# encoding: utf-8 """ @author: liaoxingyu @contact: sherlockliao01@gmail.com """ import os import cv2 import matplotlib.figure as mplfigure import matplotlib.pyplot as plt import numpy as np import tqdm from torch import nn 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, sim, q_pids, g_pids, q_camids, g_camids): self.all_ap = all_ap self.sim = sim self.q_pids = q_pids self.g_pids = g_pids self.q_camids = q_camids self.g_camids = g_camids self.indices = np.argsort(1 - sim, 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, actmap=False): fig, axes = plt.subplots(1, max_rank + 1, figsize=(3 * max_rank, 5)) fig.suptitle('query/AP/camid sim/true(false)/camid') 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) axes.flat[0].imshow(query_img) axes.flat[0].set_title('{}/AP:{:.2f}/cam{}'.format(query_name, self.all_ap[q_idx], cam_id)) # print('query' + query_info['img_path'].split('/')[-1]) for i in range(max_rank): 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' axes.flat[i + 1].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' axes.flat[i + 1].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)) axes.flat[i + 1].imshow(gallery_img) # print('/'.join(gallery_info['img_path'].split('/')[-2:])) axes.flat[i + 1].set_title(f'{self.sim[q_idx, sort_idx[i]]:.3f}/{label}/cam{cam_id}') # 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') filepath = os.path.join(output, "{}.jpg".format(cnt)) fig.savefig(filepath) plt.cla() def vis_ranking_list(self, output, num_vis=100, rank_sort='ascending', max_rank=5, actmap=False): """ Args: output (str): a file or directory to save rankling list result. rank_sort (str): save visualization results by which order, if rank_sort is ascending, AP from low to high, vice versa. num_vis (int): max_rank (int): actmap (bool): """ assert rank_sort in ['ascending', 'descending'], "{} not match [ascending, descending]".format(rank_sort) PathManager.mkdirs(output) 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, actmap) def plot_roc_curve(self): pos_sim, neg_sim = [], [] for i, q in enumerate(self.q_pids): cmc, sort_idx = self.get_matched_result(i) # remove same id in same camera for j in range(len(cmc)): if cmc[j] == 1: pos_sim.append(self.sim[i, sort_idx[j]]) else: neg_sim.append(self.sim[i, sort_idx[j]]) fig = plt.figure(figsize=(10, 5)) plt.hist(pos_sim, bins=80, alpha=0.7, density=True, color='red', label='positive') plt.hist(neg_sim, bins=80, alpha=0.5, density=True, color='blue', label='negative') plt.xticks(np.arange(-0.3, 0.8, 0.1)) plt.title('positive and negative pair distribution') return pos_sim, neg_sim 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 = mplfigure(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