ready to merge

data_handler.py

@@ -1,5 +1,4 @@
-import cv2
-import sys, os
+import cv2, sys, os, shutil, csv
 from random import randint, sample
 import numpy as np
 import pdb
@@ -53,14 +52,24 @@ def __init__(self, location, file = None, flag = False, height = 224, width = 22
             self.getGround()
 
     def OSWalk(self):
-        images = [os.path.join(root, name) for root, dirs, files in os.walk(self.location)
+        images = [os.path.join(root, name) for root, dirs, files in os.walk(self.location + 'Train/')
              for name in files if name.endswith((".png", ".jpg", ".jpeg", ".gif", ".tiff"))]
         self.imageLocs = images
 
+
         # for i in range(len(images)):
         #    a,b = images[i].split('\\')
         #    self.imageLocs[i] = a + '/' + b
 
+        if self.flag==False:
+            for i in range(len(images)):
+                a,b = images[i].split('/')
+                self.imageLocs[i] = a + '/' + b
+        else:
+            for i in range(len(images)):
+                self.imageLocs[i] = images[i]
+
+
         # array to store which image has been used
         self.numImages = len(self.imageLocs)
         self.remImages = np.ones((self.numImages), dtype = bool)
@@ -110,16 +119,18 @@ def process(self, num, indices):
         print('Generating Samples .... Note: no img normalization ')
 
         self.sampleImages = np.zeros((num*self.num_crops , self.height, self.width, self.depth), dtype=np.uint8)
-
         idx = 0
         imgs = np.zeros((num,  256, 455, 3))
-        names = []
+        names = ['' for i in range(num)]
         for i in range(num):
             # read image
             img = cv2.resize(cv2.imread(indices[i], 1).astype(float), (455,256), interpolation = cv2.INTER_CUBIC)
-            imgs[i] = img
+
             name = self.getName(indices[i])
-            names.append(name)
+            if (name in self.img2labels ):
+                names[i] = name
+                imgs[i,:,:,:] = img
+
         means = np.mean(imgs, axis=0)
         imgs = imgs - means
 
@@ -146,10 +157,15 @@ def process(self, num, indices):
                 y = randint(0,230)
 
                 self.sampleImages[j, :, :, :] = img[x:x + self.height, y:y + self.width, :].copy()
-                self.idx2img[j] = names[i]
+                try:
+                    self.idx2img[j] = names[i]
+                except IndexError:
+                    pdb.set_trace()
             idx += self.num_crops
 
-
+        for i in range(self.numImages):
+            if self.remImages[i] == False:
+                self.store(self.imageLocs[i])
 
     def fetch(self, num):
         samples = np.zeros((num, self.height, self.width, self.depth), dtype=np.float)
@@ -233,6 +249,29 @@ def getName(self, loc):
             return int(name)
 
 
+    def store(self, image):
+        name = self.getName(image)
+        location = self.location + 'usedImages/'
+        # copy image to folder
+        if  os.path.exists(location):
+            shutil.rmtree(location)
+        if not  os.path.exists(location):
+            os.makedirs(location)
+        shutil.copy(image, location +  str(name) + '.tiff')
+
+        # write image with labels to folder
+        file = location + 'trainingSet.csv'
+        csv = open(file, "a")
+        x = str(self.img2labels[name][0])
+        y = str(self.img2labels[name][1])
+        z = str(self.img2labels[name][2])
+        r = str(self.img2labels[name][3])
+        p = str(self.img2labels[name][4])
+        h = str(self.img2labels[name][5])
+
+        row = str(name) + ',' + x + ',' + y + ',' + z + ',' + r + ',' + p + ',' + h + '\n'
+        csv.write(row)
+
     def reset(self):
         self.remImages = np.ones((self.numImages), dtype = bool)
 

test.py

@@ -16,30 +16,25 @@
 # location = "./2013-01-10/"
 # file = 'groundtruth_2013-01-10.csv'
 
-location = "/media/eecs568/My Passport/NCLT/2012-03-17/2012-03-17/"
+location = "/home/eecs568/eecs568/Mobile-Robotics/nclt/"
 file = 'groundtruth_2012-03-17.csv'
 
 dh = DH.Process(location, file, True)
 
 # pick number of images to pick samples from
-numImages = 100
+numImages = 1000
 dh.generateData(numImages)
 dh.remimages()
 
 dh.numsamples()
-flag, images, labels = dh.fetch(2)
-print(labels)
+flag, images, labels = dh.fetch(6)
 print(images.shape, len(labels), flag)
 dh.remsamples()
 
-
 # Trick to pick samples from selected images
-numSamples = 60
+numSamples = 10
 flag = True
 while flag==True:
     flag, images, labels = dh.fetch(numSamples)
-    cv2.imshow('image', images[0,:,:,:])
-    cv2.waitKey(0)
-    pdb.set_trace()
     dh.remsamples()
 

train.py

@@ -1,5 +1,5 @@
 import sys, os
-sys.path.insert(0, '/home/eecs568/miniconda3/envs/tensorflow/lib/python3.5/site-packages')
+#sys.path.insert(0, '/home/eecs568/miniconda3/envs/tensorflow/lib/python3.5/site-packages')
 import data_handler
 from datetime import datetime
 import numpy as np
@@ -60,7 +60,7 @@ def __init__(self,path_to_weight, path_to_data, beta, use_quaternion=True, resum
             print("Model initialized")
 
     def init_data_handler(self,path_to_data):
-        self.data_handler = data_handler.Process(path_to_data)
+        self.data_handler = data_handler.Process(path_to_data, 'dataset_train.txt', False)
         #self.data_handler = gen_data.get_data()
 
     def load_weight(self,path_to_weight):
@@ -154,8 +154,9 @@ def test(self, img, num_random_crops=20):
     def train(self, batch_size, epochs):
 
         total_loss = 0
-
-        total_batch = int(self.data_handler.numimages() * self.data_handler.num_crops / batch_size) #100       
+        total_batch = int(self.data_handler.numimages() * self.data_handler.num_crops * 1.0 / batch_size) #100
+        if total_batch==0:
+            pdb.set_trace()
         #print("[trainer] Start Training, size of dataset is " +str(self.data_handler.numimages() * self.data_handler.num_crops ))
         #pdb.set_trace()
         for epoch in range(epochs):
@@ -200,5 +201,5 @@ def train(self, batch_size, epochs):
         argv[3] = 100
         argv[4] = bool(int(False))
     os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-    train_thread = trainer(argv[1], argv[2], int(argv[3]), bool(int(argv[4])))
+    train_thread = trainer(argv[1], argv[2], int(argv[3]), use_quaternion=True, resume_training=False )
     train_thread.train(32, 600)