SysCV
The result is worse than the original version SAM2 from Meta AI reseach
If I use point as input prompt words, the result is worse than the original version SAM2 from Meta AI reseach. I want to know if SAM-HQ only supports using boxes as prompt words. The left picture is the output of SAM-HQ, and the right picture is the output of SAM2. I also attached the modified code that uses point as prompt words and the orignal picture for test.
Code is here:
import numpy as np import torch import matplotlib.pyplot as plt import cv2 from sam2.build_sam import build_sam2 from sam2.sam2_image_predictor import SAM2ImagePredictor import os
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=False): 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() 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": checkpoint = "D:/huggingface_cache/sam2.1_hq_hiera_large.pt" model_cfg = "configs/sam2.1/sam2.1_hq_hiera_l.yaml" predictor = SAM2ImagePredictor(build_sam2(model_cfg, checkpoint))
for i in range(1,3):
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('E:/AI/sam-hq-main/sam-hq2/demo/input_images/test'+str(i)+'.jpg')
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
predictor.set_image(image)
if i==1:
input_box = None
input_point = np.array([[400, 550]])
input_label = np.ones(input_point.shape[0])
elif i==2:
input_box = None
input_point = np.array([[300, 350]])
input_label = np.ones(input_point.shape[0])
elif i==3:
input_box = None
input_point = np.array([[400, 390]])
input_label = np.ones(input_point.shape[0])
batch_box = False if input_box is None else len(input_box)>1
result_path = 'demo/hq_sam_result_vis/'
os.makedirs(result_path, exist_ok=True)
with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
masks, scores, logits = predictor.predict(point_coords=input_point,
point_labels=input_label,
box=input_box,
multimask_output=True, hq_token_only=hq_token_only)
if not batch_box:
show_res(masks,scores,input_point, input_label, input_box, result_path + 'example'+str(i), image)
else:
masks = masks.squeeze(1)
scores = scores.squeeze(1)
input_box = input_box.cpu().numpy()
show_res_multi(masks, scores, input_point, input_label, input_box, result_path + 'example'+str(i), image)
