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create_data_distribution.py
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create_data_distribution.py
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#!/usr/bin/env python
import glob
import json
import random
import numpy as np
import os
import math
leaf_map = json.loads(open("leaf-map.json", "r").read())
INPUT_FOLDER = "./raw"
OUTPUT_FOLDER = "./lmdb"
TRAIN_PERCENT = 80
def determine_leaf_group(leaf_identifier, className):
global leaf_map
try:
foo = leaf_map[leaf_identifier.lower().strip()]
if len(foo) == 1:
return foo[0]
else:
for _suggestion in foo:
if _suggestion.find(className) != -1:
return _suggestion
return str(random.randint(1,10000000000000000000000))
except:
return str(random.randint(1,10000000000000000000000))
def compute_per_class_distribution(DATASET):
classMap = {}
count = 0
for datum in DATASET:
try:
classMap[datum[1]].append(datum[0])
count += 1
except:
classMap[datum[1]] = [datum[0]]
count += 1
for _key in classMap:
classMap[_key] = len(classMap[_key])
return classMap
def distribute_buckets(BUCKETS, train_probability):
train = []
test = []
for _key in BUCKETS.keys():
bucket = BUCKETS[_key]
if random.random() <= train_probability:
train += bucket
else:
test += bucket
return train, test
for data_type in glob.glob(INPUT_FOLDER +"/*"):
data_type_name = data_type.split("/")[-1]
print data_type_name
BUCKETS = {}
all_images = glob.glob(data_type+"/*/*")
for _img in all_images:
image_name = _img.split("/")[-1]
className = _img.split("/")[-2]
#Check if the image belongs to a particular known group
image_identifier = image_name.replace("_final_masked","")
image_identifier = image_identifier.split("___")[-1]
image_identifier = image_identifier.split("copy")[0].replace(".jpg", "").replace(".JPG","").replace(".png","").replace(".PNG", "")
#print "\"",image_identifier,"\"", className
#print image_name, "======================>", determine_leaf_group(image_identifier, className)
group = determine_leaf_group(image_identifier, className)
try:
BUCKETS[group].append((_img, className))
except:
BUCKETS[group] = [(_img, className)]
train_probs = [0.2, 0.4, 0.5, 0.6, 0.8]
for train_prob in train_probs:
CANDIDATE_DISTRIBUTIONS = []
CANDIDATE_VARIANCES = []
for k in range(1000):
#print "======================="
#print "K ::",k
train, test = distribute_buckets(BUCKETS, train_prob)
train_dist = compute_per_class_distribution(train)
test_dist = compute_per_class_distribution(test)
spread_data = []
for _key in train_dist:
#print _key, train_dist[_key] * 1.0 /(train_dist[_key]+test_dist[_key])
spread_data.append(train_dist[_key] * 1.0 /(train_dist[_key]+test_dist[_key]))
CANDIDATE_DISTRIBUTIONS.append((train, test))
CANDIDATE_VARIANCES.append(np.var(spread_data))
#print "Train : ", len(train)
#print "Test : ", len(test)
train, test = CANDIDATE_DISTRIBUTIONS[np.argmax(CANDIDATE_VARIANCES)]
print len(train)
print len(test)
train_dist = compute_per_class_distribution(train)
test_dist = compute_per_class_distribution(test)
spread_data = []
for _key in train_dist:
print _key, train_dist[_key] * 1.0 /(train_dist[_key]+test_dist[_key])
spread_data.append(train_dist[_key] * 1.0 /(train_dist[_key]+test_dist[_key]))
print "Mean :: ", np.mean(spread_data)
print "Variance: ", np.var(spread_data)
target_folder_name = data_type_name + "-" + str(int(math.ceil(train_prob*100)))+"-"+str(int(math.ceil((1-train_prob)*100)))
try:
os.mkdir(OUTPUT_FOLDER+"/"+target_folder_name)
except:
pass
labels_map = {}
for _entry in train:
try:
labels_map[_entry[1]] += 1
except:
labels_map[_entry[1]] = 1
print labels_map
labels_list = sorted(labels_map.keys())
f = open(OUTPUT_FOLDER+"/"+target_folder_name+"/train.txt","w")
train_txt = ""
for _entry in train:
train_txt += os.path.abspath(_entry[0])+"\t"+str(labels_list.index(_entry[1]))+"\n"
f.write(train_txt)
f.close()
f = open(OUTPUT_FOLDER+"/"+target_folder_name+"/test.txt","w")
test_txt = ""
for _entry in test:
test_txt += os.path.abspath(_entry[0])+"\t"+str(labels_list.index(_entry[1]))+"\n"
f.write(test_txt)
f.close()
f = open(OUTPUT_FOLDER+"/"+target_folder_name+"/labels.txt","w")
f.write("\n".join(labels_list))
f.close()
#break