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support gsam2 image predictor model

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rentainhe
2024-08-01 17:05:01 +08:00
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the sav_dataset directory of this source tree.
# adapted from https://github.com/hkchengrex/vos-benchmark
# and https://github.com/davisvideochallenge/davis2017-evaluation
# with their licenses found in the LICENSE_VOS_BENCHMARK and LICENSE_DAVIS files
# in the sav_dataset directory.
import math
import os
import time
from collections import defaultdict
from multiprocessing import Pool
from os import path
from typing import Dict, List, Tuple
import cv2
import numpy as np
import tqdm
from PIL import Image
from skimage.morphology import disk
class VideoEvaluator:
def __init__(self, gt_root, pred_root, skip_first_and_last=True) -> None:
"""
gt_root: path to the folder storing the gt masks
pred_root: path to the folder storing the predicted masks
skip_first_and_last: whether we should skip the evaluation of the first and the last frame.
True for SA-V val and test, same as in DAVIS semi-supervised evaluation.
"""
self.gt_root = gt_root
self.pred_root = pred_root
self.skip_first_and_last = skip_first_and_last
def __call__(self, vid_name: str) -> Tuple[str, Dict[str, float], Dict[str, float]]:
"""
vid_name: name of the video to evaluate
"""
# scan the folder to find subfolders for evaluation and
# check if the folder structure is SA-V
to_evaluate, is_sav_format = self.scan_vid_folder(vid_name)
# evaluate each (gt_path, pred_path) pair
eval_results = []
for all_frames, obj_id, gt_path, pred_path in to_evaluate:
if self.skip_first_and_last:
# skip the first and the last frames
all_frames = all_frames[1:-1]
evaluator = Evaluator(name=vid_name, obj_id=obj_id)
for frame in all_frames:
gt_array, pred_array = self.get_gt_and_pred(
gt_path, pred_path, frame, is_sav_format
)
evaluator.feed_frame(mask=pred_array, gt=gt_array)
iou, boundary_f = evaluator.conclude()
eval_results.append((obj_id, iou, boundary_f))
if is_sav_format:
iou_output, boundary_f_output = self.consolidate(eval_results)
else:
assert len(eval_results) == 1
iou_output = eval_results[0][1]
boundary_f_output = eval_results[0][2]
return vid_name, iou_output, boundary_f_output
def get_gt_and_pred(
self,
gt_path: str,
pred_path: str,
f_name: str,
is_sav_format: bool,
) -> Tuple[np.ndarray, np.ndarray]:
"""
Get the ground-truth and predicted masks for a single frame.
"""
gt_mask_path = path.join(gt_path, f_name)
pred_mask_path = path.join(pred_path, f_name)
assert os.path.exists(pred_mask_path), f"{pred_mask_path} not found"
gt_array = np.array(Image.open(gt_mask_path))
pred_array = np.array(Image.open(pred_mask_path))
assert (
gt_array.shape[-2:] == pred_array.shape[-2:]
), f"shape mismatch: {gt_mask_path}, {pred_mask_path}"
if is_sav_format:
assert len(np.unique(gt_array)) <= 2, (
f"found more than 1 object in {gt_mask_path} "
"SA-V format assumes one object mask per png file."
)
assert len(np.unique(pred_array)) <= 2, (
f"found more than 1 object in {pred_mask_path} "
"SA-V format assumes one object mask per png file."
)
gt_array = gt_array > 0
pred_array = pred_array > 0
return gt_array, pred_array
def scan_vid_folder(self, vid_name) -> Tuple[List, bool]:
"""
Scan the folder structure of the video and return a list of folders for evaluate.
"""
vid_gt_path = path.join(self.gt_root, vid_name)
vid_pred_path = path.join(self.pred_root, vid_name)
all_files_and_dirs = sorted(os.listdir(vid_gt_path))
to_evaluate = []
if all(name.endswith(".png") for name in all_files_and_dirs):
# All files are png files, dataset structure similar to DAVIS
is_sav_format = False
frames = all_files_and_dirs
obj_dir = None
to_evaluate.append((frames, obj_dir, vid_gt_path, vid_pred_path))
else:
# SA-V dataset structure, going one layer down into each subdirectory
is_sav_format = True
for obj_dir in all_files_and_dirs:
obj_gt_path = path.join(vid_gt_path, obj_dir)
obj_pred_path = path.join(vid_pred_path, obj_dir)
frames = sorted(os.listdir(obj_gt_path))
to_evaluate.append((frames, obj_dir, obj_gt_path, obj_pred_path))
return to_evaluate, is_sav_format
def consolidate(
self, eval_results
) -> Tuple[str, Dict[str, float], Dict[str, float]]:
"""
Consolidate the results of all the objects from the video into one dictionary.
"""
iou_output = {}
boundary_f_output = {}
for obj_id, iou, boundary_f in eval_results:
assert len(iou) == 1
key = list(iou.keys())[0]
iou_output[obj_id] = iou[key]
boundary_f_output[obj_id] = boundary_f[key]
return iou_output, boundary_f_output
#################################################################################################################
# Functions below are from https://github.com/hkchengrex/vos-benchmark with minor modifications
# _seg2bmap from https://github.com/hkchengrex/vos-benchmark/blob/main/vos_benchmark/utils.py
# get_iou and Evaluator from https://github.com/hkchengrex/vos-benchmark/blob/main/vos_benchmark/evaluator.py
# benchmark from https://github.com/hkchengrex/vos-benchmark/blob/main/vos_benchmark/benchmark.py with slight mod
#################################################################################################################
def _seg2bmap(seg, width=None, height=None):
"""
From a segmentation, compute a binary boundary map with 1 pixel wide
boundaries. The boundary pixels are offset by 1/2 pixel towards the
origin from the actual segment boundary.
Arguments:
seg : Segments labeled from 1..k.
width : Width of desired bmap <= seg.shape[1]
height : Height of desired bmap <= seg.shape[0]
Returns:
bmap (ndarray): Binary boundary map.
David Martin <dmartin@eecs.berkeley.edu>
January 2003
"""
seg = seg.astype(bool)
seg[seg > 0] = 1
assert np.atleast_3d(seg).shape[2] == 1
width = seg.shape[1] if width is None else width
height = seg.shape[0] if height is None else height
h, w = seg.shape[:2]
ar1 = float(width) / float(height)
ar2 = float(w) / float(h)
assert not (
width > w | height > h | abs(ar1 - ar2) > 0.01
), "Can" "t convert %dx%d seg to %dx%d bmap." % (w, h, width, height)
e = np.zeros_like(seg)
s = np.zeros_like(seg)
se = np.zeros_like(seg)
e[:, :-1] = seg[:, 1:]
s[:-1, :] = seg[1:, :]
se[:-1, :-1] = seg[1:, 1:]
b = seg ^ e | seg ^ s | seg ^ se
b[-1, :] = seg[-1, :] ^ e[-1, :]
b[:, -1] = seg[:, -1] ^ s[:, -1]
b[-1, -1] = 0
if w == width and h == height:
bmap = b
else:
bmap = np.zeros((height, width))
for x in range(w):
for y in range(h):
if b[y, x]:
j = 1 + math.floor((y - 1) + height / h)
i = 1 + math.floor((x - 1) + width / h)
bmap[j, i] = 1
return bmap
def get_iou(intersection, pixel_sum):
# handle edge cases without resorting to epsilon
if intersection == pixel_sum:
# both mask and gt have zero pixels in them
assert intersection == 0
return 1
return intersection / (pixel_sum - intersection)
class Evaluator:
def __init__(self, boundary=0.008, name=None, obj_id=None):
# boundary: used in computing boundary F-score
self.boundary = boundary
self.name = name
self.obj_id = obj_id
self.objects_in_gt = set()
self.objects_in_masks = set()
self.object_iou = defaultdict(list)
self.boundary_f = defaultdict(list)
def feed_frame(self, mask: np.ndarray, gt: np.ndarray):
"""
Compute and accumulate metrics for a single frame (mask/gt pair)
"""
# get all objects in the ground-truth
gt_objects = np.unique(gt)
gt_objects = gt_objects[gt_objects != 0].tolist()
# get all objects in the predicted mask
mask_objects = np.unique(mask)
mask_objects = mask_objects[mask_objects != 0].tolist()
self.objects_in_gt.update(set(gt_objects))
self.objects_in_masks.update(set(mask_objects))
all_objects = self.objects_in_gt.union(self.objects_in_masks)
# boundary disk for boundary F-score. It is the same for all objects.
bound_pix = np.ceil(self.boundary * np.linalg.norm(mask.shape))
boundary_disk = disk(bound_pix)
for obj_idx in all_objects:
obj_mask = mask == obj_idx
obj_gt = gt == obj_idx
# object iou
self.object_iou[obj_idx].append(
get_iou((obj_mask * obj_gt).sum(), obj_mask.sum() + obj_gt.sum())
)
"""
# boundary f-score
This part is copied from davis2017-evaluation
"""
mask_boundary = _seg2bmap(obj_mask)
gt_boundary = _seg2bmap(obj_gt)
mask_dilated = cv2.dilate(mask_boundary.astype(np.uint8), boundary_disk)
gt_dilated = cv2.dilate(gt_boundary.astype(np.uint8), boundary_disk)
# Get the intersection
gt_match = gt_boundary * mask_dilated
fg_match = mask_boundary * gt_dilated
# Area of the intersection
n_fg = np.sum(mask_boundary)
n_gt = np.sum(gt_boundary)
# Compute precision and recall
if n_fg == 0 and n_gt > 0:
precision = 1
recall = 0
elif n_fg > 0 and n_gt == 0:
precision = 0
recall = 1
elif n_fg == 0 and n_gt == 0:
precision = 1
recall = 1
else:
precision = np.sum(fg_match) / float(n_fg)
recall = np.sum(gt_match) / float(n_gt)
# Compute F measure
if precision + recall == 0:
F = 0
else:
F = 2 * precision * recall / (precision + recall)
self.boundary_f[obj_idx].append(F)
def conclude(self):
all_iou = {}
all_boundary_f = {}
for object_id in self.objects_in_gt:
all_iou[object_id] = np.mean(self.object_iou[object_id]) * 100
all_boundary_f[object_id] = np.mean(self.boundary_f[object_id]) * 100
return all_iou, all_boundary_f
def benchmark(
gt_roots,
mask_roots,
strict=True,
num_processes=None,
*,
verbose=True,
skip_first_and_last=True,
):
"""
gt_roots: a list of paths to datasets, i.e., [path_to_DatasetA, path_to_DatasetB, ...]
mask_roots: same as above, but the .png are masks predicted by the model
strict: when True, all videos in the dataset must have corresponding predictions.
Setting it to False is useful in cases where the ground-truth contains both train/val
sets, but the model only predicts the val subset.
Either way, if a video is predicted (i.e., the corresponding folder exists),
then it must at least contain all the masks in the ground truth annotations.
Masks that are in the prediction but not in the ground-truth
(i.e., sparse annotations) are ignored.
skip_first_and_last: whether we should skip the first and the last frame in evaluation.
This is used by DAVIS 2017 in their semi-supervised evaluation.
It should be disabled for unsupervised evaluation.
"""
assert len(gt_roots) == len(mask_roots)
single_dataset = len(gt_roots) == 1
if verbose:
if skip_first_and_last:
print(
"We are *SKIPPING* the evaluation of the first and the last frame (standard for semi-supervised video object segmentation)."
)
else:
print(
"We are *NOT SKIPPING* the evaluation of the first and the last frame (*NOT STANDARD* for semi-supervised video object segmentation)."
)
pool = Pool(num_processes)
start = time.time()
to_wait = []
for gt_root, mask_root in zip(gt_roots, mask_roots):
# Validate folders
validated = True
gt_videos = os.listdir(gt_root)
mask_videos = os.listdir(mask_root)
# if the user passed the root directory instead of Annotations
if len(gt_videos) != len(mask_videos):
if "Annotations" in gt_videos:
if ".png" not in os.listdir(path.join(gt_root, "Annotations"))[0]:
gt_root = path.join(gt_root, "Annotations")
gt_videos = os.listdir(gt_root)
# remove non-folder items
gt_videos = list(filter(lambda x: path.isdir(path.join(gt_root, x)), gt_videos))
mask_videos = list(
filter(lambda x: path.isdir(path.join(mask_root, x)), mask_videos)
)
if not strict:
videos = sorted(list(set(gt_videos) & set(mask_videos)))
else:
gt_extras = set(gt_videos) - set(mask_videos)
mask_extras = set(mask_videos) - set(gt_videos)
if len(gt_extras) > 0:
print(
f"Videos that are in {gt_root} but not in {mask_root}: {gt_extras}"
)
validated = False
if len(mask_extras) > 0:
print(
f"Videos that are in {mask_root} but not in {gt_root}: {mask_extras}"
)
validated = False
if not validated:
print("Validation failed. Exiting.")
exit(1)
videos = sorted(gt_videos)
if verbose:
print(
f"In dataset {gt_root}, we are evaluating on {len(videos)} videos: {videos}"
)
if single_dataset:
if verbose:
results = tqdm.tqdm(
pool.imap(
VideoEvaluator(
gt_root, mask_root, skip_first_and_last=skip_first_and_last
),
videos,
),
total=len(videos),
)
else:
results = pool.map(
VideoEvaluator(
gt_root, mask_root, skip_first_and_last=skip_first_and_last
),
videos,
)
else:
to_wait.append(
pool.map_async(
VideoEvaluator(
gt_root, mask_root, skip_first_and_last=skip_first_and_last
),
videos,
)
)
pool.close()
all_global_jf, all_global_j, all_global_f = [], [], []
all_object_metrics = []
for i, mask_root in enumerate(mask_roots):
if not single_dataset:
results = to_wait[i].get()
all_iou = []
all_boundary_f = []
object_metrics = {}
for name, iou, boundary_f in results:
all_iou.extend(list(iou.values()))
all_boundary_f.extend(list(boundary_f.values()))
object_metrics[name] = (iou, boundary_f)
global_j = np.array(all_iou).mean()
global_f = np.array(all_boundary_f).mean()
global_jf = (global_j + global_f) / 2
time_taken = time.time() - start
"""
Build string for reporting results
"""
# find max length for padding
ml = max(*[len(n) for n in object_metrics.keys()], len("Global score"))
# build header
out_string = f'{"sequence":<{ml}},{"obj":>3}, {"J&F":>4}, {"J":>4}, {"F":>4}\n'
out_string += f'{"Global score":<{ml}},{"":>3}, {global_jf:.1f}, {global_j:.1f}, {global_f:.1f}\n'
# append one line for each object
for name, (iou, boundary_f) in object_metrics.items():
for object_idx in iou.keys():
j, f = iou[object_idx], boundary_f[object_idx]
jf = (j + f) / 2
out_string += (
f"{name:<{ml}},{object_idx:03}, {jf:>4.1f}, {j:>4.1f}, {f:>4.1f}\n"
)
# print to console
if verbose:
print(out_string.replace(",", " "), end="")
print("\nSummary:")
print(
f"Global score: J&F: {global_jf:.1f} J: {global_j:.1f} F: {global_f:.1f}"
)
print(f"Time taken: {time_taken:.2f}s")
# print to file
result_path = path.join(mask_root, "results.csv")
print(f"Saving the results to {result_path}")
with open(result_path, "w") as f:
f.write(out_string)
all_global_jf.append(global_jf)
all_global_j.append(global_j)
all_global_f.append(global_f)
all_object_metrics.append(object_metrics)
return all_global_jf, all_global_j, all_global_f, all_object_metrics