diff --git a/dataset/dji_roco/robomaster_Final Tournament/image/desktop.ini b/dataset/dji_roco/robomaster_Final Tournament/image/desktop.ini
new file mode 100644
index 0000000000000000000000000000000000000000..6e6d9b27d5f2752c3d0980b29497a3fa73efadd7
--- /dev/null
+++ b/dataset/dji_roco/robomaster_Final Tournament/image/desktop.ini
@@ -0,0 +1,3 @@
+[LocalizedFileNames]
+VW_CH3IRobotVsEvolution_BO2_2_10.jpg=@VW_CH3IRobotVsEvolution_BO2_2_10.jpg,0
+VW_CH3IRobotVsEvolution_BO2_2_9.jpg=@VW_CH3IRobotVsEvolution_BO2_2_9.jpg,0
diff --git a/dataset/dji_roco/robomaster_Final Tournament/image_annotation/desktop.ini b/dataset/dji_roco/robomaster_Final Tournament/image_annotation/desktop.ini
new file mode 100644
index 0000000000000000000000000000000000000000..209520bac2150516db3db2fe06f869b1520208c3
--- /dev/null
+++ b/dataset/dji_roco/robomaster_Final Tournament/image_annotation/desktop.ini
@@ -0,0 +1,2 @@
+[LocalizedFileNames]
+VW_CH3IRobotVsEvolution_BO2_2_9.xml=@VW_CH3IRobotVsEvolution_BO2_2_9.xml,0
diff --git a/robots-at-robots/research/armor_digit/baseline_experiments.py b/robots-at-robots/research/armor_digit/baseline_experiments.py
index 23d89d1f3e401b4e3a3e0085e33051524173a6dc..0b7c979881f725679f8d5cfb7ce62f308b6897de 100644
--- a/robots-at-robots/research/armor_digit/baseline_experiments.py
+++ b/robots-at-robots/research/armor_digit/baseline_experiments.py
@@ -10,7 +10,8 @@ from sklearn.model_selection import train_test_split
IMG_SIZE = 28
TEST_SIZE_FACTOR = 0.1
-N_CLASSES = 10
+# TODO maybe use an ENUM (here and armor_dataset_factory) because no 6 makes lost space in array of classes
+N_CLASSES = 9 # there is no 6s or 9s
# from polystar.common.image_pipeline.image_pipeline import ImagePipeline
@@ -34,9 +35,8 @@ N_CLASSES = 10
def load_digits_img_to_data():
# TODO add a model for digits_img in common/polystar/common/models
- # TODO add a digits_img in roco_dataset
# TODO create a from_dataset (see armor_dataset_factory) to read digits_img from digits_found folder
- # TODO change the way to append digit label to label_train and label_test (with the info from model and dataset)
+ # TODO maybe use an ENUM for labels (here and armor_dataset_factory) because no 6 makes lost space in classes' array
# TODO but before, we use this hardcoded path...
path = '.\\..\\..\\..\\dataset\\dji_roco\\robomaster_Final Tournament\\digits_found'
digits_found = os.listdir(path)
@@ -60,7 +60,7 @@ def load_digits_img_to_data():
def training_model(features_train, labels_train, features_test, labels_test):
# Source: https://www.sitepoint.com/keras-digit-recognition-tutorial/
- # Cleaning
+ # Formatting
features_train = features_train.reshape(features_train.shape[0], IMG_SIZE, IMG_SIZE, 1)
features_test = features_test.reshape(features_test.shape[0], IMG_SIZE, IMG_SIZE, 1)
label_train = to_categorical(labels_train, N_CLASSES)
@@ -73,12 +73,12 @@ def training_model(features_train, labels_train, features_test, labels_test):
input_shape=(IMG_SIZE, IMG_SIZE, 1)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
- # TODO maybe dont drop or less if not enough data in dataset
- # model.add(Dropout(0.25))
+ # randomly drop units to avoid overfitting
+ model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
- # TODO maybe dont drop or less if not enough data in dataset
- # model.add((Dropout(0.5)))
+ # randomly drop units to avoid overfitting
+ model.add((Dropout(0.5)))
# because we have a number of pre-decided classes
model.add(Dense(N_CLASSES, activation='softmax'))
@@ -88,7 +88,7 @@ def training_model(features_train, labels_train, features_test, labels_test):
metrics=['accuracy'])
batch_size = 128
- epochs = 10
+ epochs = 20
model.fit(features_train, label_train,
batch_size=batch_size,
@@ -98,13 +98,11 @@ def training_model(features_train, labels_train, features_test, labels_test):
score = model.evaluate(features_test, label_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
- model.save("test_model.h5")
+ model.save("test_model.h5") # TODO change resulting file name (and erase file test_model.h5)
if __name__ == "__main__":
digits_features_train, digits_labels_train, digits_features_test, digits_labels_test = load_digits_img_to_data()
- for img in digits_labels_train:
- print('train', img)
for img in digits_labels_test:
print('test', img)
- # training_model(digits_features_train, digits_labels_train, digits_features_test, digits_labels_test)
+ training_model(digits_features_train, digits_labels_train, digits_features_test, digits_labels_test)
diff --git a/robots-at-robots/research/armor_digit/test_model.h5 b/robots-at-robots/research/armor_digit/test_model.h5
new file mode 100644
index 0000000000000000000000000000000000000000..ab1a911f7af581ed58f33b67e214745ecce8b546
Binary files /dev/null and b/robots-at-robots/research/armor_digit/test_model.h5 differ
diff --git a/robots-at-robots/research/dataset/armor_dataset_factory.py b/robots-at-robots/research/dataset/armor_dataset_factory.py
index 8d85045acf09d99ce326ff2b19e07539cf3d3642..75688971c9851a5dd321374a8491e1f69154576a 100644
--- a/robots-at-robots/research/dataset/armor_dataset_factory.py
+++ b/robots-at-robots/research/dataset/armor_dataset_factory.py
@@ -1,5 +1,6 @@
from pathlib import Path
from typing import List, Iterable, Tuple
+import cv2
import matplotlib.pyplot as plt
@@ -32,13 +33,17 @@ class ArmorDatasetFactory:
def save_digits_img():
for j, (digit_img, c, n, k, p) in enumerate(ArmorDatasetFactory.from_dataset(DJIROCODataset.Final)):
# we ignore the 6s, because the format of their pictures is not accurate and we don't need them
- if n != 6:
- if j >= 50:
- break
- print(c, n, k, 'in', p)
- plt.imshow(digit_img)
- plt.savefig(str(p.parents[1]) + '\\digits_found\\digit' + str(j) + '_' + c.name + '_' + str(n) + '.png')
- plt.clf()
+ if n != 6 and j >= 5925: # TODO change j value depending where you are in saving (to save time)
+ # if j >= 50:
+ # break
+ try:
+ print(c, n, k, 'in', p)
+ plt.imshow(digit_img)
+ plt.savefig(str(p.parents[1]) + '\\digits_found\\digit' + str(j) + '_' + c.name + '_' + str(n) + '.png')
+ plt.clf()
+ except ValueError:
+ # because some digit's pictures have zero-array values
+ continue
if __name__ == "__main__":