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Optical-Illusion-First-Fixation-Predictor
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3d22019
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Initial commit with Git LFS

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  2. .gitignore +3 -0
  3. Optical Illusion - Trained Models/duck-rabbit_models.pkl +3 -0
  4. Optical Illusion - Trained Models/face-vase_models.pkl +3 -0
  5. Optical Illusion - Trained Models/lily-woman_models.pkl +3 -0
  6. Optical Illusion - Trained Models/models_summary.csv +7 -0
  7. Optical Illusion - Trained Models/princess-oldMan_models.pkl +3 -0
  8. Optical Illusion - Trained Models/tiger-monkey_models.pkl +3 -0
  9. Optical Illusion - Trained Models/young-old_models.pkl +3 -0
  10. Optical Illusion Enriched Data/combined_engineered_data.csv +488 -0
  11. Optical Illusion Enriched Data/enriched_duck-rabbit.csv +78 -0
  12. Optical Illusion Enriched Data/enriched_face-vase.csv +81 -0
  13. Optical Illusion Enriched Data/enriched_lily-woman.csv +83 -0
  14. Optical Illusion Enriched Data/enriched_princess-oldMan.csv +83 -0
  15. Optical Illusion Enriched Data/enriched_tiger-monkey.csv +84 -0
  16. Optical Illusion Enriched Data/enriched_young-old.csv +84 -0
  17. Optical Illusion Images/duck-rabbit.png +3 -0
  18. Optical Illusion Images/face-vase.png +3 -0
  19. Optical Illusion Images/lily-woman.png +3 -0
  20. Optical Illusion Images/princess-oldMan.png +3 -0
  21. Optical Illusion Images/tiger-monkey.png +3 -0
  22. Optical Illusion Images/young-old.png +3 -0
  23. README.md +14 -0
  24. app.py +436 -0
  25. requirements.txt +7 -0
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Optical Illusion Enriched Data/enriched_duck-rabbit.csv ADDED
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1
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README.md ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ title: Optical Illusion First Fixation Predictor
3
+ emoji: 🚀
4
+ colorFrom: gray
5
+ colorTo: red
6
+ sdk: gradio
7
+ sdk_version: 5.33.2
8
+ app_file: app.py
9
+ pinned: false
10
+ license: mit
11
+ short_description: Universiti Malaya Cognitive Science OCC 2 Group 2
12
+ ---
13
+
14
+ Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
app.py ADDED
@@ -0,0 +1,436 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Gradio Interface for Optical Illusion Predictor
2
+
3
+ import gradio as gr
4
+ import numpy as np
5
+ import pandas as pd
6
+ import joblib
7
+ import matplotlib.pyplot as plt
8
+ from PIL import Image
9
+ import io
10
+ import os
11
+
12
+ # Define the image folder path
13
+ image_folder = "Optical Illusion Images"
14
+
15
+ # Define the enriched folder path
16
+ enriched_folder = 'Optical Illusion Enriched Data'
17
+ master_df = pd.read_csv(f'{enriched_folder}/combined_engineered_data.csv')
18
+
19
+ # Define the trained models folder path
20
+ trained_models_folder = 'Optical Illusion - Trained Models'
21
+
22
+ # Constants for image dimensions
23
+ DISPLAY_WIDTH = 1920
24
+ DISPLAY_HEIGHT = 1080
25
+
26
+ # Load all saved models at startup
27
+ def load_all_models():
28
+ """Load all saved models into memory"""
29
+ models = {}
30
+ for image_name in master_df['image_type'].unique():
31
+ try:
32
+ model_path = f'{trained_models_folder}/{image_name}_models.pkl'
33
+ models[image_name] = joblib.load(model_path)
34
+ print(f"✓ Loaded model for {image_name}")
35
+ except:
36
+ print(f"✗ Could not load model for {image_name}")
37
+ return models
38
+
39
+ # Load models
40
+ all_models = load_all_models()
41
+
42
+ # Function to load and resize images
43
+ def load_illusion_images(image_folder):
44
+ """Load optical illusion images from a folder and resize to 1920x1080"""
45
+ images = {}
46
+ for image_name in all_models.keys():
47
+ image_path = f'{image_folder}/{image_name}.png'
48
+ if os.path.exists(image_path):
49
+ # Load and resize image to 1920x1080
50
+ img = Image.open(image_path)
51
+ img_resized = img.resize((DISPLAY_WIDTH, DISPLAY_HEIGHT), Image.Resampling.LANCZOS)
52
+ images[image_name] = img_resized
53
+ print(f"✓ Loaded and resized image for {image_name}")
54
+ else:
55
+ print(f"✗ Image not found for {image_name} at {image_path}")
56
+ return images
57
+
58
+ # Load images
59
+ illusion_images = load_illusion_images(image_folder)
60
+
61
+ # Create placeholder image with correct dimensions
62
+ def create_placeholder_image(image_name):
63
+ """Create a placeholder image with the correct dimensions"""
64
+ fig, ax = plt.subplots(figsize=(19.2, 10.8), dpi=100)
65
+
66
+ # Handle None or empty image_name
67
+ if image_name is None:
68
+ display_text = 'NO IMAGE SELECTED\n\nSelect an image from the dropdown'
69
+ else:
70
+ display_text = f'{image_name.upper()}\n\nClick where you first look\n\n(Image not found)'
71
+
72
+ ax.text(0.5, 0.5, display_text,
73
+ transform=ax.transAxes, ha='center', va='center',
74
+ fontsize=32, fontweight='bold')
75
+ ax.set_xlim(0, DISPLAY_WIDTH)
76
+ ax.set_ylim(0, DISPLAY_HEIGHT)
77
+ ax.axis('off')
78
+
79
+ buf = io.BytesIO()
80
+ plt.savefig(buf, format='png', dpi=100, bbox_inches='tight', pad_inches=0)
81
+ buf.seek(0)
82
+ plt.close()
83
+
84
+ img = Image.open(buf)
85
+ # Ensure it's exactly 1920x1080
86
+ img_resized = img.resize((DISPLAY_WIDTH, DISPLAY_HEIGHT), Image.Resampling.LANCZOS)
87
+ return img_resized
88
+
89
+ def process_click(image_name, model_type, evt: gr.SelectData):
90
+ """Process click on image and return prediction"""
91
+
92
+ if evt is None:
93
+ return "Please click on the image where you first looked!", None, None
94
+
95
+ if image_name is None:
96
+ return "Please select an image first!", None, None
97
+
98
+ # Get click coordinates (Gradio provides them in image coordinates)
99
+ click_x_img, click_y_img = evt.index
100
+
101
+ # Convert to normalized coordinates
102
+ # Gradio coordinates: (0,0) is top-left
103
+ # Our coordinates: (0,0) is center
104
+ # x range: -960 to 960, y range: -540 to 540
105
+ click_x_norm = click_x_img - (DISPLAY_WIDTH / 2) # Convert to -960 to 960
106
+ click_y_norm = (DISPLAY_HEIGHT / 2) - click_y_img # INVERTED: Convert to -540 to 540
107
+
108
+ # Get model data
109
+ if image_name not in all_models:
110
+ return f"No model found for {image_name}", None, None
111
+
112
+ model_data = all_models[image_name]
113
+
114
+ # Calculate features
115
+ centroid_left = np.array([model_data['centroid_left_x'], model_data['centroid_left_y']])
116
+ centroid_right = np.array([model_data['centroid_right_x'], model_data['centroid_right_y']])
117
+ fixation = np.array([click_x_norm, click_y_norm])
118
+
119
+ dist_left = np.linalg.norm(fixation - centroid_left)
120
+ dist_right = np.linalg.norm(fixation - centroid_right)
121
+ bias = dist_right - dist_left
122
+
123
+ # Make prediction
124
+ X = np.array([[dist_left, dist_right, bias]])
125
+ model = model_data[f'{model_type}_model']
126
+ prediction = model.predict(X)[0]
127
+ probability = model.predict_proba(X)[0]
128
+
129
+ # Decode prediction
130
+ predicted_class = model_data['label_classes'][prediction]
131
+ confidence = probability[prediction]
132
+
133
+ # Create detailed message
134
+ message = f"""
135
+ ## Prediction Results
136
+
137
+ **You clicked at:** ({click_x_img}, {click_y_img}) in image coordinates
138
+ **Normalized position:** ({click_x_norm:.1f}, {click_y_norm:.1f})
139
+
140
+ **Distance to left centroid:** {dist_left:.1f} pixels
141
+ **Distance to right centroid:** {dist_right:.1f} pixels
142
+
143
+ ### Prediction: You likely see the **{predicted_class.upper()}** interpretation
144
+ ### Confidence: {confidence:.1%}
145
+ """
146
+
147
+ # Create visualization
148
+ viz = create_visualization(image_name, click_x_norm, click_y_norm,
149
+ predicted_class, confidence, model_type)
150
+
151
+ # Get example interpretations
152
+ interpretations = {
153
+ 'duck-rabbit': {'left': 'Duck', 'right': 'Rabbit'},
154
+ 'face-vase': {'left': 'Faces', 'right': 'Vase'},
155
+ 'young-old': {'left': 'Young Woman', 'right': 'Old Woman'},
156
+ 'princess-oldMan': {'left': 'Princess', 'right': 'Old Man'},
157
+ 'lily-woman': {'left': 'Lily', 'right': 'Woman'},
158
+ 'tiger-monkey': {'left': 'Tiger', 'right': 'Monkey'}
159
+ }
160
+
161
+ if image_name in interpretations:
162
+ specific = interpretations[image_name][predicted_class]
163
+ message += f"\n**Specific interpretation:** You see the **{specific}**"
164
+
165
+ return message, viz, create_stats_table(image_name, model_type)
166
+
167
+ def create_visualization(image_name, click_x, click_y, prediction, confidence, model_type='rf'):
168
+ """Create a visualization showing the click point, centroids, and prediction"""
169
+
170
+ fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))
171
+
172
+ # Get model data
173
+ model_data = all_models[image_name]
174
+ centroid_left = np.array([model_data['centroid_left_x'], model_data['centroid_left_y']])
175
+ centroid_right = np.array([model_data['centroid_right_x'], model_data['centroid_right_y']])
176
+
177
+ # Left plot: Decision boundary with proper axis range
178
+ resolution = 100
179
+ x_range = np.linspace(-960, 960, resolution) # Full x range
180
+ y_range = np.linspace(-540, 540, resolution) # Full y range
181
+ xx, yy = np.meshgrid(x_range, y_range)
182
+
183
+ # Calculate features for grid
184
+ points = np.c_[xx.ravel(), yy.ravel()]
185
+ features = []
186
+ for point in points:
187
+ dist_left = np.linalg.norm(point - centroid_left)
188
+ dist_right = np.linalg.norm(point - centroid_right)
189
+ bias = dist_right - dist_left
190
+ features.append([dist_left, dist_right, bias])
191
+
192
+ X = np.array(features)
193
+ model = model_data[f'{model_type}_model']
194
+ Z = model.predict(X)
195
+ Z = Z.reshape(xx.shape)
196
+
197
+ # Plot decision boundary
198
+ from matplotlib.colors import ListedColormap
199
+ colors = ListedColormap(['lightblue', 'lightcoral'])
200
+ ax1.contourf(xx, yy, Z, alpha=0.6, cmap=colors)
201
+
202
+ # Plot centroids
203
+ ax1.scatter(centroid_left[0], centroid_left[1],
204
+ c='blue', marker='*', s=500, edgecolors='black', label='Left centroid')
205
+ ax1.scatter(centroid_right[0], centroid_right[1],
206
+ c='red', marker='*', s=500, edgecolors='black', label='Right centroid')
207
+
208
+ # Plot user's click
209
+ ax1.scatter(click_x, click_y, c='green', marker='X', s=300,
210
+ edgecolors='black', linewidth=2, label='Your fixation', zorder=10)
211
+
212
+ # Draw lines to centroids
213
+ ax1.plot([click_x, centroid_left[0]], [click_y, centroid_left[1]],
214
+ 'b--', alpha=0.5, linewidth=2)
215
+ ax1.plot([click_x, centroid_right[0]], [click_y, centroid_right[1]],
216
+ 'r--', alpha=0.5, linewidth=2)
217
+
218
+ ax1.set_xlabel('X (pixels from center)')
219
+ ax1.set_ylabel('Y (pixels from center)')
220
+ ax1.set_title(f'Decision Space - {model_type.upper()} Model')
221
+ ax1.grid(True, alpha=0.3)
222
+ ax1.legend()
223
+ ax1.set_xlim(-960, 960) # Full width range
224
+ ax1.set_ylim(-540, 540) # Full height range
225
+ ax1.set_aspect('equal')
226
+
227
+ # Right plot: Statistics
228
+ image_df = master_df[master_df['image_type'] == image_name]
229
+
230
+ # Create bar chart of choices
231
+ choice_counts = image_df['choice'].value_counts()
232
+ ax2.bar(choice_counts.index, choice_counts.values,
233
+ color=['blue' if x == 'left' else 'red' for x in choice_counts.index])
234
+
235
+ # Add prediction annotation
236
+ ax2.text(0.5, 0.95, f'Your Predicted Choice: {prediction.upper()}',
237
+ transform=ax2.transAxes, ha='center', va='top',
238
+ fontsize=16, fontweight='bold',
239
+ bbox=dict(boxstyle='round', facecolor='lightgreen' if prediction == 'left' else 'lightcoral'))
240
+
241
+ ax2.text(0.5, 0.85, f'Confidence: {confidence:.1%}',
242
+ transform=ax2.transAxes, ha='center', va='top', fontsize=14)
243
+
244
+ ax2.set_xlabel('Interpretation')
245
+ ax2.set_ylabel('Number of Participants')
246
+ ax2.set_title(f'Overall Distribution for {image_name}')
247
+
248
+ # Add model accuracy info
249
+ ax2.text(0.5, 0.05, f'Model CV Accuracy: {model_data[f"cv_accuracy_{model_type}"]:.1%}',
250
+ transform=ax2.transAxes, ha='center', va='bottom', fontsize=12,
251
+ style='italic', alpha=0.7)
252
+
253
+ plt.tight_layout()
254
+
255
+ # Convert plot to image
256
+ buf = io.BytesIO()
257
+ plt.savefig(buf, format='png', dpi=100, bbox_inches='tight')
258
+ buf.seek(0)
259
+ plt.close()
260
+
261
+ return Image.open(buf)
262
+
263
+ def create_stats_table(image_name, model_type):
264
+ """Create a statistics table for the selected image"""
265
+ model_data = all_models[image_name]
266
+ image_df = master_df[master_df['image_type'] == image_name]
267
+
268
+ stats = {
269
+ 'Metric': ['Total Participants', 'Left Choices', 'Right Choices',
270
+ 'Model Accuracy', 'Class Balance'],
271
+ 'Value': [
272
+ len(image_df),
273
+ model_data['class_distribution'].get('left', 0),
274
+ model_data['class_distribution'].get('right', 0),
275
+ f"{model_data[f'cv_accuracy_{model_type}']:.1%}",
276
+ f"{min(model_data['class_distribution'].values()) / len(image_df):.1%}"
277
+ ]
278
+ }
279
+
280
+ return pd.DataFrame(stats)
281
+
282
+ # Create Gradio Interface
283
+ with gr.Blocks(title="Optical Illusion First Fixation Predictor") as demo:
284
+ gr.Markdown("""
285
+ # 🧠 Optical Illusion First Fixation Predictor
286
+
287
+ This tool predicts which interpretation of an ambiguous image you'll see based on where you first look!
288
+
289
+ ## How to use:
290
+ 1. Select an optical illusion from the dropdown
291
+ 2. Choose a model type (Random Forest usually performs better)
292
+ 3. Click on the image where your eyes first landed
293
+ 4. See the prediction of what you're likely to perceive!
294
+
295
+ **Note:** Images are displayed at 1920x1080 resolution. Click where you naturally first looked at the image.
296
+ """)
297
+
298
+ with gr.Row():
299
+ with gr.Column(scale=2):
300
+ # Image selection
301
+ available_images = list(all_models.keys()) if all_models else []
302
+ default_image = available_images[0] if available_images else None
303
+
304
+ image_choice = gr.Dropdown(
305
+ choices=available_images,
306
+ value=default_image,
307
+ label="Select Optical Illusion",
308
+ info="Choose which ambiguous image to analyze"
309
+ )
310
+
311
+ # Model selection
312
+ model_type = gr.Radio(
313
+ choices=["rf", "lr"],
314
+ value="rf",
315
+ label="Model Type",
316
+ info="Random Forest (rf) usually performs better"
317
+ )
318
+
319
+ # Display image with proper dimensions
320
+ image_display = gr.Image(
321
+ label="Click where you first look",
322
+ interactive=True,
323
+ type="pil",
324
+ height=1080, # Set to full height
325
+ width=1920 # Set to full width
326
+ )
327
+
328
+ with gr.Column(scale=1):
329
+ # Results section
330
+ prediction_output = gr.Markdown(label="Prediction Results")
331
+ stats_table = gr.DataFrame(label="Image Statistics")
332
+
333
+ # Visualization output
334
+ with gr.Row():
335
+ visualization_output = gr.Image(
336
+ label="Analysis Visualization",
337
+ type="pil"
338
+ )
339
+
340
+ # Information section
341
+ with gr.Accordion("ℹ️ About the Models", open=False):
342
+ gr.Markdown("""
343
+ ### Model Information
344
+ - **Random Forest (RF)**: An ensemble learning method that creates multiple decision trees
345
+ - **Logistic Regression (LR)**: A linear model for binary classification
346
+
347
+ ### Features Used
348
+ - Distance from first fixation to left interpretation centroid
349
+ - Distance from first fixation to right interpretation centroid
350
+ - Bias (difference between distances)
351
+
352
+ ### Coordinate System
353
+ - Image dimensions: 1920x1080 pixels
354
+ - Normalized coordinates: X(-960 to 960), Y(-540 to 540)
355
+ - Center point (0,0) is the middle of the image
356
+
357
+ ### Training Method
358
+ - Leave-One-Participant-Out Cross-Validation (LOPO-CV)
359
+ - Ensures model generalizes to new participants
360
+ """)
361
+
362
+ # Summary statistics
363
+ with gr.Accordion("📊 Model Performance Summary", open=False):
364
+ summary_data = []
365
+ for img_name, model_data in all_models.items():
366
+ summary_data.append({
367
+ 'Image': img_name,
368
+ 'RF Accuracy': f"{model_data['cv_accuracy_rf']:.1%}",
369
+ 'LR Accuracy': f"{model_data['cv_accuracy_lr']:.1%}",
370
+ 'Total Samples': model_data['total_samples']
371
+ })
372
+
373
+ gr.DataFrame(
374
+ value=pd.DataFrame(summary_data),
375
+ label="Cross-Validation Accuracies"
376
+ )
377
+
378
+ # Function to update image when selection changes
379
+ def update_image(image_name):
380
+ # Handle None case
381
+ if image_name is None:
382
+ return create_placeholder_image(None), "Please select an image first!", None
383
+
384
+ # Use real images if available, otherwise use placeholder
385
+ if image_name in illusion_images:
386
+ return illusion_images[image_name], "Click on the image to get a prediction!", None
387
+ else:
388
+ return create_placeholder_image(image_name), "Click on the image to get a prediction!", None
389
+
390
+ # Connect events
391
+ image_choice.change(
392
+ fn=update_image,
393
+ inputs=[image_choice],
394
+ outputs=[image_display, prediction_output, stats_table]
395
+ )
396
+
397
+ # Handle click event
398
+ image_display.select(
399
+ fn=process_click,
400
+ inputs=[image_choice, model_type],
401
+ outputs=[prediction_output, visualization_output, stats_table]
402
+ )
403
+
404
+ # Load initial image
405
+ demo.load(
406
+ fn=update_image,
407
+ inputs=[image_choice],
408
+ outputs=[image_display, prediction_output, stats_table]
409
+ )
410
+
411
+ # Examples section
412
+ if available_images:
413
+ gr.Markdown("## 📌 Example Interpretations")
414
+ with gr.Row():
415
+ # Only show examples for available images
416
+ example_list = []
417
+ for img in ["duck-rabbit", "face-vase", "young-old", "tiger-monkey"]:
418
+ if img in available_images:
419
+ example_list.append([img, "rf"])
420
+
421
+ if example_list:
422
+ gr.Examples(
423
+ examples=example_list,
424
+ inputs=[image_choice, model_type],
425
+ label="Try these examples"
426
+ )
427
+
428
+ # Debug info
429
+ print(f"\nImage folder: {image_folder}")
430
+ print(f"Images loaded: {list(illusion_images.keys())}")
431
+ print(f"Models loaded: {list(all_models.keys())}")
432
+ print(f"Image dimensions: {DISPLAY_WIDTH}x{DISPLAY_HEIGHT}")
433
+
434
+ # Launch the app
435
+ if __name__ == "__main__":
436
+ demo.launch(share=True)
requirements.txt ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ gradio==5.31.0
2
+ numpy
3
+ pandas
4
+ joblib
5
+ matplotlib
6
+ scikit-learn
7
+ pillow