# coding: utf-8

import os
import pickle

import matplotlib
import pandas as pd

matplotlib.use('Agg')
import matplotlib.pyplot as plt
import timeit
import sklearn
import argparse
from sklearn.metrics import roc_curve, auc

from menpo.visualize.viewmatplotlib import sample_colours_from_colourmap
from prettytable import PrettyTable
from pathlib import Path
import sys
import warnings

sys.path.insert(0, "../")
warnings.filterwarnings("ignore")

parser = argparse.ArgumentParser(description='do ijb test')
# general
parser.add_argument('--model-prefix', default='', help='path to load model.')
parser.add_argument('--image-path', default='', type=str, help='')
parser.add_argument('--result-dir', default='.', type=str, help='')
parser.add_argument('--batch-size', default=128, type=int, help='')
parser.add_argument('--network', default='iresnet50', type=str, help='')
parser.add_argument('--job', default='insightface', type=str, help='job name')
parser.add_argument('--target', default='IJBC', type=str, help='target, set to IJBC or IJBB')
args = parser.parse_args()

target = args.target
model_path = args.model_prefix
image_path = args.image_path
result_dir = args.result_dir
gpu_id = None
use_norm_score = True  # if Ture, TestMode(N1)
use_detector_score = True  # if Ture, TestMode(D1)
use_flip_test = True  # if Ture, TestMode(F1)
job = args.job
batch_size = args.batch_size

import cv2
import numpy as np
import torch
from skimage import transform as trans
import backbones


class Embedding(object):
    def __init__(self, prefix, data_shape, batch_size=1):
        image_size = (112, 112)
        self.image_size = image_size
        weight = torch.load(prefix)
        resnet = eval("backbones.{}".format(args.network))(False).cuda()
        resnet.load_state_dict(weight)
        model = torch.nn.DataParallel(resnet)
        self.model = model
        self.model.eval()
        src = np.array([
            [30.2946, 51.6963],
            [65.5318, 51.5014],
            [48.0252, 71.7366],
            [33.5493, 92.3655],
            [62.7299, 92.2041]], dtype=np.float32)
        src[:, 0] += 8.0
        self.src = src
        self.batch_size = batch_size
        self.data_shape = data_shape

    def get(self, rimg, landmark):

        assert landmark.shape[0] == 68 or landmark.shape[0] == 5
        assert landmark.shape[1] == 2
        if landmark.shape[0] == 68:
            landmark5 = np.zeros((5, 2), dtype=np.float32)
            landmark5[0] = (landmark[36] + landmark[39]) / 2
            landmark5[1] = (landmark[42] + landmark[45]) / 2
            landmark5[2] = landmark[30]
            landmark5[3] = landmark[48]
            landmark5[4] = landmark[54]
        else:
            landmark5 = landmark
        tform = trans.SimilarityTransform()
        tform.estimate(landmark5, self.src)
        M = tform.params[0:2, :]
        img = cv2.warpAffine(rimg,
                             M, (self.image_size[1], self.image_size[0]),
                             borderValue=0.0)
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        img_flip = np.fliplr(img)
        img = np.transpose(img, (2, 0, 1))  # 3*112*112, RGB
        img_flip = np.transpose(img_flip, (2, 0, 1))
        input_blob = np.zeros((2, 3, self.image_size[1], self.image_size[0]), dtype=np.uint8)
        input_blob[0] = img
        input_blob[1] = img_flip
        return input_blob

    @torch.no_grad()
    def forward_db(self, batch_data):
        imgs = torch.Tensor(batch_data).cuda()
        imgs.div_(255).sub_(0.5).div_(0.5)
        feat = self.model(imgs)
        feat = feat.reshape([self.batch_size, 2 * feat.shape[1]])
        return feat.cpu().numpy()


# 将一个list尽量均分成n份，限制len(list)==n，份数大于原list内元素个数则分配空list[]
def divideIntoNstrand(listTemp, n):
    twoList = [[] for i in range(n)]
    for i, e in enumerate(listTemp):
        twoList[i % n].append(e)
    return twoList


def read_template_media_list(path):
    # ijb_meta = np.loadtxt(path, dtype=str)
    ijb_meta = pd.read_csv(path, sep=' ', header=None).values
    templates = ijb_meta[:, 1].astype(np.int)
    medias = ijb_meta[:, 2].astype(np.int)
    return templates, medias


# In[ ]:


def read_template_pair_list(path):
    # pairs = np.loadtxt(path, dtype=str)
    pairs = pd.read_csv(path, sep=' ', header=None).values
    # print(pairs.shape)
    # print(pairs[:, 0].astype(np.int))
    t1 = pairs[:, 0].astype(np.int)
    t2 = pairs[:, 1].astype(np.int)
    label = pairs[:, 2].astype(np.int)
    return t1, t2, label


# In[ ]:


def read_image_feature(path):
    with open(path, 'rb') as fid:
        img_feats = pickle.load(fid)
    return img_feats


# In[ ]:


def get_image_feature(img_path, files_list, model_path, epoch, gpu_id):
    batch_size = args.batch_size
    data_shape = (3, 112, 112)

    files = files_list
    print('files:', len(files))
    rare_size = len(files) % batch_size
    faceness_scores = []
    batch = 0
    img_feats = np.empty((len(files), 1024), dtype=np.float32)

    batch_data = np.empty((2 * batch_size, 3, 112, 112))
    embedding = Embedding(model_path, data_shape, batch_size)
    for img_index, each_line in enumerate(files[:len(files) - rare_size]):
        name_lmk_score = each_line.strip().split(' ')
        img_name = os.path.join(img_path, name_lmk_score[0])
        img = cv2.imread(img_name)
        lmk = np.array([float(x) for x in name_lmk_score[1:-1]],
                       dtype=np.float32)
        lmk = lmk.reshape((5, 2))
        input_blob = embedding.get(img, lmk)

        batch_data[2 * (img_index - batch * batch_size)][:] = input_blob[0]
        batch_data[2 * (img_index - batch * batch_size) + 1][:] = input_blob[1]
        if (img_index + 1) % batch_size == 0:
            print('batch', batch)
            img_feats[batch * batch_size:batch * batch_size +
                                         batch_size][:] = embedding.forward_db(batch_data)
            batch += 1
        faceness_scores.append(name_lmk_score[-1])

    batch_data = np.empty((2 * rare_size, 3, 112, 112))
    embedding = Embedding(model_path, data_shape, rare_size)
    for img_index, each_line in enumerate(files[len(files) - rare_size:]):
        name_lmk_score = each_line.strip().split(' ')
        img_name = os.path.join(img_path, name_lmk_score[0])
        img = cv2.imread(img_name)
        lmk = np.array([float(x) for x in name_lmk_score[1:-1]],
                       dtype=np.float32)
        lmk = lmk.reshape((5, 2))
        input_blob = embedding.get(img, lmk)
        batch_data[2 * img_index][:] = input_blob[0]
        batch_data[2 * img_index + 1][:] = input_blob[1]
        if (img_index + 1) % rare_size == 0:
            print('batch', batch)
            img_feats[len(files) -
                      rare_size:][:] = embedding.forward_db(batch_data)
            batch += 1
        faceness_scores.append(name_lmk_score[-1])
    faceness_scores = np.array(faceness_scores).astype(np.float32)
    # img_feats = np.ones( (len(files), 1024), dtype=np.float32) * 0.01
    # faceness_scores = np.ones( (len(files), ), dtype=np.float32 )
    return img_feats, faceness_scores


# In[ ]:


def image2template_feature(img_feats=None, templates=None, medias=None):
    # ==========================================================
    # 1. face image feature l2 normalization. img_feats:[number_image x feats_dim]
    # 2. compute media feature.
    # 3. compute template feature.
    # ==========================================================
    unique_templates = np.unique(templates)
    template_feats = np.zeros((len(unique_templates), img_feats.shape[1]))

    for count_template, uqt in enumerate(unique_templates):

        (ind_t,) = np.where(templates == uqt)
        face_norm_feats = img_feats[ind_t]
        face_medias = medias[ind_t]
        unique_medias, unique_media_counts = np.unique(face_medias,
                                                       return_counts=True)
        media_norm_feats = []
        for u, ct in zip(unique_medias, unique_media_counts):
            (ind_m,) = np.where(face_medias == u)
            if ct == 1:
                media_norm_feats += [face_norm_feats[ind_m]]
            else:  # image features from the same video will be aggregated into one feature
                media_norm_feats += [
                    np.mean(face_norm_feats[ind_m], axis=0, keepdims=True)
                ]
        media_norm_feats = np.array(media_norm_feats)
        # media_norm_feats = media_norm_feats / np.sqrt(np.sum(media_norm_feats ** 2, -1, keepdims=True))
        template_feats[count_template] = np.sum(media_norm_feats, axis=0)
        if count_template % 2000 == 0:
            print('Finish Calculating {} template features.'.format(
                count_template))
    # template_norm_feats = template_feats / np.sqrt(np.sum(template_feats ** 2, -1, keepdims=True))
    template_norm_feats = sklearn.preprocessing.normalize(template_feats)
    # print(template_norm_feats.shape)
    return template_norm_feats, unique_templates


# In[ ]:


def verification(template_norm_feats=None,
                 unique_templates=None,
                 p1=None,
                 p2=None):
    # ==========================================================
    #         Compute set-to-set Similarity Score.
    # ==========================================================
    template2id = np.zeros((max(unique_templates) + 1, 1), dtype=int)
    for count_template, uqt in enumerate(unique_templates):
        template2id[uqt] = count_template

    score = np.zeros((len(p1),))  # save cosine distance between pairs

    total_pairs = np.array(range(len(p1)))
    batchsize = 100000  # small batchsize instead of all pairs in one batch due to the memory limiation
    sublists = [
        total_pairs[i:i + batchsize] for i in range(0, len(p1), batchsize)
    ]
    total_sublists = len(sublists)
    for c, s in enumerate(sublists):
        feat1 = template_norm_feats[template2id[p1[s]]]
        feat2 = template_norm_feats[template2id[p2[s]]]
        similarity_score = np.sum(feat1 * feat2, -1)
        score[s] = similarity_score.flatten()
        if c % 10 == 0:
            print('Finish {}/{} pairs.'.format(c, total_sublists))
    return score


# In[ ]:
def verification2(template_norm_feats=None,
                  unique_templates=None,
                  p1=None,
                  p2=None):
    template2id = np.zeros((max(unique_templates) + 1, 1), dtype=int)
    for count_template, uqt in enumerate(unique_templates):
        template2id[uqt] = count_template
    score = np.zeros((len(p1),))  # save cosine distance between pairs
    total_pairs = np.array(range(len(p1)))
    batchsize = 100000  # small batchsize instead of all pairs in one batch due to the memory limiation
    sublists = [
        total_pairs[i:i + batchsize] for i in range(0, len(p1), batchsize)
    ]
    total_sublists = len(sublists)
    for c, s in enumerate(sublists):
        feat1 = template_norm_feats[template2id[p1[s]]]
        feat2 = template_norm_feats[template2id[p2[s]]]
        similarity_score = np.sum(feat1 * feat2, -1)
        score[s] = similarity_score.flatten()
        if c % 10 == 0:
            print('Finish {}/{} pairs.'.format(c, total_sublists))
    return score


def read_score(path):
    with open(path, 'rb') as fid:
        img_feats = pickle.load(fid)
    return img_feats


# # Step1: Load Meta Data

# In[ ]:

assert target == 'IJBC' or target == 'IJBB'

# =============================================================
# load image and template relationships for template feature embedding
# tid --> template id,  mid --> media id
# format:
#           image_name tid mid
# =============================================================
start = timeit.default_timer()
templates, medias = read_template_media_list(
    os.path.join('%s/meta' % image_path,
                 '%s_face_tid_mid.txt' % target.lower()))
stop = timeit.default_timer()
print('Time: %.2f s. ' % (stop - start))

# In[ ]:

# =============================================================
# load template pairs for template-to-template verification
# tid : template id,  label : 1/0
# format:
#           tid_1 tid_2 label
# =============================================================
start = timeit.default_timer()
p1, p2, label = read_template_pair_list(
    os.path.join('%s/meta' % image_path,
                 '%s_template_pair_label.txt' % target.lower()))
stop = timeit.default_timer()
print('Time: %.2f s. ' % (stop - start))

# # Step 2: Get Image Features

# In[ ]:

# =============================================================
# load image features
# format:
#           img_feats: [image_num x feats_dim] (227630, 512)
# =============================================================
start = timeit.default_timer()
img_path = '%s/loose_crop' % image_path
img_list_path = '%s/meta/%s_name_5pts_score.txt' % (image_path, target.lower())
img_list = open(img_list_path)
files = img_list.readlines()
# files_list = divideIntoNstrand(files, rank_size)
files_list = files

# img_feats
# for i in range(rank_size):
img_feats, faceness_scores = get_image_feature(img_path, files_list,
                                               model_path, 0, gpu_id)
stop = timeit.default_timer()
print('Time: %.2f s. ' % (stop - start))
print('Feature Shape: ({} , {}) .'.format(img_feats.shape[0],
                                          img_feats.shape[1]))

# # Step3: Get Template Features

# In[ ]:

# =============================================================
# compute template features from image features.
# =============================================================
start = timeit.default_timer()
# ==========================================================
# Norm feature before aggregation into template feature?
# Feature norm from embedding network and faceness score are able to decrease weights for noise samples (not face).
# ==========================================================
# 1. FaceScore （Feature Norm）
# 2. FaceScore （Detector）

if use_flip_test:
    # concat --- F1
    # img_input_feats = img_feats
    # add --- F2
    img_input_feats = img_feats[:, 0:img_feats.shape[1] //
                                     2] + img_feats[:, img_feats.shape[1] // 2:]
else:
    img_input_feats = img_feats[:, 0:img_feats.shape[1] // 2]

if use_norm_score:
    img_input_feats = img_input_feats
else:
    # normalise features to remove norm information
    img_input_feats = img_input_feats / np.sqrt(
        np.sum(img_input_feats ** 2, -1, keepdims=True))

if use_detector_score:
    print(img_input_feats.shape, faceness_scores.shape)
    img_input_feats = img_input_feats * faceness_scores[:, np.newaxis]
else:
    img_input_feats = img_input_feats

template_norm_feats, unique_templates = image2template_feature(
    img_input_feats, templates, medias)
stop = timeit.default_timer()
print('Time: %.2f s. ' % (stop - start))

# # Step 4: Get Template Similarity Scores

# In[ ]:

# =============================================================
# compute verification scores between template pairs.
# =============================================================
start = timeit.default_timer()
score = verification(template_norm_feats, unique_templates, p1, p2)
stop = timeit.default_timer()
print('Time: %.2f s. ' % (stop - start))

# In[ ]:
save_path = os.path.join(result_dir, args.job)
# save_path = result_dir + '/%s_result' % target

if not os.path.exists(save_path):
    os.makedirs(save_path)

score_save_file = os.path.join(save_path, "%s.npy" % target.lower())
np.save(score_save_file, score)

# # Step 5: Get ROC Curves and TPR@FPR Table

# In[ ]:

files = [score_save_file]
methods = []
scores = []
for file in files:
    methods.append(Path(file).stem)
    scores.append(np.load(file))

methods = np.array(methods)
scores = dict(zip(methods, scores))
colours = dict(
    zip(methods, sample_colours_from_colourmap(methods.shape[0], 'Set2')))
x_labels = [10 ** -6, 10 ** -5, 10 ** -4, 10 ** -3, 10 ** -2, 10 ** -1]
tpr_fpr_table = PrettyTable(['Methods'] + [str(x) for x in x_labels])
fig = plt.figure()
for method in methods:
    fpr, tpr, _ = roc_curve(label, scores[method])
    roc_auc = auc(fpr, tpr)
    fpr = np.flipud(fpr)
    tpr = np.flipud(tpr)  # select largest tpr at same fpr
    plt.plot(fpr,
             tpr,
             color=colours[method],
             lw=1,
             label=('[%s (AUC = %0.4f %%)]' %
                    (method.split('-')[-1], roc_auc * 100)))
    tpr_fpr_row = []
    tpr_fpr_row.append("%s-%s" % (method, target))
    for fpr_iter in np.arange(len(x_labels)):
        _, min_index = min(
            list(zip(abs(fpr - x_labels[fpr_iter]), range(len(fpr)))))
        tpr_fpr_row.append('%.2f' % (tpr[min_index] * 100))
    tpr_fpr_table.add_row(tpr_fpr_row)
plt.xlim([10 ** -6, 0.1])
plt.ylim([0.3, 1.0])
plt.grid(linestyle='--', linewidth=1)
plt.xticks(x_labels)
plt.yticks(np.linspace(0.3, 1.0, 8, endpoint=True))
plt.xscale('log')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('ROC on IJB')
plt.legend(loc="lower right")
fig.savefig(os.path.join(save_path, '%s.pdf' % target.lower()))
print(tpr_fpr_table)
