# encoding: utf-8
"""
@author: liaoxingyu
@contact: sherlockliao01@gmail.com
"""
import os
import pickle
import random
import matplotlib.pyplot as plt
import numpy as np
import tqdm
from scipy.stats import norm
from sklearn import metrics
from .file_io import PathManager
class Visualizer:
r"""Visualize images(activation map) ranking list of features generated by reid models."""
def __init__(self, dataset):
self.dataset = dataset
def get_model_output(self, all_ap, dist, q_pids, g_pids, q_camids, g_camids):
self.all_ap = all_ap
self.dist = dist
self.sim = 1 - dist
self.q_pids = q_pids
self.g_pids = g_pids
self.q_camids = q_camids
self.g_camids = g_camids
self.indices = np.argsort(dist, axis=1)
self.matches = (g_pids[self.indices] == q_pids[:, np.newaxis]).astype(np.int32)
self.num_query = len(q_pids)
def get_matched_result(self, q_index):
q_pid = self.q_pids[q_index]
q_camid = self.q_camids[q_index]
order = self.indices[q_index]
remove = (self.g_pids[order] == q_pid) & (self.g_camids[order] == q_camid)
keep = np.invert(remove)
cmc = self.matches[q_index][keep]
sort_idx = order[keep]
return cmc, sort_idx
def save_rank_result(self, query_indices, output, max_rank=5, vis_label=False, label_sort='ascending',
actmap=False):
if vis_label:
fig, axes = plt.subplots(2, max_rank + 1, figsize=(3 * max_rank, 12))
else:
fig, axes = plt.subplots(1, max_rank + 1, figsize=(3 * max_rank, 6))
for cnt, q_idx in enumerate(tqdm.tqdm(query_indices)):
all_imgs = []
cmc, sort_idx = self.get_matched_result(q_idx)
query_info = self.dataset[q_idx]
query_img = query_info['images']
cam_id = query_info['camid']
query_name = query_info['img_path'].split('/')[-1]
all_imgs.append(query_img)
query_img = np.rollaxis(np.asarray(query_img.numpy(), dtype=np.uint8), 0, 3)
plt.clf()
ax = fig.add_subplot(1, max_rank + 1, 1)
ax.imshow(query_img)
ax.set_title('{}/{:.2f}/cam{}'.format(query_name, self.all_ap[q_idx], cam_id))
ax.axis("off")
for i in range(max_rank):
if vis_label:
ax = fig.add_subplot(2, max_rank + 1, i + 2)
else:
ax = fig.add_subplot(1, max_rank + 1, i + 2)
g_idx = self.num_query + sort_idx[i]
gallery_info = self.dataset[g_idx]
gallery_img = gallery_info['images']
cam_id = gallery_info['camid']
all_imgs.append(gallery_img)
gallery_img = np.rollaxis(np.asarray(gallery_img, dtype=np.uint8), 0, 3)
if cmc[i] == 1:
label = 'true'
ax.add_patch(plt.Rectangle(xy=(0, 0), width=gallery_img.shape[1] - 1,
height=gallery_img.shape[0] - 1, edgecolor=(1, 0, 0),
fill=False, linewidth=5))
else:
label = 'false'
ax.add_patch(plt.Rectangle(xy=(0, 0), width=gallery_img.shape[1] - 1,
height=gallery_img.shape[0] - 1,
edgecolor=(0, 0, 1), fill=False, linewidth=5))
ax.imshow(gallery_img)
ax.set_title(f'{self.sim[q_idx, sort_idx[i]]:.3f}/{label}/cam{cam_id}')
ax.axis("off")
# if actmap:
# act_outputs = []
#
# def hook_fns_forward(module, input, output):
# act_outputs.append(output.cpu())
#
# all_imgs = np.stack(all_imgs, axis=0) # (b, 3, h, w)
# all_imgs = torch.from_numpy(all_imgs).float()
# # normalize
# all_imgs = all_imgs.sub_(self.mean).div_(self.std)
# sz = list(all_imgs.shape[-2:])
# handle = m.base.register_forward_hook(hook_fns_forward)
# with torch.no_grad():
# _ = m(all_imgs.cuda())
# handle.remove()
# acts = self.get_actmap(act_outputs[0], sz)
# for i in range(top + 1):
# axes.flat[i].imshow(acts[i], alpha=0.3, cmap='jet')
if vis_label:
label_indice = np.where(cmc == 1)[0]
if label_sort == "ascending": label_indice = label_indice[::-1]
label_indice = label_indice[:max_rank]
for i in range(max_rank):
if i >= len(label_indice): break
j = label_indice[i]
g_idx = self.num_query + sort_idx[j]
gallery_info = self.dataset[g_idx]
gallery_img = gallery_info['images']
cam_id = gallery_info['camid']
gallery_img = np.rollaxis(np.asarray(gallery_img, dtype=np.uint8), 0, 3)
ax = fig.add_subplot(2, max_rank + 1, max_rank + 3 + i)
ax.add_patch(plt.Rectangle(xy=(0, 0), width=gallery_img.shape[1] - 1,
height=gallery_img.shape[0] - 1,
edgecolor=(1, 0, 0),
fill=False, linewidth=5))
ax.imshow(gallery_img)
ax.set_title(f'{self.sim[q_idx, sort_idx[j]]:.3f}/cam{cam_id}')
ax.axis("off")
plt.tight_layout()
filepath = os.path.join(output, "{}.jpg".format(cnt))
fig.savefig(filepath)
def vis_rank_list(self, output, vis_label, num_vis=100, rank_sort="ascending", label_sort="ascending", max_rank=5,
actmap=False):
r"""Visualize rank list of query instance
Args:
output (str): a directory to save rank list result.
vis_label (bool): if visualize label of query
num_vis (int):
rank_sort (str): save visualization results by which order,
if rank_sort is ascending, AP from low to high, vice versa.
label_sort (bool):
max_rank (int): maximum number of rank result to visualize
actmap (bool):
"""
assert rank_sort in ['ascending', 'descending'], "{} not match [ascending, descending]".format(rank_sort)
query_indices = np.argsort(self.all_ap)
if rank_sort == 'descending': query_indices = query_indices[::-1]
query_indices = query_indices[:num_vis]
self.save_rank_result(query_indices, output, max_rank, vis_label, label_sort, actmap)
def vis_roc_curve(self, output):
PathManager.mkdirs(output)
pos, neg = [], []
for i, q in enumerate(self.q_pids):
cmc, sort_idx = self.get_matched_result(i) # remove same id in same camera
ind_pos = np.where(cmc == 1)[0]
q_dist = self.dist[i]
pos.extend(q_dist[sort_idx[ind_pos]])
ind_neg = np.where(cmc == 0)[0]
neg.extend(q_dist[sort_idx[ind_neg]])
scores = np.hstack((pos, neg))
labels = np.hstack((np.zeros(len(pos)), np.ones(len(neg))))
fpr, tpr, thresholds = metrics.roc_curve(labels, scores)
self.plot_roc_curve(fpr, tpr)
filepath = os.path.join(output, "roc.jpg")
plt.savefig(filepath)
self.plot_distribution(pos, neg)
filepath = os.path.join(output, "pos_neg_dist.jpg")
plt.savefig(filepath)
return fpr, tpr, pos, neg
@staticmethod
def plot_roc_curve(fpr, tpr, name='model', fig=None):
if fig is None:
fig = plt.figure()
plt.semilogx(np.arange(0, 1, 0.01), np.arange(0, 1, 0.01), 'r', linestyle='--', label='Random guess')
plt.semilogx(fpr, tpr, color=(random.uniform(0, 1), random.uniform(0, 1), random.uniform(0, 1)),
label='ROC curve with {}'.format(name))
plt.title('Receiver Operating Characteristic')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.legend(loc='best')
return fig
@staticmethod
def plot_distribution(pos, neg, name='model', fig=None):
if fig is None:
fig = plt.figure()
pos_color = (random.uniform(0, 1), random.uniform(0, 1), random.uniform(0, 1))
n, bins, _ = plt.hist(pos, bins=80, alpha=0.7, density=True,
color=pos_color,
label='positive with {}'.format(name))
mu = np.mean(pos)
sigma = np.std(pos)
y = norm.pdf(bins, mu, sigma) # fitting curve
plt.plot(bins, y, color=pos_color) # plot y curve
neg_color = (random.uniform(0, 1), random.uniform(0, 1), random.uniform(0, 1))
n, bins, _ = plt.hist(neg, bins=80, alpha=0.5, density=True,
color=neg_color,
label='negative with {}'.format(name))
mu = np.mean(neg)
sigma = np.std(neg)
y = norm.pdf(bins, mu, sigma) # fitting curve
plt.plot(bins, y, color=neg_color) # plot y curve
plt.xticks(np.arange(0, 1.5, 0.1))
plt.title('positive and negative pairs distribution')
plt.legend(loc='best')
return fig
@staticmethod
def save_roc_info(output, fpr, tpr, pos, neg):
results = {
"fpr": np.asarray(fpr),
"tpr": np.asarray(tpr),
"pos": np.asarray(pos),
"neg": np.asarray(neg),
}
with open(os.path.join(output, "roc_info.pickle"), "wb") as handle:
pickle.dump(results, handle, protocol=pickle.HIGHEST_PROTOCOL)
@staticmethod
def load_roc_info(path):
with open(path, 'rb') as handle: res = pickle.load(handle)
return res
# def plot_camera_dist(self):
# same_cam, diff_cam = [], []
# for i, q in enumerate(self.q_pids):
# q_camid = self.q_camids[i]
#
# order = self.indices[i]
# same = (self.g_pids[order] == q) & (self.g_camids[order] == q_camid)
# diff = (self.g_pids[order] == q) & (self.g_camids[order] != q_camid)
# sameCam_idx = order[same]
# diffCam_idx = order[diff]
#
# same_cam.extend(self.sim[i, sameCam_idx])
# diff_cam.extend(self.sim[i, diffCam_idx])
#
# fig = plt.figure(figsize=(10, 5))
# plt.hist(same_cam, bins=80, alpha=0.7, density=True, color='red', label='same camera')
# plt.hist(diff_cam, bins=80, alpha=0.5, density=True, color='blue', label='diff camera')
# plt.xticks(np.arange(0.1, 1.0, 0.1))
# plt.title('positive and negative pair distribution')
# return fig
# def get_actmap(self, features, sz):
# """
# :param features: (1, 2048, 16, 8) activation map
# :return:
# """
# features = (features ** 2).sum(1) # (1, 16, 8)
# b, h, w = features.size()
# features = features.view(b, h * w)
# features = nn.functional.normalize(features, p=2, dim=1)
# acts = features.view(b, h, w)
# all_acts = []
# for i in range(b):
# act = acts[i].numpy()
# act = cv2.resize(act, (sz[1], sz[0]))
# act = 255 * (act - act.max()) / (act.max() - act.min() + 1e-12)
# act = np.uint8(np.floor(act))
# all_acts.append(act)
# return all_acts