198 lines
7.4 KiB
Python
198 lines
7.4 KiB
Python
import cv2
|
|
import os
|
|
import numpy as np
|
|
from segment_anything import sam_model_registry, SamPredictor
|
|
|
|
input_dir = 'scripts/input/images'
|
|
output_dir = 'scripts/output/mask'
|
|
crop_mode = True
|
|
|
|
print('最好是每加一个点就按w键predict一次')
|
|
os.makedirs(output_dir, exist_ok=True)
|
|
image_files = [f for f in os.listdir(input_dir) if
|
|
f.lower().endswith(('.png', '.jpg', '.jpeg', '.JPG', '.JPEG', '.PNG', '.tiff'))]
|
|
|
|
sam = sam_model_registry["vit_b"](checkpoint=r"D:\Program Files\Pycharm items\segment-anything-model\weights\vit_b.pth")
|
|
_ = sam.to(device="cuda")
|
|
predictor = SamPredictor(sam)
|
|
|
|
WINDOW_WIDTH = 1280
|
|
WINDOW_HEIGHT = 720
|
|
cv2.namedWindow("image", cv2.WINDOW_NORMAL)
|
|
cv2.resizeWindow("image", WINDOW_WIDTH, WINDOW_HEIGHT)
|
|
cv2.moveWindow("image", (1920 - WINDOW_WIDTH) // 2, (1080 - WINDOW_HEIGHT) // 2)
|
|
|
|
|
|
def mouse_click(event, x, y, flags, param):
|
|
global input_point, input_label, input_stop
|
|
if not input_stop:
|
|
if event == cv2.EVENT_LBUTTONDOWN:
|
|
input_point.append([x, y])
|
|
input_label.append(1)
|
|
elif event == cv2.EVENT_RBUTTONDOWN:
|
|
input_point.append([x, y])
|
|
input_label.append(0)
|
|
else:
|
|
if event == cv2.EVENT_LBUTTONDOWN or event == cv2.EVENT_RBUTTONDOWN:
|
|
print('此时不能添加点,按w退出mask选择模式')
|
|
|
|
|
|
def apply_mask(image, mask, alpha_channel=True):
|
|
if alpha_channel:
|
|
alpha = np.zeros_like(image[..., 0])
|
|
alpha[mask == 1] = 255
|
|
image = cv2.merge((image[..., 0], image[..., 1], image[..., 2], alpha))
|
|
else:
|
|
image = np.where(mask[..., None] == 1, image, 0)
|
|
return image
|
|
|
|
|
|
def apply_color_mask(image, mask, color, color_dark=0.5):
|
|
for c in range(3):
|
|
image[:, :, c] = np.where(mask == 1, image[:, :, c] * (1 - color_dark) + color_dark * color[c], image[:, :, c])
|
|
return image
|
|
|
|
|
|
def get_next_filename(base_path, filename):
|
|
name, ext = os.path.splitext(filename)
|
|
for i in range(1, 101):
|
|
new_name = f"{name}_{i}{ext}"
|
|
if not os.path.exists(os.path.join(base_path, new_name)):
|
|
return new_name
|
|
return None
|
|
|
|
|
|
def save_masked_image(image, mask, output_dir, filename, crop_mode_):
|
|
if crop_mode_:
|
|
y, x = np.where(mask)
|
|
y_min, y_max, x_min, x_max = y.min(), y.max(), x.min(), x.max()
|
|
cropped_mask = mask[y_min:y_max + 1, x_min:x_max + 1]
|
|
cropped_image = image[y_min:y_max + 1, x_min:x_max + 1]
|
|
masked_image = apply_mask(cropped_image, cropped_mask)
|
|
else:
|
|
masked_image = apply_mask(image, mask)
|
|
filename = filename[:filename.rfind('.')] + '.png'
|
|
new_filename = get_next_filename(output_dir, filename)
|
|
|
|
if new_filename:
|
|
if masked_image.shape[-1] == 4:
|
|
cv2.imwrite(os.path.join(output_dir, new_filename), masked_image, [cv2.IMWRITE_PNG_COMPRESSION, 9])
|
|
else:
|
|
cv2.imwrite(os.path.join(output_dir, new_filename), masked_image)
|
|
print(f"Saved as {new_filename}")
|
|
else:
|
|
print("Could not save the image. Too many variations exist.")
|
|
|
|
|
|
current_index = 0
|
|
cv2.namedWindow("image")
|
|
cv2.setMouseCallback("image", mouse_click)
|
|
input_point = []
|
|
input_label = []
|
|
input_stop = False
|
|
|
|
while True:
|
|
filename = image_files[current_index]
|
|
image_orign = cv2.imread(os.path.join(input_dir, filename))
|
|
image_crop = image_orign.copy()
|
|
image = cv2.cvtColor(image_orign.copy(), cv2.COLOR_BGR2RGB)
|
|
selected_mask = None
|
|
logit_input = None
|
|
|
|
while True:
|
|
input_stop = False
|
|
image_display = image_orign.copy()
|
|
display_info = f'{filename} '
|
|
cv2.putText(image_display, display_info, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2, cv2.LINE_AA)
|
|
|
|
for point, label in zip(input_point, input_label):
|
|
color = (0, 255, 0) if label == 1 else (0, 0, 255)
|
|
cv2.circle(image_display, tuple(point), 5, color, -1)
|
|
|
|
if selected_mask is not None:
|
|
color = tuple(np.random.randint(0, 256, 3).tolist())
|
|
selected_image = apply_color_mask(image_display, selected_mask, color)
|
|
|
|
cv2.imshow("image", image_display)
|
|
key = cv2.waitKey(1)
|
|
|
|
if key == ord(" "):
|
|
input_point = []
|
|
input_label = []
|
|
selected_mask = None
|
|
logit_input = None
|
|
|
|
elif key == ord("w"):
|
|
input_stop = True
|
|
if len(input_point) > 0 and len(input_label) > 0:
|
|
predictor.set_image(image)
|
|
input_point_np = np.array(input_point)
|
|
input_label_np = np.array(input_label)
|
|
|
|
masks, scores, logits = predictor.predict(
|
|
point_coords=input_point_np,
|
|
point_labels=input_label_np,
|
|
mask_input=logit_input[None, :, :] if logit_input is not None else None,
|
|
multimask_output=True,
|
|
)
|
|
|
|
mask_idx = 0
|
|
num_masks = len(masks)
|
|
while (1):
|
|
color = tuple(np.random.randint(0, 256, 3).tolist())
|
|
image_select = image_orign.copy()
|
|
selected_mask = masks[mask_idx]
|
|
selected_image = apply_color_mask(image_select, selected_mask, color)
|
|
mask_info = f'Total: {num_masks} | Current: {mask_idx} | Score: {scores[mask_idx]:.2f} | w 预测 | d 切换下一个 | a 切换上一个 | q 移除最后一个 | s 保存'
|
|
cv2.putText(selected_image, mask_info, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2,
|
|
cv2.LINE_AA)
|
|
cv2.imshow("image", selected_image)
|
|
|
|
key = cv2.waitKey(10)
|
|
if key == ord('q') and len(input_point) > 0:
|
|
input_point.pop(-1)
|
|
input_label.pop(-1)
|
|
elif key == ord('s'):
|
|
save_masked_image(image_crop, selected_mask, output_dir, filename, crop_mode_=crop_mode)
|
|
elif key == ord('a'):
|
|
if mask_idx > 0:
|
|
mask_idx -= 1
|
|
else:
|
|
mask_idx = num_masks - 1
|
|
elif key == ord('d'):
|
|
if mask_idx < num_masks - 1:
|
|
mask_idx += 1
|
|
else:
|
|
mask_idx = 0
|
|
elif key == ord('w'):
|
|
break
|
|
elif key == ord(" "):
|
|
input_point = []
|
|
input_label = []
|
|
selected_mask = None
|
|
logit_input = None
|
|
break
|
|
logit_input = logits[mask_idx, :, :]
|
|
print('max score:', np.argmax(scores), ' select:', mask_idx)
|
|
|
|
elif key == ord('a'):
|
|
current_index = max(0, current_index - 1)
|
|
input_point = []
|
|
input_label = []
|
|
break
|
|
elif key == ord('d'):
|
|
current_index = min(len(image_files) - 1, current_index + 1)
|
|
input_point = []
|
|
input_label = []
|
|
break
|
|
elif key == 27:
|
|
break
|
|
elif key == ord('q') and len(input_point) > 0:
|
|
input_point.pop(-1)
|
|
input_label.pop(-1)
|
|
elif key == ord('s') and selected_mask is not None:
|
|
save_masked_image(image_crop, selected_mask, output_dir, filename, crop_mode_=crop_mode)
|
|
|
|
if key == 27:
|
|
break
|