Create utility.py

utility.py

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+import requests
+import numpy as np
+import tensorflow as tf 
+import tensorflow_hub as hub
+
+_UINT8_MAX_F = float(np.iinfo(np.uint8).max)
+
+def load_image(img_url: str):
+  """Returns an image with shape [height, width, num_channels], with pixels in [0..1] range, and type np.float32."""
+
+  if (img_url.startswith("https")):
+    response = requests.get(img_url)
+    image_data = response.content
+  else:
+    image_data = tf.io.read_file(img_url)
+
+  image = tf.io.decode_image(image_data, channels=3)
+  image_numpy = tf.cast(image, dtype=tf.float32).numpy()
+  return image_numpy / _UINT8_MAX_F
+
+
+
+model = hub.load('https://www.kaggle.com/models/google/film/TensorFlow2/film/1')
+
+def interpolate_batch(img1, img2, batch_size=30):
+    if model is None:
+        print("Model not loaded. Exiting interpolation.")
+        return None
+
+    # Generate time steps from 0 to 1, rounded to two decimal places
+    time_values = np.linspace(0.0, 1.0, batch_size, dtype=np.float32)
+    time_values = np.round(time_values, 2)  # Round time values to two decimal places
+    time_values = np.expand_dims(time_values, axis=0)  # Add batch dimension
+
+    image1 = load_image(img1)
+    image2 = load_image(img2)
+    
+    if image1 is None or image2 is None:
+        print("One or both images failed to load. Exiting interpolation.")
+        return None
+
+    # Create batch input for model
+    input_data = {
+        'time': np.repeat(time_values, image1.shape[0], axis=0),  # Expand time across batch
+        'x0': np.repeat(np.expand_dims(image1, axis=0), batch_size, axis=0),  # Repeat image1 for each batch
+        'x1': np.repeat(np.expand_dims(image2, axis=0), batch_size, axis=0)   # Repeat image2 for each batch
+    }
+
+    try:
+        mid_frames = model(input_data)
+        frames = mid_frames['image'].numpy()  # Get interpolated frames
+        return frames
+    except Exception as e:
+        print(f"Error during interpolation: {e}")
+        return None
+
+
+def interpolate_single(img1, img2):
+    """Interpolate a single frame at the midpoint between two images (time=0.5)."""
+
+    if model is None:
+        print("Model not loaded. Exiting interpolation.")
+        return None
+
+    # Midpoint time value with batch size of 1
+    time_value = np.array([[0.5]], dtype=np.float32)  # shape [1, 1]
+
+    # Load and normalize images
+    image1 = load_image(img1)
+    image2 = load_image(img2)
+
+    # Ensure the images have the same height and width if needed
+    target_height, target_width = image1.shape[0], image1.shape[1]
+    image1_resized = tf.image.resize(image1, [target_height, target_width]).numpy()
+    image2_resized = tf.image.resize(image2, [target_height, target_width]).numpy()
+
+    # Expand dimensions to add batch dimension
+    input_data = {
+        'time': time_value,  # shape [1, 1]
+        'x0': np.expand_dims(image1_resized, axis=0),  # shape [1, H, W, 3]
+        'x1': np.expand_dims(image2_resized, axis=0),  # shape [1, H, W, 3]
+    }
+
+    try:
+        mid_frame = model(input_data)
+        frame = mid_frame['image'][0].numpy()  # Extract the interpolated frame
+        return frame
+    except Exception as e:
+        print(f"Error during interpolation: {e}")
+        return None