upload image functionality testing

app.py

@@ -21,18 +21,19 @@ from bayes.models import *
 from image_posterior import create_gif	
 
 
-def get_image_data(image_name):
+def get_image_data(inp_image):
     """Gets the image data and model."""
-    image = get_dataset_by_name(image_name, get_label=False)
+    image = get_dataset_by_name(inp_image, get_label=False)
+    print("image returned\n", image)
     model_and_data = process_imagenet_get_model(image)
-
+    print("model returned\n", model_and_data)
     return image, model_and_data
 
 
 def segmentation_generation(image_name, c_width, n_top, n_gif_imgs):
     print("Inputs Received:", image_name, c_width, n_top, n_gif_imgs)
 
-    print("imagename", image_name.filename)
+    get_image_data(image_name)
 
     return "yeehaw"
 

bayes/data_routines.py

@@ -113,45 +113,49 @@ def get_PIL_transf():
     ])    
     return transf  
 
-def load_image(path):
+# def load_image(path):
+#     """Loads an image by path."""
+#     with open(os.path.abspath(path), 'rb') as f:
+#         with Image.open(f) as img:
+#             return img.convert('RGB') 
+
+def load_image(pil_image):
     """Loads an image by path."""
-    with open(os.path.abspath(path), 'rb') as f:
-        with Image.open(f) as img:
-            return img.convert('RGB') 
+    with Image.open(pil_image) as img:
+        return img.convert('RGB') 
 
-def get_imagenet(name, get_label=True):
+def get_imagenet(pil_image, get_label=True):
     """Gets the imagenet data.
 
     Arguments:
         name: The name of the imagenet dataset
     """
-    images_paths = []
+    # images_paths = []
 
     # Store all the paths of the images
-    data_dir = os.path.join("./data", name)
-    for (dirpath, dirnames, filenames) in os.walk(data_dir):
-        for fn in filenames:
-            if fn != ".DS_Store":
-                images_paths.append(os.path.join(dirpath, fn))
+    # data_dir = os.path.join("./data", name)
+    # for (dirpath, dirnames, filenames) in os.walk(data_dir):
+    #     for fn in filenames:
+    #         if fn != ".DS_Store":
+    #             images_paths.append(os.path.join(dirpath, fn))
     
     # Load & do transforms for the images
     pill_transf = get_PIL_transf()
     images, segs = [], []
-    for img_path in images_paths:
-        img = load_image(img_path)
-        PIL_transformed_image = np.array(pill_transf(img))
-        segments = slic(PIL_transformed_image, n_segments=NSEGMENTS, compactness=100, sigma=1)
+    img = load_image(pil_image)
+    PIL_transformed_image = np.array(pill_transf(img))
+    segments = slic(PIL_transformed_image, n_segments=NSEGMENTS, compactness=100, sigma=1)
 
-        images.append(PIL_transformed_image)
-        segs.append(segments)
+    images.append(PIL_transformed_image)
+    segs.append(segments)
 
     images = np.array(images)
 
-    if get_label:
-        assert name in IMAGENET_LABELS, "Get label set to True but name not in known imagenet labels"
-        y = np.ones(images.shape[0]) * IMAGENET_LABELS[name]
-    else:
-        y = np.ones(images.shape[0]) * -1
+    # if get_label:
+    #     assert name in IMAGENET_LABELS, "Get label set to True but name not in known imagenet labels"
+    #     y = np.ones(images.shape[0]) * IMAGENET_LABELS[name]
+    # else:
+    y = np.ones(images.shape[0]) * -1
 
     segs = np.array(segs)
 
@@ -203,16 +207,17 @@ def get_mnist(num):
 
     return output
 
-def get_dataset_by_name(name, get_label=True):
-    if name == "compas":
-        d = get_and_preprocess_compas_data()
-    elif name == "german":
-        d = get_and_preprocess_german()
-    elif "mnist" in name:
-        d = get_mnist(int(name[-1]))
-    elif "imagenet" in name:
-        d = get_imagenet(name[9:], get_label=get_label)
-    else:
-        raise NameError("Unkown dataset %s", name)
-    d['name'] = name
+def get_dataset_by_name(inp_image, get_label=True):
+    d = get_imagenet(inp_image, get_label=get_lable)
+    # if name == "compas":
+    #     d = get_and_preprocess_compas_data()
+    # elif name == "german":
+    #     d = get_and_preprocess_german()
+    # elif "mnist" in name:
+    #     d = get_mnist(int(name[-1]))
+    # elif "imagenet" in name:
+    #     d = get_imagenet(name[9:], get_label=get_label)
+    # else:
+    #     raise NameError("Unkown dataset %s", name)
+    # d['name'] = name
     return d