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segment-anything-2.py
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import sys
import os
import time
from logging import getLogger
import numpy as np
import cv2
import os
import numpy as np
# import original modules
sys.path.append('../../util')
import webcamera_utils
from arg_utils import get_base_parser, update_parser, get_savepath # noqa
from model_utils import check_and_download_models # noqa
from webcamera_utils import get_capture, get_writer # noqa
logger = getLogger(__name__)
# ======================
# Parameters
# ======================
IMAGE_PATH = 'truck.jpg'
SAVE_IMAGE_PATH = 'output.png'
POINT1 = (500, 375)
POINT2 = (1125, 625)
TARGET_LENGTH = 1024
# ======================
# Arguemnt Parser Config
# ======================
parser = get_base_parser(
'Segment Anything 2', IMAGE_PATH, SAVE_IMAGE_PATH
)
parser.add_argument(
'-p', '--pos', action='append', type=int, metavar="X", nargs=2,
help='Positive coordinate specified by x,y.'
)
parser.add_argument(
'--neg', action='append', type=int, metavar="X", nargs=2,
help='Negative coordinate specified by x,y.'
)
parser.add_argument(
'--box', type=int, metavar="X", nargs=4,
help='Box coordinate specified by x1,y1,x2,y2.'
)
parser.add_argument(
'--num_mask_mem', type=int, default=7, choices=(0, 1, 2, 3, 4, 5, 6, 7),
help='Number of mask mem. (default 1 input frame + 6 previous frames)'
)
parser.add_argument(
'--max_obj_ptrs_in_encoder', type=int, default=16, choices=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15),
help='Number of obj ptr in encoder.'
)
parser.add_argument(
'-m', '--model_type', default='hiera_l', choices=('hiera_l', 'hiera_b+', 'hiera_s', 'hiera_t'),
help='Select model.'
)
parser.add_argument(
'--onnx', action='store_true',
help='execute onnxruntime version.'
)
parser.add_argument(
'--normal', action='store_true',
help='Use normal version of onnx model. Normal version requires 6 dim matmul.'
)
parser.add_argument(
'--version', default='2', choices=('2', '2.1'),
help='Select model.'
)
args = update_parser(parser)
# ======================
# Model path
# ======================
if args.version == "2.1":
REMOTE_PATH = 'https://storage.googleapis.com/ailia-models/segment-anything-2.1/'
else:
REMOTE_PATH = 'https://storage.googleapis.com/ailia-models/segment-anything-2/'
# ======================
# Utility
# ======================
np.random.seed(3)
def show_mask(mask, img, color = np.array([255, 144, 30]), obj_id=None):
color = color.reshape(1, 1, -1)
h, w = mask.shape[-2:]
mask = mask.reshape(h, w, 1)
mask_image = mask * color
img = (img * ~mask) + (img * mask) * 0.6 + mask_image * 0.4
return img
def show_points(coords, labels, img):
pos_points = coords[labels == 1]
neg_points = coords[labels == 0]
for p in pos_points:
cv2.drawMarker(
img, p, (0, 255, 0), markerType=cv2.MARKER_TILTED_CROSS, line_type=cv2.LINE_AA,
markerSize=30, thickness=5)
for p in neg_points:
cv2.drawMarker(
img, p, (0, 0, 255), markerType=cv2.MARKER_TILTED_CROSS, line_type=cv2.LINE_AA,
markerSize=30, thickness=5)
return img
def show_box(box, img):
if box is None:
return img
cv2.rectangle(
img, (box[0], box[1]), (box[2], box[3]), color=(2, 118, 2),
thickness=3,
lineType=cv2.LINE_4,
shift=0)
return img
# ======================
# Logic
# ======================
from sam2_image_predictor import SAM2ImagePredictor
from sam2_video_predictor import SAM2VideoPredictor
# ======================
# Main
# ======================
def get_input_point():
pos_points = args.pos
neg_points = args.neg
box = args.box
if pos_points is None:
if neg_points is None and box is None:
pos_points = [POINT1]
else:
pos_points = []
if neg_points is None:
neg_points = []
input_point = []
input_label = []
if pos_points:
for i in range(len(pos_points)):
input_point.append(pos_points[i])
input_label.append(1)
if neg_points:
for i in range(len(neg_points)):
input_point.append(neg_points[i])
input_label.append(0)
input_point = np.array(input_point)
input_label = np.array(input_label)
input_box = None
if box:
input_box = np.array(box)
return input_point, input_label, input_box
def recognize_from_image(image_encoder, prompt_encoder, mask_decoder):
input_point, input_label, input_box = get_input_point()
image_predictor = SAM2ImagePredictor()
for image_path in args.input:
image = cv2.imread(image_path)
orig_hw = [image.shape[0], image.shape[1]]
image_size = 1024
image_np = preprocess_frame(image, image_size=image_size)
if args.benchmark:
logger.info('BENCHMARK mode')
total_time_estimation = 0
for i in range(args.benchmark_count):
start = int(round(time.time() * 1000))
features = image_predictor.set_image(image_np, image_encoder, args.onnx)
masks, scores, logits = image_predictor.predict(
orig_hw=orig_hw,
features=features,
point_coords=input_point,
point_labels=input_label,
box=input_box,
prompt_encoder=prompt_encoder,
mask_decoder=mask_decoder,
onnx=args.onnx
)
end = int(round(time.time() * 1000))
estimation_time = (end - start)
# Logging
logger.info(f'\tailia processing estimation time {estimation_time} ms')
if i != 0:
total_time_estimation = total_time_estimation + estimation_time
logger.info(f'\taverage time estimation {total_time_estimation / (args.benchmark_count - 1)} ms')
else:
features = image_predictor.set_image(image_np, image_encoder, args.onnx)
masks, scores, logits = image_predictor.predict(
orig_hw=orig_hw,
features=features,
point_coords=input_point,
point_labels=input_label,
box=input_box,
prompt_encoder=prompt_encoder,
mask_decoder=mask_decoder,
onnx=args.onnx
)
sorted_ind = np.argsort(scores)[::-1]
masks = masks[sorted_ind]
scores = scores[sorted_ind]
logits = logits[sorted_ind]
savepath = get_savepath(args.savepath, image_path, ext='.png')
logger.info(f'saved at : {savepath}')
image = show_mask(masks[0], image)
image = show_points(input_point, input_label, image)
image = show_box(input_box, image)
cv2.imwrite(savepath, image)
def preprocess_frame(img, image_size):
img_mean=(0.485, 0.456, 0.406)
img_std=(0.229, 0.224, 0.225)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (image_size, image_size))
img = img / 255.0
img = img - img_mean
img = img / img_std
img = np.transpose(img, (2, 0, 1))
return img
def recognize_from_video(image_encoder, prompt_encoder, mask_decoder, memory_attention, memory_encoder, mlp, obj_ptr_tpos_proj):
image_size = 1024
if args.video == "demo":
frame_names = [
p for p in os.listdir(args.video)
if os.path.splitext(p)[-1] in [".jpg", ".jpeg", ".JPG", ".JPEG"]
]
frame_names.sort(key=lambda p: int(os.path.splitext(p)[0]))
input_point = np.array([[210, 350], [250, 220]], dtype=np.float32)
input_label = np.array([1, 1], np.int32)
input_box = None
video_width = 960
video_height = 540
else:
frame_names = None
capture = webcamera_utils.get_capture(args.video)
video_height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
video_width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
input_point, input_label, input_box = get_input_point()
if args.savepath != SAVE_IMAGE_PATH:
writer = webcamera_utils.get_writer(args.savepath, video_height, video_width)
else:
writer = None
predictor = SAM2VideoPredictor(args.onnx, args.normal, args.benchmark)
inference_state = predictor.init_state(args.num_mask_mem, args.max_obj_ptrs_in_encoder, args.version)
predictor.reset_state(inference_state)
frame_shown = False
if args.benchmark:
start = int(round(time.time() * 1000))
frame_idx = 0
while (True):
if frame_names is None:
ret, frame = capture.read()
else:
ret = True
if frame_idx >= len(frame_names):
break
frame = cv2.imread(os.path.join(args.video, frame_names[frame_idx]))
video_height = frame.shape[0]
video_width = frame.shape[1]
if (cv2.waitKey(1) & 0xFF == ord('q')) or not ret:
break
if frame_shown and cv2.getWindowProperty('frame', cv2.WND_PROP_VISIBLE) == 0:
break
image = preprocess_frame(frame, image_size)
predictor.append_image(
inference_state,
image,
video_height,
video_width,
image_encoder)
if frame_idx == 0:
annotate_frame(input_point, input_label, input_box, predictor, inference_state, image_encoder, prompt_encoder, mask_decoder, memory_attention, memory_encoder, mlp, obj_ptr_tpos_proj)
frame = process_frame(frame, frame_idx, predictor, inference_state, image_encoder, prompt_encoder, mask_decoder, memory_attention, memory_encoder, mlp, obj_ptr_tpos_proj)
frame = frame.astype(np.uint8)
if frame_idx == 0:
frame = show_points(input_point.astype(np.int64), input_label.astype(np.int64), frame)
frame = show_box(input_box, frame)
cv2.imshow('frame', frame)
if frame_names is not None:
cv2.imwrite(f'video_{frame_idx}.png', frame)
if writer is not None:
writer.write(frame)
frame_shown = True
frame_idx = frame_idx + 1
if args.benchmark:
end = int(round(time.time() * 1000))
estimation_time = (end - start)
logger.info(f'\ttotal processing time {estimation_time} ms')
if writer is not None:
writer.release()
def annotate_frame(points, labels, box, predictor, inference_state, image_encoder, prompt_encoder, mask_decoder, memory_attention, memory_encoder, mlp, obj_ptr_tpos_proj):
ann_frame_idx = 0 # the frame index we interact with
ann_obj_id = 1 # give a unique id to each object we interact with (it can be any integers)
_, out_obj_ids, out_mask_logits = predictor.add_new_points_or_box(
inference_state=inference_state,
frame_idx=ann_frame_idx,
obj_id=ann_obj_id,
points=points,
labels=labels,
box=box,
image_encoder=image_encoder,
prompt_encoder=prompt_encoder,
mask_decoder=mask_decoder,
memory_attention=memory_attention,
memory_encoder=memory_encoder,
mlp=mlp
)
predictor.propagate_in_video_preflight(inference_state,
image_encoder = image_encoder,
prompt_encoder = prompt_encoder,
mask_decoder = mask_decoder,
memory_attention = memory_attention,
memory_encoder = memory_encoder,
mlp = mlp,
obj_ptr_tpos_proj = obj_ptr_tpos_proj)
def process_frame(image, frame_idx, predictor, inference_state, image_encoder, prompt_encoder, mask_decoder, memory_attention, memory_encoder, mlp, obj_ptr_tpos_proj):
out_frame_idx, out_obj_ids, out_mask_logits = predictor.propagate_in_video(inference_state,
image_encoder = image_encoder,
prompt_encoder = prompt_encoder,
mask_decoder = mask_decoder,
memory_attention = memory_attention,
memory_encoder = memory_encoder,
mlp = mlp,
obj_ptr_tpos_proj = obj_ptr_tpos_proj,
frame_idx = frame_idx)
image = show_mask((out_mask_logits[0] > 0.0), image, color = np.array([30, 144, 255]), obj_id = out_obj_ids[0])
return image
def main():
# fetch image encoder model
model_type = args.model_type
if args.version == "2.1":
model_type = model_type + "_2.1"
WEIGHT_IMAGE_ENCODER_L_PATH = 'image_encoder_'+model_type+'.onnx'
MODEL_IMAGE_ENCODER_L_PATH = 'image_encoder_'+model_type+'.onnx.prototxt'
WEIGHT_PROMPT_ENCODER_L_PATH = 'prompt_encoder_'+model_type+'.onnx'
MODEL_PROMPT_ENCODER_L_PATH = 'prompt_encoder_'+model_type+'.onnx.prototxt'
WEIGHT_MASK_DECODER_L_PATH = 'mask_decoder_'+model_type+'.onnx'
MODEL_MASK_DECODER_L_PATH = 'mask_decoder_'+model_type+'.onnx.prototxt'
if args.normal:
# 6dim matmul
if args.version == "2.1":
raise Exception("SAM2.1 not exported normal model.")
WEIGHT_MEMORY_ATTENTION_L_PATH = 'memory_attention_'+model_type+'.onnx'
MODEL_MEMORY_ATTENTION_L_PATH = 'memory_attention_'+model_type+'.onnx.prototxt'
else:
# 4dim matmul with batch 1
WEIGHT_MEMORY_ATTENTION_L_PATH = 'memory_attention_'+model_type+'.opt.onnx'
MODEL_MEMORY_ATTENTION_L_PATH = 'memory_attention_'+model_type+'.opt.onnx.prototxt'
WEIGHT_MEMORY_ENCODER_L_PATH = 'memory_encoder_'+model_type+'.onnx'
MODEL_MEMORY_ENCODER_L_PATH = 'memory_encoder_'+model_type+'.onnx.prototxt'
WEIGHT_MLP_L_PATH = 'mlp_'+model_type+'.onnx'
MODEL_MLP_L_PATH = 'mlp_'+model_type+'.onnx.prototxt'
if args.version == "2.1":
WEIGHT_TPOS_L_PATH = 'obj_ptr_tpos_proj_'+model_type+'.onnx'
MODEL_TPOS_L_PATH = 'obj_ptr_tpos_proj_'+model_type+'.onnx.prototxt'
else:
WEIGHT_TPOS_L_PATH = None
MODEL_TPOS_L_PATH = None
# model files check and download
check_and_download_models(WEIGHT_IMAGE_ENCODER_L_PATH, MODEL_IMAGE_ENCODER_L_PATH, REMOTE_PATH)
check_and_download_models(WEIGHT_PROMPT_ENCODER_L_PATH, MODEL_PROMPT_ENCODER_L_PATH, REMOTE_PATH)
check_and_download_models(WEIGHT_MASK_DECODER_L_PATH, MODEL_MASK_DECODER_L_PATH, REMOTE_PATH)
check_and_download_models(WEIGHT_MEMORY_ATTENTION_L_PATH, MODEL_MEMORY_ATTENTION_L_PATH, REMOTE_PATH)
check_and_download_models(WEIGHT_MEMORY_ENCODER_L_PATH, MODEL_MEMORY_ENCODER_L_PATH, REMOTE_PATH)
check_and_download_models(WEIGHT_MLP_L_PATH, MODEL_MLP_L_PATH, REMOTE_PATH)
if args.version == "2.1":
check_and_download_models(WEIGHT_TPOS_L_PATH, MODEL_TPOS_L_PATH, REMOTE_PATH)
if args.onnx:
import onnxruntime
image_encoder = onnxruntime.InferenceSession(WEIGHT_IMAGE_ENCODER_L_PATH)
prompt_encoder = onnxruntime.InferenceSession(WEIGHT_PROMPT_ENCODER_L_PATH)
mask_decoder = onnxruntime.InferenceSession(WEIGHT_MASK_DECODER_L_PATH)
memory_attention = onnxruntime.InferenceSession(WEIGHT_MEMORY_ATTENTION_L_PATH)
memory_encoder = onnxruntime.InferenceSession(WEIGHT_MEMORY_ENCODER_L_PATH)
mlp = onnxruntime.InferenceSession(WEIGHT_MLP_L_PATH)
if args.version == "2.1":
obj_ptr_tpos_proj = onnxruntime.InferenceSession(WEIGHT_TPOS_L_PATH)
else:
obj_ptr_tpos_proj = None
else:
import ailia
memory_mode = ailia.get_memory_mode(reduce_constant=True, ignore_input_with_initializer=True, reduce_interstage=False, reuse_interstage=True)
image_encoder = ailia.Net(weight=WEIGHT_IMAGE_ENCODER_L_PATH, stream=MODEL_IMAGE_ENCODER_L_PATH, memory_mode=memory_mode, env_id=args.env_id)
prompt_encoder = ailia.Net(weight=WEIGHT_PROMPT_ENCODER_L_PATH, stream=MODEL_PROMPT_ENCODER_L_PATH, memory_mode=memory_mode, env_id=args.env_id)
mask_decoder = ailia.Net(weight=WEIGHT_MASK_DECODER_L_PATH, stream=MODEL_MASK_DECODER_L_PATH, memory_mode=memory_mode, env_id=args.env_id)
memory_attention = ailia.Net(weight=WEIGHT_MEMORY_ATTENTION_L_PATH, stream=MODEL_MEMORY_ATTENTION_L_PATH, memory_mode=memory_mode, env_id=args.env_id)
memory_encoder = ailia.Net(weight=WEIGHT_MEMORY_ENCODER_L_PATH, stream=MODEL_MEMORY_ENCODER_L_PATH, memory_mode=memory_mode, env_id=args.env_id)
mlp = ailia.Net(weight=WEIGHT_MLP_L_PATH, stream=MODEL_MLP_L_PATH, memory_mode=memory_mode, env_id=args.env_id)
if args.version == "2.1":
obj_ptr_tpos_proj = ailia.Net(weight=WEIGHT_TPOS_L_PATH, stream=MODEL_TPOS_L_PATH, memory_mode=memory_mode, env_id=args.env_id)
else:
obj_ptr_tpos_proj = None
if args.video is not None:
recognize_from_video(image_encoder, prompt_encoder, mask_decoder, memory_attention, memory_encoder, mlp, obj_ptr_tpos_proj)
else:
recognize_from_image(image_encoder, prompt_encoder, mask_decoder)
logger.info('Script finished successfully.')
if __name__ == '__main__':
main()