Merge pull request #39 from CogitoNTNU/flask-implementation

Flask implementation

app.tsx

@@ -0,0 +1,10 @@
+// Example function for sending data to the Flask backend
+async function runTrain() {
+    const response = await fetch('http://localhost:5000/train', {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify({ n_generations: 100, neat_name: 'experiment1' })
+    });
+    const result = await response.json();
+    console.log(result.output); // Process the output as needed
+}

gui_with_pygame.py

@@ -45,8 +45,8 @@ def get_genomes(self):
 class Settings():
     def __init__(self):
         self.fps = 60
-        self.default_x_size = 2560
-        self.default_y_size = 1600
+        self.default_x_size = 1080
+        self.default_y_size = 720
         self.fullscreen = False
         self.fs_start_time = 0
         self.fs_is_pressed = False
@@ -125,7 +125,7 @@ def __init__(self, x, y, width, height, items, font, text_color, bg_color, selec
         self.scroll_offset = 0  # This tracks where we are in the list
         self.padding = padding  # Distance between items
         # Create a base list of items without positioning them
-
+        
         self.list_items = [
             SelectableListItem(
                 x, y, width, (height - (visible_count - 1) * padding) // visible_count,  # Adjust height to account for padding
@@ -400,7 +400,7 @@ def __init__(self):
                            Button(460, 170, 200, 50, "Start Training", st.normal_font, st.text_color, st.button_color, st.hover_color, st.pressed_color, self.start_training_process),
                            Button(460, 300, 200, 50, "Back to Menu", st.normal_font, st.text_color, st.button_color, st.hover_color, st.pressed_color, self.main_menu_scene)]
         try:
-            self.fitness_graph = ImageSprite("data/latest/fitness/fitness_plot.png", (700, 100))
+            self.fitness_graph = ImageSprite("data/latest/latest/fitness/fitness_plot.png", (700, 100))
         except:
             print("ERROR: Could not find image path")
             self.fitness_graph = ImageSprite("data/genome_frames/genome_0.png", (700, 100))
@@ -445,7 +445,6 @@ def __init__(self):
         # Use the loaded genome objects (printing as an example)
         for genome in loaded_genomes:
             self.genomes.append(genome)
-            print(genome)
 
         ## Genome viewer
         self.genome_viewer = GenomeViewer(self.genomes, st.normal_font, st.text_color, st.input_field_bg, st.input_field_active_bg)
@@ -658,7 +657,6 @@ def tick(self):
         self.update_screen()
         self.clock.tick(st.fps)
 
-
 st = Settings()
 
 def main():

requirements.txt

@@ -47,3 +47,6 @@ tqdm==4.66.5
 triton==3.0.0
 typing_extensions==4.12.2
 zipp==3.20.2
+flask-socketio 
+eventlet
+watchdog

src/environments/debug_env.py

@@ -1,16 +1,17 @@
+import numpy as np
+import warnings
+import time
+import gym_super_mario_bros
+from gym_super_mario_bros.actions import SIMPLE_MOVEMENT
+from typing import Tuple
 from src.genetics.genome import Genome
 from src.genetics.traverse import Traverse
 from src.environments.fitness_function import Fitness
 from src.environments.mario_env import MarioJoypadSpace
 from src.visualization.visualize_genome import visualize_genome
 from src.utils.utils import save_state_as_png
 from src.utils.utils import insert_input
-import gym_super_mario_bros
-from gym_super_mario_bros.actions import SIMPLE_MOVEMENT
-from typing import Tuple
-import numpy as np
-import warnings
-import time
+
 
 def env_debug_init() -> Tuple[MarioJoypadSpace, np.ndarray]:
     "Initialize the super-mario environment in human_mode"
@@ -24,7 +25,8 @@ def env_debug_init() -> Tuple[MarioJoypadSpace, np.ndarray]:
     state = env.reset() # Good practice to reset the env before using it.
     return env, state
 
-def run_game_debug(env: MarioJoypadSpace, initial_state: np.ndarray, genome: Genome, neat_name: str, visualize: bool = True) -> float:
+
+def run_game_debug(env: MarioJoypadSpace, initial_state: np.ndarray, genome: Genome, neat_name: str, visualize: bool = True, frame_queue=None) -> float:
 
     forward = Traverse(genome)
     fitness = Fitness()
@@ -37,11 +39,11 @@ def run_game_debug(env: MarioJoypadSpace, initial_state: np.ndarray, genome: Gen
         action = forward.traverse()
         time.sleep(0.01)
         sr = env.step(action) # State, Reward, Done, Info
-        env.render()
+
         # timeout = 600 + sr.info["x_pos"]
-        if visualize and i % 10000 == 0:
+        if visualize and i % 1 == 0:
             save_state_as_png(i, sr.state, neat_name)
-            visualize_genome(genome, neat_name, i)
+            visualize_genome(genome, neat_name, 0)
 
         fitness.calculate_fitness(sr.info, action)
 
@@ -56,5 +58,6 @@ def run_game_debug(env: MarioJoypadSpace, initial_state: np.ndarray, genome: Gen
         if sr.info["life"] == 1 or stagnation_counter > 150:
             env.close()
             return fitness.get_fitness()
+        env.render()
         i += 1
         insert_input(genome, sr.state)

src/environments/mario_env.py

@@ -8,6 +8,7 @@
 import numpy as np
 import cv2
 from skimage.transform import resize
+from src.utils.config import Config
 
 class StepResult(NamedTuple):
     """A namedtuple-like class representing the result of an environment step.
@@ -43,6 +44,7 @@ class MarioJoypadSpace(JoypadSpace):
 
     def __init__(self, env, actions):
         super().__init__(env, actions)
+        self.config = Config()
         # Potentially add extra variables here.
 
     def reset(self) -> np.ndarray:
@@ -69,7 +71,7 @@ def interpret_state(self, state: np.ndarray) -> np.ndarray:
         state = np.dot(state[..., :3], [0.2989, 0.5870, 0.1140]) # Convert to grayscale
         state = state.astype(np.uint8) # Ensure valid grayscale value (can't be float)
 
-        state = resize(state, (10, 20), anti_aliasing=False, preserve_range=True).astype(np.uint8) # Reduce pixel count.
+        state = resize(state, (self.config.input_shape[0], self.config.input_shape[1]), anti_aliasing=True, preserve_range=True).astype(np.uint8) # Reduce pixel count.
 
         state = np.array(state)
         grayscale_pixel_values = state / MAX_COLOR

src/utils/config.py

@@ -7,8 +7,8 @@ class Config:
     c2: float = 1.5
     c3: float = 0.4
     genomic_distance_threshold: float = 2.69
-    population_size: int = 56 * 10 # 56 cores on IDUN
-    generations: int = int(1e6) # A bunch of iterations 
+    population_size: int = 1 # 56 cores on IDUN
+    generations: int = 2 # A bunch of iterations 
 
     connection_weight_mutation_chance: float = 0.8
 
@@ -24,8 +24,8 @@ class Config:
     # Connections should be added way more often than nodes
 
     num_output_nodes: int = 7
-    num_input_nodes: int = 200
-    input_shape: Tuple[int, int] = (10, 20)
+    num_input_nodes: int = 800
+    input_shape: Tuple[int, int] = (20, 40)
 
     # Activation function
     # Paper: 1/(1+exp(-0.49*x))

src/utils/utils.py

@@ -45,9 +45,10 @@ def insert_input(genome:Genome, state: np.ndarray) -> None:
     start_idx_input_node = config.num_output_nodes
     num_input_nodes = config.num_input_nodes
     num_columns = config.input_shape[-1]
-
+     
     for i, node in enumerate(genome.nodes[start_idx_input_node:start_idx_input_node+num_input_nodes]): # get all input nodes
         node.value = state[i//num_columns][i % num_columns]
+        # print(f"node value: {node.value} node id: {node.id}")
 
 def save_fitness(best: list, avg: list, min: list, name: str):
     os.makedirs(f'data/{name}/fitness', exist_ok=True)

src/visualization/visualize_genome.py

@@ -8,7 +8,7 @@
 from typing import List, Dict, Tuple
 from src.visualization.colors_visualization import get_weight_color, get_node_colz
 from src.visualization.visualization_position_utils import get_position_dict
-from src.visualization.viz_config import GRAPH_XMIN, GRAPH_XMAX, GRAPH_YMIN, GRAPH_YMAX
+from src.visualization.viz_config import GRAPH_XMIN, GRAPH_XMAX, GRAPH_YMIN, GRAPH_YMAX, NODE_SIZE, FONT_SIZE
 
 def get_node_in_layers(genome: Genome) -> List[List[int]]:
     """
@@ -74,8 +74,8 @@ def visualize_genome(genome: Genome, neat_name: str, frame_number: int):
     layers = get_node_in_layers(genome)
     pos_dict = get_position_dict(layers)
 
-    nx.draw(G, pos_dict, with_labels=True, edgelist=edges, edge_color=edge_colz, node_size=500,
-            font_size=8, font_color='y', font_weight='bold',
+    nx.draw(G, pos_dict, with_labels=True, edgelist=edges, edge_color=edge_colz, node_size=NODE_SIZE,
+            font_size=FONT_SIZE, font_color='y', font_weight='bold',
             node_color=node_colz, ax=ax)
 
     fig.set_facecolor('#d4e6f1') # Background color of network popup.

src/visualization/viz_config.py

@@ -3,12 +3,15 @@
 GRAPH_YMIN = 0
 GRAPH_YMAX = 21
 
-HORIZONTAL_GAP_INPUT = 0.5
-VERTICAL_GAP = 1.5
+HORIZONTAL_GAP_INPUT = 0.3
+VERTICAL_GAP = 0.7
 
-NUM_INPUT_ROWS = 10
-NUM_INPUT_COLS = 20
+NUM_INPUT_ROWS = 20
+NUM_INPUT_COLS = 40
 
 XSTART_INPUT = 1.5
 XSTART_OUTPUT = 15
 YSTART = 3.0
+
+NODE_SIZE = 100
+FONT_SIZE = 4