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-rwxr-xr-xpart2.py282
-rw-r--r--rerank.py82
2 files changed, 158 insertions, 206 deletions
diff --git a/part2.py b/part2.py
index 299fdcd..1792f81 100755
--- a/part2.py
+++ b/part2.py
@@ -1,11 +1,9 @@
-#!/usr/bin/env python
+#!/usr/bin/python -W ignore::DeprecationWarning
# Author: Vasil Zlatanov, Nunzio Pucci
# EE4 Pattern Recognition coursework
#
-# usage: train.py [-h] -i DATA [-m EIGEN] [-M REIGEN] [-e ENSEMBLE] [-b]
-# [-R RANDOM] [-n NEIGHBORS] [-f FACES] [-c] [-s SEED]
-# [-t SPLIT] [-2] [-p] [-l] [-r RECONSTRUCT] [-cm] [-q] [-pr]
-# [-alt]
+# usage: part2.py [-h] [-t] [-cm] [-km] [-ma] [-e] [-r] [-ka RERANKA]
+# [-kb RERANKB] [-v]
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
@@ -30,90 +28,28 @@ from numpy import genfromtxt
from numpy import linalg as LA
from timeit import default_timer as timer
from scipy.spatial.distance import cdist
+from rerank import re_ranking
-#prob query, gal train
-def re_ranking(probFea,galFea,k1,k2,lambda_value, MemorySave = False, Minibatch = 2000):
-
- query_num = probFea.shape[0]
- all_num = query_num + galFea.shape[0]
- feat = np.append(probFea,galFea,axis = 0)
- feat = feat.astype(np.float16)
- print('computing original distance')
- if MemorySave:
- original_dist = np.zeros(shape = [all_num,all_num],dtype = np.float16)
- i = 0
- while True:
- it = i + Minibatch
- if it < np.shape(feat)[0]:
- original_dist[i:it,] = np.power(cdist(feat[i:it,],feat),2).astype(np.float16)
- else:
- original_dist[i:,:] = np.power(cdist(feat[i:,],feat),2).astype(np.float16)
- break
- i = it
- else:
- original_dist = cdist(feat,feat).astype(np.float16)
- original_dist = np.power(original_dist,2).astype(np.float16)
- del feat
- gallery_num = original_dist.shape[0]
- original_dist = np.transpose(original_dist/np.max(original_dist,axis = 0))
- V = np.zeros_like(original_dist).astype(np.float16)
- initial_rank = np.argsort(original_dist).astype(np.int32)
-
-
- print('starting re_ranking')
- for i in range(all_num):
- # k-reciprocal neighbors
- forward_k_neigh_index = initial_rank[i,:k1+1]
- backward_k_neigh_index = initial_rank[forward_k_neigh_index,:k1+1]
- fi = np.where(backward_k_neigh_index==i)[0]
- k_reciprocal_index = forward_k_neigh_index[fi]
- k_reciprocal_expansion_index = k_reciprocal_index
- for j in range(len(k_reciprocal_index)):
- candidate = k_reciprocal_index[j]
- candidate_forward_k_neigh_index = initial_rank[candidate,:int(np.around(k1/2))+1]
- candidate_backward_k_neigh_index = initial_rank[candidate_forward_k_neigh_index,:int(np.around(k1/2))+1]
- fi_candidate = np.where(candidate_backward_k_neigh_index == candidate)[0]
- candidate_k_reciprocal_index = candidate_forward_k_neigh_index[fi_candidate]
- if len(np.intersect1d(candidate_k_reciprocal_index,k_reciprocal_index))> 2/3*len(candidate_k_reciprocal_index):
- k_reciprocal_expansion_index = np.append(k_reciprocal_expansion_index,candidate_k_reciprocal_index)
-
- k_reciprocal_expansion_index = np.unique(k_reciprocal_expansion_index)
- weight = np.exp(-original_dist[i,k_reciprocal_expansion_index])
- V[i,k_reciprocal_expansion_index] = weight/np.sum(weight)
- original_dist = original_dist[:query_num,]
- if k2 != 1:
- V_qe = np.zeros_like(V,dtype=np.float16)
- for i in range(all_num):
- V_qe[i,:] = np.mean(V[initial_rank[i,:k2],:],axis=0)
- V = V_qe
- del V_qe
- del initial_rank
- invIndex = []
- for i in range(gallery_num):
- invIndex.append(np.where(V[:,i] != 0)[0])
-
- jaccard_dist = np.zeros_like(original_dist,dtype = np.float16)
+parser = argparse.ArgumentParser()
+parser.add_argument("-t", "--test", help="Use test data instead of query", action='store_true')
+parser.add_argument("-cm", "--conf_mat", help="Show visual confusion matrix", action='store_true')
+parser.add_argument("-km", "--kmean", help="Perform Kmeans", action='store_true', default=0)
+parser.add_argument("-ma", "--mahalanobis", help="Perform Mahalanobis Distance metric", action='store_true', default=0)
+parser.add_argument("-e", "--euclidean", help="Standard euclidean", action='store_true', default=0)
+parser.add_argument("-r", "--rerank", help="Use k-reciprocal rernaking", action='store_true')
+parser.add_argument("-ka", "--reranka", help="Parameter 1 for Rerank", type=int, default = 20)
+parser.add_argument("-kb", "--rerankb", help="Parameter 2 for rerank", type=int, default = 6)
+parser.add_argument("-v", "--verbose", help="Use verbose output", action='store_true')
+args = parser.parse_args()
- for i in range(query_num):
- temp_min = np.zeros(shape=[1,gallery_num],dtype=np.float16)
- indNonZero = np.where(V[i,:] != 0)[0]
- indImages = []
- indImages = [invIndex[ind] for ind in indNonZero]
- for j in range(len(indNonZero)):
- temp_min[0,indImages[j]] = temp_min[0,indImages[j]]+ np.minimum(V[i,indNonZero[j]],V[indImages[j],indNonZero[j]])
- jaccard_dist[i] = 1-temp_min/(2-temp_min)
-
- final_dist = jaccard_dist*(1-lambda_value) + original_dist*lambda_value
- del original_dist
- del V
- del jaccard_dist
- final_dist = final_dist[:query_num,query_num:]
-
- return final_dist
+def verbose(*text):
+ if args.verbose:
+ print(text)
-def draw_results(args, target_test, target_pred):
- acc_sc = accuracy_score(target_test, target_pred)
- cm = confusion_matrix(target_test, target_pred)
+#prob query, gal train
+def draw_results(test_label, pred_label):
+ acc_sc = accuracy_score(test_label, pred_label)
+ cm = confusion_matrix(test_label, pred_label)
print('Accuracy: ', acc_sc)
if (args.conf_mat):
plt.matshow(cm, cmap='Blues')
@@ -123,121 +59,62 @@ def draw_results(args, target_test, target_pred):
plt.show()
return
-def test_model(train_data, test_data, target_train, target_test, args):
- classifier = KNeighborsClassifier(n_neighbors=args.neighbors, metric='euclidean')
-# else:
-# S = LA.inv(np.cov(train_data, rowvar=False))
-# print(S.shape)
-# classifier = KNeighborsClassifier(n_neighbors=args.neighbors, metric='mahalanobis', metric_params={'VI':S})
- classifier.fit(train_data, target_train)
- target_pred = classifier.predict(test_data)
- dist, nn_idx = classifier.kneighbors(test_data)
- #USE NN_IDX TO RECOVER NEIGHBORS
+def test_model(gallery_data, probe_data, gallery_label, probe_label, gallery_cam, probe_cam):
+ # metric = 'jaccard' is also valid
+ if args.mahalanobis:
+ metric = 'sqeuclidean'
+ else:
+ metric = 'euclidean'
+
+ verbose("probe shape:", probe_data.shape)
+ verbose("gallery shape:", gallery_data.shape)
+
+ if args.rerank:
+ distances = re_ranking(probe_data, gallery_data,
+ args.reranka ,args.rerankb , 0.3,
+ MemorySave = False, Minibatch = 2000)
+ else:
+ distances = cdist(probe_data, gallery_data, metric)
+
+ ranklist = np.argsort(distances, axis=1)
+
+ target_pred = np.zeros(ranklist.shape[0])
+ for probe_idx in range(probe_data.shape[0]):
+ row = ranklist[probe_idx]
+ n = 0
+ while (probe_cam[probe_idx] == gallery_cam[row[n]] and
+ probe_label[probe_idx] == gallery_label[row[n]]):
+ n += 1
+ target_pred[probe_idx] = gallery_label[row[n]]
+
return target_pred
def main():
- parser = argparse.ArgumentParser()
- parser.add_argument("-R", "--random", help="Number of eigen value to randomise", type=int)
- parser.add_argument("-n", "--neighbors", help="How many neighbors to use", type=int, default = 1)
- parser.add_argument("-c", "--principal", help="Show principal components", action='store_true')
- parser.add_argument("-s", "--seed", help="Seed to use", type=int, default=0)
- parser.add_argument("-t", "--split", help="Fractoin of data to use for testing", type=float, default=0.3)
- parser.add_argument("-2", "--grapheigen", help="Swow 2D graph of targets versus principal components",
- action='store_true')
- parser.add_argument("-cm", "--conf_mat", help="Show visual confusion matrix", action='store_true')
- parser.add_argument("-q", "--pca_r", help="Use Reduced PCA", action='store_true')
- parser.add_argument("-pr", "--prob", help="Certainty on each guess", action='store_true')
- parser.add_argument("-km", "--kmean", help="Perform Kmeans", action='store_true', default=0)
- parser.add_argument("-ma", "--mala", help="Perform Mahalanobis Distance metric", action='store_true', default=0)
- parser.add_argument("-e", "--eucl", help="Standard euclidean", action='store_true', default=0)
- parser.add_argument("-ka", "--reranka", help="Parameter 1 for Rerank", type=int, default = 20)
- parser.add_argument("-kb", "--rerankb", help="Parameter 2 for rerank", type=int, default = 6)
- args = parser.parse_args()
-
- ###PART2 INPUT DATA
mat = scipy.io.loadmat('data/cuhk03_new_protocol_config_labeled.mat')
camId = mat['camId']
filelist = mat['filelist']
- gallery_idx = mat['gallery_idx']
labels = mat['labels']
- query_idx = mat['query_idx']
- train_idx = mat['train_idx']
+ gallery_idx = mat['gallery_idx'] - 1
+ query_idx = mat['query_idx'] - 1
+ train_idx = mat['train_idx'] - 1
with open("data/feature_data.json", "r") as read_file:
feature_vectors = np.array(json.load(read_file))
+
+ gallery_idx = gallery_idx.reshape(gallery_idx.shape[0])
+ if args.test:
+ query_idx = train_idx.reshape(train_idx.shape[0])
+ else:
+ query_idx = query_idx.reshape(query_idx.shape[0])
+ camId = camId.reshape(camId.shape[0])
+
+ train_data = feature_vectors[gallery_idx]
+ test_data = feature_vectors[query_idx]
+ train_label = labels[gallery_idx]
+ test_label = labels[query_idx]
+ train_cam = camId[gallery_idx]
+ test_cam = camId[query_idx]
- query_cam_1 = 0
- for i in range(query_idx.size):
- if camId[query_idx[i]] == 1:
- query_cam_1 = query_cam_1 + 1
- query_cam_2 = query_idx.size - query_cam_1
-
- train_cam_1 = 0
- for i in range(gallery_idx.size):
- if camId[gallery_idx[i]] == 1:
- train_cam_1 = train_cam_1 + 1
- train_cam_2 = gallery_idx.size - train_cam_1
-
- train_data_1 = np.zeros(((train_cam_1),(feature_vectors.shape[1])))
- train_label_1 = np.zeros(train_cam_1)
- test_data_1 = np.zeros(((query_cam_1),(feature_vectors.shape[1])))
- test_label_1 = np.zeros(query_cam_1)
-
- train_data_2 = np.zeros(((train_cam_2),(feature_vectors.shape[1])))
- train_label_2 = np.zeros(train_cam_2)
- test_data_2 = np.zeros(((query_cam_2),(feature_vectors.shape[1])))
- test_label_2 = np.zeros(query_cam_2)
-
- i_1 = 0
- i_2 = 0
- for i in range(gallery_idx.size):
- if camId[gallery_idx[i]] == 1:
- train_data_1[i_1] = feature_vectors[gallery_idx[i]]
- i_1 = i_1 + 1
- else:
- train_data_2[i_2] = feature_vectors[gallery_idx[i]]
- i_2 = i_2 + 1
- i_1 = 0
- i_2 = 0
- for i in range(query_idx.size):
- if camId[query_idx[i]] == 1:
- test_data_1[i_1] = feature_vectors[query_idx[i]]
- i_1 = i_1 + 1
- else:
- test_data_2[i_2] = feature_vectors[query_idx[i]]
- i_2 = i_2 + 1
- i_1 = 0
- i_2 = 0
- for i in range(gallery_idx.size):
- if camId[gallery_idx[i]] == 1:
- train_label_1[i_1] = labels[gallery_idx[i]]
- i_1 = i_1 + 1
- else:
- train_label_2[i_2] = labels[gallery_idx[i]]
- i_2 = i_2 + 1
- i_1 = 0
- i_2 = 0
- for i in range(query_idx.size):
- if camId[query_idx[i]] == 1:
- test_label_1[i_1] = labels[query_idx[i]]
- i_1 = i_1 + 1
- else:
- test_label_2[i_2] = labels[query_idx[i]]
- i_2 = i_2 + 1
-
- if (args.mala):
- final_dist = re_ranking(test_data_1, train_data_2, args.reranka, args.rerankb, 0.3)
- target_pred = np.zeros(final_dist.shape[0])
- for i in range(test_label_1.size):
- target_pred[i] = train_label_2[np.argmin(final_dist[i])]
- draw_results(args, test_label_1, target_pred)
-
- final_dist2 = re_ranking(test_data_2, train_data_1, args.reranka, args.rerankb, 0.3)
- target_pred2 = np.zeros(final_dist2.shape[0])
- for i in range(test_label_2.size):
- target_pred2[i] = train_label_1[np.argmin(final_dist2[i])]
- draw_results(args, test_label_2, target_pred2)
-
- elif(args.kmean):
+ if(args.kmean):
km_labels_1 = np.arange(1,np.max(labels)+1)
km_labels_2 = np.arange(1,np.max(labels)+1)
km_train_data_1 = np.zeros(((km_labels_1.size),(feature_vectors.shape[1])))
@@ -253,8 +130,8 @@ def main():
class_vote[int(train_label_1[q])-1] = class_vote[int(train_label_1[q])-1] + 1
km_labels_1[i] = np.argmax(class_vote) + 1
- target_pred = test_model(km_train_data_1.cluster_centers_, test_data_2, km_labels_1, test_label_2, args)
- draw_results(args, test_label_2, target_pred)
+ target_pred = test_model(km_train_data_1.cluster_centers_, test_data_2, km_labels_1, test_label_2)
+ draw_results(test_label_2, target_pred)
km_idx_2 = km_train_data_2.labels_
for i in range(np.max(labels)):
@@ -264,20 +141,13 @@ def main():
class_vote[int(train_label_2[q])-1] = class_vote[int(train_label_2[q])-1] + 1
km_labels_2[i] = np.argmax(class_vote) + 1
- target_pred = test_model(km_train_data_2.cluster_centers_, test_data_1, km_labels_2, test_label_1, args)
- draw_results(args, test_label_1, target_pred)
+ target_pred = test_model(km_train_data_2.cluster_centers_, test_data_1, km_labels_2, test_label_1)
+ draw_results(test_label_1, target_pred)
- elif(args.eucl):
- target_pred = test_model(train_data_2, test_data_1, train_label_2, test_label_1, args)
- draw_results(args, test_label_1, target_pred)
- target_pred = test_model(train_data_1, test_data_2, train_label_1, test_label_2, args)
- draw_results(args, test_label_2, target_pred)
-
-
- print('N-Query from cam 1:', test_data_1.shape)
- print('N-Query from cam 2:', test_data_2.shape)
- print('Complete')
+ else:
+ target_pred = test_model(train_data, test_data, train_label, test_label, train_cam, test_cam)
+ draw_results(test_label, target_pred)
if __name__ == "__main__":
main()
- \ No newline at end of file
+
diff --git a/rerank.py b/rerank.py
new file mode 100644
index 0000000..6b20f53
--- /dev/null
+++ b/rerank.py
@@ -0,0 +1,82 @@
+from scipy.spatial.distance import cdist
+import numpy as np
+
+def re_ranking(probFea,galFea,k1,k2,lambda_value, MemorySave = False, Minibatch = 2000):
+
+ query_num = probFea.shape[0]
+ all_num = query_num + galFea.shape[0]
+ feat = np.append(probFea,galFea,axis = 0)
+ feat = feat.astype(np.float16)
+ print('computing original distance')
+ if MemorySave:
+ original_dist = np.zeros(shape = [all_num,all_num],dtype = np.float16)
+ i = 0
+ while True:
+ it = i + Minibatch
+ if it < np.shape(feat)[0]:
+ original_dist[i:it,] = np.power(cdist(feat[i:it,],feat),2).astype(np.float16)
+ else:
+ original_dist[i:,:] = np.power(cdist(feat[i:,],feat),2).astype(np.float16)
+ break
+ i = it
+ else:
+ original_dist = cdist(feat,feat).astype(np.float16)
+ original_dist = np.power(original_dist,2).astype(np.float16)
+ del feat
+ gallery_num = original_dist.shape[0]
+ original_dist = np.transpose(original_dist/np.max(original_dist,axis = 0))
+ V = np.zeros_like(original_dist).astype(np.float16)
+ initial_rank = np.argsort(original_dist).astype(np.int32)
+
+
+ print('starting re_ranking')
+ for i in range(all_num):
+ # k-reciprocal neighbors
+ forward_k_neigh_index = initial_rank[i,:k1+1]
+ backward_k_neigh_index = initial_rank[forward_k_neigh_index,:k1+1]
+ fi = np.where(backward_k_neigh_index==i)[0]
+ k_reciprocal_index = forward_k_neigh_index[fi]
+ k_reciprocal_expansion_index = k_reciprocal_index
+ for j in range(len(k_reciprocal_index)):
+ candidate = k_reciprocal_index[j]
+ candidate_forward_k_neigh_index = initial_rank[candidate,:int(np.around(k1/2))+1]
+ candidate_backward_k_neigh_index = initial_rank[candidate_forward_k_neigh_index,:int(np.around(k1/2))+1]
+ fi_candidate = np.where(candidate_backward_k_neigh_index == candidate)[0]
+ candidate_k_reciprocal_index = candidate_forward_k_neigh_index[fi_candidate]
+ if len(np.intersect1d(candidate_k_reciprocal_index,k_reciprocal_index))> 2/3*len(candidate_k_reciprocal_index):
+ k_reciprocal_expansion_index = np.append(k_reciprocal_expansion_index,candidate_k_reciprocal_index)
+
+ k_reciprocal_expansion_index = np.unique(k_reciprocal_expansion_index)
+ weight = np.exp(-original_dist[i,k_reciprocal_expansion_index])
+ V[i,k_reciprocal_expansion_index] = weight/np.sum(weight)
+ original_dist = original_dist[:query_num,]
+ if k2 != 1:
+ V_qe = np.zeros_like(V,dtype=np.float16)
+ for i in range(all_num):
+ V_qe[i,:] = np.mean(V[initial_rank[i,:k2],:],axis=0)
+ V = V_qe
+ del V_qe
+ del initial_rank
+ invIndex = []
+ for i in range(gallery_num):
+ invIndex.append(np.where(V[:,i] != 0)[0])
+
+ jaccard_dist = np.zeros_like(original_dist,dtype = np.float16)
+
+ for i in range(query_num):
+ temp_min = np.zeros(shape=[1,gallery_num],dtype=np.float16)
+ indNonZero = np.where(V[i,:] != 0)[0]
+ indImages = []
+ indImages = [invIndex[ind] for ind in indNonZero]
+ for j in range(len(indNonZero)):
+ temp_min[0,indImages[j]] = temp_min[0,indImages[j]]+ np.minimum(V[i,indNonZero[j]],V[indImages[j],indNonZero[j]])
+ jaccard_dist[i] = 1-temp_min/(2-temp_min)
+
+ final_dist = jaccard_dist*(1-lambda_value) + original_dist*lambda_value
+ del original_dist
+ del V
+ del jaccard_dist
+ final_dist = final_dist[:query_num,query_num:]
+
+ return final_dist
+