raphael/siamese
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

updates for products

Browse Source
This commit is contained in:
Raphael Maenle 2021-09-20 08:06:52 +02:00
parent 3ef8c74f2e
commit 0bf090c2a8
5 changed files with 59 additions and 44 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

44
coco_cvat_gen.py
View File

@ -11,17 +11,14 @@ count = 0
print("loading coco annotations...")
coco = json.load(open('./coco/annotations/instances_default.json'))
print("done")
def findAnnotationName(annotationId):
for c in coco['categories']:
def findAnnotationName(annotationId, annotations):
for c in annotations['categories']:
if c['id'] == annotationId:
return c['name']
def findAnnotationToId(ident):
for annotation in coco['annotations']:
def findAnnotationToId(ident, annotations):
for annotation in annotations['annotations']:
img_an = annotation['image_id']
if img_an == ident:
return annotation
@ -34,14 +31,14 @@ def show(pil, pause=0.2):
plt.close()
def parseImage(coImg):
global no_label, small, passed, coco
def parseImage(coImg, annotations, subset):
global no_label, small, passed
# open image file
path = "coco/roto_frank/images/" + coImg['file_name'].split('/')[4]
path = "coco/"+subset+"/images/" + coImg['file_name'].split('/')[4]
img = Image.open(path)
an = findAnnotationToId(coImg['id'])
an = findAnnotationToId(coImg['id'], annotations)
if an == None:
no_label += 1
return
@ -53,15 +50,20 @@ def parseImage(coImg):
small += 1
return
imagePath = f"classified_roto/{findAnnotationName(an['category_id'])}/{an['id']}.png"
imagePath = f"classified/classified_{subset}/{findAnnotationName(an['category_id'], annotations)}/{an['id']}.png"
os.makedirs(os.path.dirname(imagePath), exist_ok=True)
crop.save(imagePath)
passed += 1
if __name__ == "__main__":
def parseSubset(subset):
global count
for coImg in coco['images']:
parseImage(coImg)
print("loading" + subset + "annotations...")
annotations = json.load(open('./coco/'+subset+'/annotations/instances_default.json'))
print("done")
for coImg in annotations['images']:
parseImage(coImg, annotations,subset)
count += 1
if count % 100 == 0:
print("status:")
@ -70,3 +72,15 @@ if __name__ == "__main__":
print(f"passed: {passed}")
print("-----")
if __name__ == "__main__":
parseSubset('donaulager')
parseSubset('instantina')
parseSubset('lkw_walter_2019')
parseSubset('lkw_walter_2020')
parseSubset('tkl')
parseSubset('vw_bratislava')
parseSubset('vw_portugal')
parseSubset('watt')
parseSubset('wls')

9
coco_gen.py
View File

@ -12,7 +12,7 @@ count = 0
print("loading coco annotations...")
coco = json.load(open('./coco/annotations/instances_val2014.json'))
coco = json.load(open('./coco/annotations/instances_default.json'))
print("done")
def findAnnotationName(annotationId):
@ -38,7 +38,7 @@ def parseImage(coImg):
global no_label, small, passed, coco
# open image file
path = "coco/val2014/" + coImg['file_name']
path = "coco/roto_frank/images/" + coImg['file_name'].split('/')[4]
img = Image.open(path)
an = findAnnotationToId(coImg['id'])
@ -46,9 +46,6 @@ def parseImage(coImg):
no_label += 1
return
path = "coco/val2014/" + coImg['file_name']
img = Image.open(path)
c = an['bbox']
crop = img.crop((c[0], c[1], c[0]+c[2], c[1]+c[3]))
@ -56,7 +53,7 @@ def parseImage(coImg):
small += 1
return
imagePath = f"classified/{findAnnotationName(an['category_id'])}/{an['id']}.png"
imagePath = f"classified_roto/{findAnnotationName(an['category_id'])}/{an['id']}.png"
os.makedirs(os.path.dirname(imagePath), exist_ok=True)
crop.save(imagePath)
passed += 1

20
siamese.py
View File

@ -202,10 +202,13 @@ class SiameseNetwork:
img_rows = self.input_shape[0]
img_cols = self.input_shape[1]
img1 = np.asarray(Image.open(x[element_index_1]).convert('RGB').resize((img_rows, img_cols)))/255.0
img2 = np.asarray(Image.open(x[element_index_2]).convert('RGB').resize((img_rows, img_cols)))/255.0
# img1 = x[element_index_1]
# img2 = x[element_index_2]
if type(x[element_index_1]) == str:
img1 = np.asarray(Image.open(x[element_index_1]).convert('RGB').resize((img_rows, img_cols)))/255.0
img2 = np.asarray(Image.open(x[element_index_2]).convert('RGB').resize((img_rows, img_cols)))/255.0
else:
img1 = x[element_index_1]
img2 = x[element_index_2]
positive_pairs.append([img1,img2])
positive_labels.append([1.0])
return positive_pairs, positive_labels
@ -241,8 +244,13 @@ class SiameseNetwork:
img_rows = self.input_shape[0]
img_cols = self.input_shape[1]
img1 = np.asarray(Image.open(x[element_index_1]).convert('RGB').resize((img_rows, img_cols)))/255.0
img2 = np.asarray(Image.open(x[element_index_2]).convert('RGB').resize((img_rows, img_cols)))/255.0
if type(x[element_index_1]) == str:
img1 = np.asarray(Image.open(x[element_index_1]).convert('RGB').resize((img_rows, img_cols)))/255.0
img2 = np.asarray(Image.open(x[element_index_2]).convert('RGB').resize((img_rows, img_cols)))/255.0
else:
img1 = x[element_index_1]
img2 = x[element_index_2]
negative_pairs.append([img1,img2])
negative_labels.append([0.0])

28
train_coco.py
View File

@ -21,9 +21,9 @@ num_classes = 131
img_rows, img_cols = 100, 100
def createTrainingData():
base_dir = './classified/'
base_dir = './data/COCO/products/'
train_test_split = 0.7
no_of_files_in_each_class = 10
no_of_files_in_each_class = 20
#Read all the folders in the directory
folder_list = os.listdir(base_dir)
@ -39,6 +39,8 @@ def createTrainingData():
#Using just 5 images per category
for folder_name in folder_list:
files_list = os.listdir(os.path.join(base_dir, folder_name))
if len(files_list) < no_of_files_in_each_class:
continue
temp=[]
for file_name in files_list[:no_of_files_in_each_class]:
temp.append(len(x))
@ -220,18 +222,19 @@ head_model = create_own_head_model(base_model.output_shape)
siamese_network = SiameseNetwork(base_model, head_model)
siamese_network.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
siamese_checkpoint_path = "./siamese_checkpoint"
siamese_checkpoint_path = "../siamese100_products"
model_path = "/variables/variables"
siamese_callbacks = [
# EarlyStopping(monitor='val_accuracy', patience=10, verbose=0),
ModelCheckpoint(siamese_checkpoint_path, monitor='val_accuracy', save_best_only=True, verbose=0)
]
# batch_size = 64
# for epoch in range(1, epochs):
# batch_x, batch_y = get_batch(x_train, y_train, x_test, y_test, cat_train, train_size, batch_size)
# loss = siamese_network.train_on_batch(batch_x, batch_y)
# print('Epoch:', epoch, ', Loss:', loss)
try:
print("loading weights for model")
siamese_network.load_weights(siamese_checkpoint_path+model_path)
except Exception as e:
print(e)
siamese_network.fit(x_train, y_train,
validation_data=(x_test, y_test),
@ -239,13 +242,6 @@ siamese_network.fit(x_train, y_train,
epochs=epochs,
callbacks=siamese_callbacks)
# try:
# siamese_network = keras.models.load_model(siamese_checkpoint_path)
# except Exception as e:
# print(e)
# print("!!!!!!")
# siamese_network.load_weights(siamese_checkpoint_path)
score = siamese_network.evaluate(x_test, y_test, batch_size=60, verbose=0)
print('Test loss:', score[0])

2
train_lambda_coco.py
View File

@ -28,7 +28,7 @@ num_classes = 131
img_rows, img_cols = 100, 100
def createTrainingData():
base_dir = './data/combined/'
base_dir = './COCO/products/'
train_test_split = 0.7
no_of_files_in_each_class = 200