
contours = [cv2.approxPolyDP(contour, epsilon=0.01, closed=True) for contour in contours]
mask_image = cv2.drawContours(mask_image, contours, -1, (1, 1, 1, 0.5), thickness=2)
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))
def show_masks(image, masks, scores, point_coords=None, box_coords=None, input_labels=None, borders=True):
for i, (mask, score) in enumerate(zip(masks, scores)):
plt.figure(figsize=(10, 10))
plt.imshow(image)
show_mask(mask, plt.gca(), borders=borders)
if point_coords is not None:
assert input_labels is not None
show_points(point_coords, input_labels, plt.gca())
if box_coords is not None:
# boxes
show_box(box_coords, plt.gca())
if len(scores) > 1:
plt.title(f"Mask {i+1}, Score: {score:.3f}", fontsize=18)
plt.axis('off')
plt.show()
image = Image.open('images/truck.jpg')
image = np.array(image.convert("RGB"))
from sam2.build_sam import build_sam2
from sam2.sam2_image_predictor import SAM2ImagePredictor
sam2_checkpoint = "./checkpoints/sam2_hiera_base_plus.pt"
model_cfg = "configs/sam2/sam2_hiera_b+.yaml"
sam2_model = build_sam2(model_cfg, sam2_checkpoint, device=device)
predictor = SAM2ImagePredictor(sam2_model)
predictor.set_image(image)
# input_point = np.array([[500, 375]])
# input_label = np.array([1])
input_point = np.array([[500, 375], [1125, 625]])
input_label = np.array([1, 1])
masks, scores, logits = predictor.predict(
point_coords=input_point,
point_labels=input_label,
multimask_output=True,
)
# sorted_ind = np.argsort(scores)[::-1]
# masks = masks[sorted_ind]
# scores = scores[sorted_ind]
# logits = logits[sorted_ind]
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