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demo_hqsam.py
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demo_hqsam.py
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import numpy as np
import torch
import matplotlib.pyplot as plt
import cv2
from segment_anything import sam_model_registry, SamPredictor
import os
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_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_res(masks, scores, input_point, input_label, input_box, filename, image):
for i, (mask, score) in enumerate(zip(masks, scores)):
plt.figure(figsize=(10,10))
plt.imshow(image)
show_mask(mask, plt.gca())
if input_box is not None:
box = input_box[i]
show_box(box, plt.gca())
if (input_point is not None) and (input_label is not None):
show_points(input_point, input_label, plt.gca())
print(f"Score: {score:.3f}")
plt.axis('off')
plt.savefig(filename+'_'+str(i)+'.png',bbox_inches='tight',pad_inches=-0.1)
plt.close()
def show_res_multi(masks, scores, input_point, input_label, input_box, filename, image):
plt.figure(figsize=(10, 10))
plt.imshow(image)
for mask in masks:
show_mask(mask, plt.gca(), random_color=True)
for box in input_box:
show_box(box, plt.gca())
for score in scores:
print(f"Score: {score:.3f}")
plt.axis('off')
plt.savefig(filename +'.png',bbox_inches='tight',pad_inches=-0.1)
plt.close()
if __name__ == "__main__":
sam_checkpoint = "./pretrained_checkpoint/sam_hq_vit_l.pth"
model_type = "vit_l"
device = "cuda"
sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)
sam.to(device=device)
predictor = SamPredictor(sam)
for i in range(8):
print("image: ",i)
# hq_token_only: False means use hq output to correct SAM output.
# True means use hq output only.
# Default: False
hq_token_only = False
# To achieve best visualization effect, for images contain multiple objects (like typical coco images), we suggest to set hq_token_only=False
# For images contain single object, we suggest to set hq_token_only = True
# For quantiative evaluation on COCO/YTVOS/DAVIS/UVO/LVIS etc., we set hq_token_only = False
image = cv2.imread('demo/input_imgs/example'+str(i)+'.png')
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
predictor.set_image(image)
if i==0:
input_box = np.array([[4,13,1007,1023]])
input_point, input_label = None, None
elif i==1:
input_box = np.array([[306, 132, 925, 893]])
input_point, input_label = None, None
hq_token_only = True
elif i==2:
input_point = np.array([[495,518],[217,140]])
input_label = np.ones(input_point.shape[0])
input_box = None
hq_token_only = True
elif i==3:
input_point = np.array([[221,482],[498,633],[750,379]])
input_label = np.ones(input_point.shape[0])
input_box = None
elif i==4:
input_box = np.array([[64,76,940,919]])
input_point, input_label = None, None
hq_token_only = True
elif i==5:
input_point = np.array([[373,363], [452, 575]])
input_label = np.ones(input_point.shape[0])
input_box = None
elif i==6:
input_box = np.array([[181, 196, 757, 495]])
input_point, input_label = None, None
elif i==7:
# multi box input
input_box = torch.tensor([[45,260,515,470], [310,228,424,296]],device=predictor.device)
transformed_box = predictor.transform.apply_boxes_torch(input_box, image.shape[:2])
input_point, input_label = None, None
batch_box = False if input_box is None else len(input_box)>1
result_path = 'demo/hq_sam_result/'
os.makedirs(result_path, exist_ok=True)
if not batch_box:
masks, scores, logits = predictor.predict(
point_coords=input_point,
point_labels=input_label,
box = input_box,
multimask_output=False,
hq_token_only=hq_token_only,
)
show_res(masks,scores,input_point, input_label, input_box, result_path + 'example'+str(i), image)
else:
masks, scores, logits = predictor.predict_torch(
point_coords=input_point,
point_labels=input_label,
boxes=transformed_box,
multimask_output=False,
hq_token_only=hq_token_only,
)
masks = masks.squeeze(1).cpu().numpy()
scores = scores.squeeze(1).cpu().numpy()
input_box = input_box.cpu().numpy()
show_res_multi(masks, scores, input_point, input_label, input_box, result_path + 'example'+str(i), image)