updates for products

coco_cvat_gen.py

@@ -11,17 +11,14 @@ count = 0
 
 
 
-print("loading coco annotations...")
-coco = json.load(open('./coco/annotations/instances_default.json'))
-print("done")
 
-def findAnnotationName(annotationId):
+def findAnnotationName(annotationId, annotations):
-    for c in coco['categories']:
+    for c in annotations['categories']:
         if c['id'] == annotationId:
             return c['name']
 
-def findAnnotationToId(ident):
+def findAnnotationToId(ident, annotations):
-    for annotation in coco['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):
+def parseImage(coImg, annotations, subset):
-    global no_label, small, passed, coco
+    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']:
+    print("loading" + subset + "annotations...")
-        parseImage(coImg)
+    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')
+

coco_gen.py

@@ -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

siamese.py

@@ -202,10 +202,13 @@ class SiameseNetwork:
 
             img_rows = self.input_shape[0]
             img_cols = self.input_shape[1]
+
+            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
-            # img1 = x[element_index_1]
+            else: 
-            # img2 = x[element_index_2]
+                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]
+
+            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])

train_coco.py

@@ -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
+try:
-# for epoch in range(1, epochs):
+    print("loading weights for model")
-#     batch_x, batch_y = get_batch(x_train, y_train, x_test, y_test, cat_train, train_size, batch_size)
+    siamese_network.load_weights(siamese_checkpoint_path+model_path)
-#     loss = siamese_network.train_on_batch(batch_x, batch_y)
+except Exception as e:
-#     print('Epoch:', epoch, ', Loss:', loss)
+    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])

train_lambda_coco.py

@@ -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