...

Author: CursedPrograms

scripts/deep_dream.py

@@ -0,0 +1,85 @@
+import sys
+from types import ModuleType
+
+# --- STEP 1: FIX FOR PYTHON 3.12 ---
+pkg_mock = ModuleType("pkg_resources")
+try:
+    from packaging.version import parse as parse_version
+    pkg_mock.parse_version = parse_version
+except ImportError:
+    pkg_mock.parse_version = lambda v: [int(x) for x in v.split('.') if x.isdigit()]
+sys.modules["pkg_resources"] = pkg_mock
+
+# --- STEP 2: IMPORTS ---
+import os
+import numpy as np
+import tensorflow as tf
+from PIL import Image
+
+# --- STEP 3: PREPARE MODEL ---
+# Using InceptionV3 - the classic "Dream" model
+base_model = tf.keras.applications.InceptionV3(include_top=False, weights='imagenet')
+
+# Maximize the activations of these specific layers
+# Mixed3/4/5 are the "trippy" layers with eyes and patterns
+names = ['mixed3', 'mixed5']
+layers = [base_model.get_layer(name).output for name in names]
+dream_model = tf.keras.Model(inputs=base_model.input, outputs=layers)
+
+def deprocess(img):
+    img = 255 * (img + 1.0) / 2.0
+    return tf.cast(img, tf.uint8)
+
+def calc_loss(img, model):
+    img_batch = tf.expand_dims(img, axis=0)
+    layer_activations = model(img_batch)
+    if len(layer_activations) == 1:
+        layer_activations = [layer_activations]
+
+    losses = []
+    for act in layer_activations:
+        loss = tf.math.reduce_mean(act)
+        losses.append(loss)
+    return tf.reduce_sum(losses)
+
+@tf.function
+def deepdream_step(img, model, step_size):
+    with tf.GradientTape() as tape:
+        tape.watch(img)
+        loss = calc_loss(img, model)
+    gradients = tape.gradient(loss, img)
+    gradients /= tf.math.reduce_std(gradients) + 1e-8
+    img = img + gradients * step_size
+    img = tf.clip_by_value(img, -1, 1)
+    return loss, img
+
+# --- STEP 4: MAIN PROCESS ---
+def run_dream(input_path, output_path, steps=100, step_size=0.01):
+    # Load Image
+    if not os.path.exists(input_path):
+        print(f"Error: {input_path} not found!")
+        return
+
+    img = Image.open(input_path).convert('RGB')
+    img = img.resize((500, 500)) # Resizing to save your RAM
+    img = np.array(img)
+    img = tf.keras.applications.inception_v3.preprocess_input(img)
+    img = tf.convert_to_tensor(img)
+
+    print(f"Dreaming on {input_path}...")
+    for step in range(steps):
+        loss, img = deepdream_step(img, dream_model, step_size)
+        if step % 20 == 0:
+            print(f"Step {step}, Loss {loss.numpy()}")
+
+    # Save Output
+    result = deprocess(img)
+    final_img = Image.fromarray(result.numpy())
+
+    # Ensure output dir exists
+    os.makedirs(os.path.dirname(output_path), exist_ok=True)
+    final_img.save(output_path)
+    print(f"Success! Dream saved to {output_path}")
+
+if __name__ == "__main__":
+    run_dream('input/input.jpg', 'output/output.jpg')