deep-person-reid/torchreid/utils/reidtools.py

122 lines
4.6 KiB
Python

from __future__ import absolute_import
from __future__ import print_function
__all__ = ['visualize_ranked_results']
import numpy as np
import os
import os.path as osp
import shutil
import cv2
from matplotlib import pyplot as plt
from .tools import mkdir_if_missing
GRID_SPACING = 10
QUERY_EXTRA_SPACING = 90
BW = 5 # border width
GREEN = (0, 255, 0)
RED = (0, 0, 255)
def visualize_ranked_results(distmat, dataset, data_type, width=128, height=256, save_dir='', topk=10):
"""Visualizes ranked results.
Supports both image-reid and video-reid.
For image-reid, ranks will be plotted in a single figure. For video-reid, ranks will be
saved in folders each containing a tracklet.
Args:
distmat (numpy.ndarray): distance matrix of shape (num_query, num_gallery).
dataset (tuple): a 2-tuple containing (query, gallery), each of which contains
tuples of (img_path(s), pid, camid).
data_type (str): "image" or "video".
width (int, optional): resized image width. Default is 128.
height (int, optional): resized image height. Default is 256.
save_dir (str): directory to save output images.
topk (int, optional): denoting top-k images in the rank list to be visualized.
Default is 10.
"""
num_q, num_g = distmat.shape
mkdir_if_missing(save_dir)
print('# query: {}\n# gallery {}'.format(num_q, num_g))
print('Visualizing top-{} ranks ...'.format(topk))
query, gallery = dataset
assert num_q == len(query)
assert num_g == len(gallery)
indices = np.argsort(distmat, axis=1)
def _cp_img_to(src, dst, rank, prefix, matched=False):
"""
Args:
src: image path or tuple (for vidreid)
dst: target directory
rank: int, denoting ranked position, starting from 1
prefix: string
matched: bool
"""
if isinstance(src, tuple) or isinstance(src, list):
if prefix == 'gallery':
suffix = 'TRUE' if matched else 'FALSE'
dst = osp.join(dst, prefix + '_top' + str(rank).zfill(3)) + '_' + suffix
else:
dst = osp.join(dst, prefix + '_top' + str(rank).zfill(3))
mkdir_if_missing(dst)
for img_path in src:
shutil.copy(img_path, dst)
else:
dst = osp.join(dst, prefix + '_top' + str(rank).zfill(3) + '_name_' + osp.basename(src))
shutil.copy(src, dst)
for q_idx in range(num_q):
qimg_path, qpid, qcamid = query[q_idx]
num_cols = topk + 1
grid_img = 255 * np.ones((height, num_cols*width+topk*GRID_SPACING+QUERY_EXTRA_SPACING, 3), dtype=np.uint8)
if data_type == 'image':
qimg = cv2.imread(qimg_path)
qimg = cv2.resize(qimg, (width, height))
qimg = cv2.copyMakeBorder(qimg, BW, BW, BW, BW, cv2.BORDER_CONSTANT, value=(0, 0, 0))
qimg = cv2.resize(qimg, (width, height)) # resize twice to ensure that the border width is consistent across images
grid_img[:, :width, :] = qimg
else:
qdir = osp.join(save_dir, osp.basename(osp.splitext(qimg_path)[0]))
mkdir_if_missing(qdir)
_cp_img_to(qimg_path, qdir, rank=0, prefix='query')
rank_idx = 1
for g_idx in indices[q_idx,:]:
gimg_path, gpid, gcamid = gallery[g_idx]
invalid = (qpid == gpid) & (qcamid == gcamid)
if not invalid:
matched = gpid==qpid
if data_type == 'image':
border_color = GREEN if matched else RED
gimg = cv2.imread(gimg_path)
gimg = cv2.resize(gimg, (width, height))
gimg = cv2.copyMakeBorder(gimg, BW, BW, BW, BW, cv2.BORDER_CONSTANT, value=border_color)
gimg = cv2.resize(gimg, (width, height))
start = rank_idx*width + rank_idx*GRID_SPACING + QUERY_EXTRA_SPACING
end = (rank_idx+1)*width + rank_idx*GRID_SPACING + QUERY_EXTRA_SPACING
grid_img[:, start: end, :] = gimg
else:
_cp_img_to(gimg_path, qdir, rank=rank_idx, prefix='gallery', matched=matched)
rank_idx += 1
if rank_idx > topk:
break
imname = osp.basename(osp.splitext(qimg_path)[0])
cv2.imwrite(osp.join(save_dir, imname+'.jpg'), grid_img)
if (q_idx+1) % 100 == 0:
print('- done {}/{}'.format(q_idx+1, num_q))
print('Done. Images have been saved to "{}" ...'.format(save_dir))