Merge pull request #3 from techstartucalgary/arham

Arham

Author: minhazzz

.gitignore

@@ -1,6 +1,17 @@
 # See https://help.github.com/articles/ignoring-files/ for more about ignoring files.
 
-# dependencies
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+venv/
+env/
+ENV/
+.venv
+
+# Node.js / Next.js
 /node_modules
 /.pnp
 .pnp.js
@@ -17,6 +28,7 @@
 
 # misc
 .DS_Store
+Thumbs.db
 *.pem
 
 # debug
@@ -33,3 +45,10 @@ yarn-error.log*
 # typescript
 *.tsbuildinfo
 next-env.d.ts
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+*~

backend-workflow/binary_image_classifier_model.keras

@@ -0,0 +1,112 @@
+import cv2
+import numpy as np
+
+def is_blurry(img_path):
+    img = cv2.imread(img_path)
+    if img is None:
+        return False
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    laplacian = cv2.Laplacian(gray, cv2.CV_64F)
+    cv2.imshow("img", img)
+    cv2.imshow("gray", gray)
+    cv2.imshow("laplacian", laplacian)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+    score = laplacian.var()
+    return score < 100
+
+def check_contrast_brightness(img_path, min_contrast=40.0, min_brightness=30.0, max_brightness=230.0):
+    img = cv2.imread(img_path)
+    if img is None:
+        return {
+            'has_sufficient_contrast': False,
+            'has_good_brightness': False,
+            'contrast_score': 0.0,
+            'brightness_score': 0.0,
+            'is_acceptable': False
+        }
+    
+    # Convert to grayscale for analysis
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    
+    # Calculate contrast (standard deviation of pixel intensities)
+    # Higher std dev = more contrast between different regions
+    contrast_score = float(gray.std())
+    has_sufficient_contrast = contrast_score >= min_contrast
+    
+    # Calculate brightness (mean pixel intensity)
+    # Values range from 0 (black) to 255 (white)
+    brightness_score = float(gray.mean())
+    has_good_brightness = min_brightness <= brightness_score <= max_brightness
+    
+    is_acceptable = has_sufficient_contrast and has_good_brightness
+    
+    return {
+        'has_sufficient_contrast': has_sufficient_contrast,
+        'has_good_brightness': has_good_brightness,
+        'contrast_score': contrast_score,
+        'brightness_score': brightness_score,
+        'is_acceptable': is_acceptable
+    }
+
+def check_aspect_ratio(img_path, min_ratio=0.7, max_ratio=1.4):
+    img = cv2.imread(img_path)
+    if img is None:
+        return {
+            'has_valid_aspect_ratio': False,
+            'aspect_ratio': 0.0,
+            'width': 0,
+            'height': 0,
+            'ratio_description': 'invalid'
+        }
+    
+    height, width = img.shape[:2]
+    
+    # Calculate min(width, height) / max(width, height)
+    # This gives a value between 0 and 1, where 1 means square
+    min_dim = min(width, height)
+    max_dim = max(width, height)
+    aspect_ratio = float(min_dim) / float(max_dim)
+    
+    # Check if ratio is within valid range: 0.7 ≤ ratio ≤ 1.4
+    # Note: Since min/max ratio is always ≤ 1.0, the effective range is 0.7 to 1.0
+    has_valid_aspect_ratio = min_ratio <= aspect_ratio <= max_ratio
+    
+    # Calculate traditional width:height ratio for description
+    width_height_ratio = float(width) / float(height)
+    
+    # Create a readable description of the ratio
+    if 0.95 <= width_height_ratio <= 1.05:
+        ratio_description = "1:1 (square)"
+    elif width_height_ratio < 1.0:
+        # Portrait orientation (height > width)
+        simplified_ratio = f"1:{1/width_height_ratio:.2f}"
+        ratio_description = f"{simplified_ratio} (portrait)"
+    else:
+        # Landscape orientation (width > height)
+        simplified_ratio = f"{width_height_ratio:.2f}:1"
+        ratio_description = f"{simplified_ratio} (landscape)"
+    
+    return {
+        'has_valid_aspect_ratio': has_valid_aspect_ratio,
+        'aspect_ratio': aspect_ratio,  # This is min/max ratio
+        'width_height_ratio': width_height_ratio,  # Traditional width:height ratio
+        'width': width,
+        'height': height,
+        'ratio_description': ratio_description
+    }
+
+if __name__ == "__main__":
+    img_path = "test5.png"  # Relative path for cross-platform compatibility
+    
+    # Blurriness check
+    blurry = is_blurry(img_path)
+    print("blurry" if blurry else "not blurry")
+    
+    # Contrast/Brightness check
+    result = check_contrast_brightness(img_path)
+    print("bright/contrasted enough" if result['is_acceptable'] else "not bright/contrasted enough")
+    
+    # Aspect Ratio check
+    aspect_result = check_aspect_ratio(img_path)
+    print("valid aspect ratio" if aspect_result['has_valid_aspect_ratio'] else "invalid aspect ratio")

backend-workflow/blurry.py

@@ -0,0 +1,34 @@
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras.utils import load_img, img_to_array
+import numpy as np
+import sys
+from pathlib import Path
+
+# Get the directory where the model is located
+script_dir = Path(__file__).parent
+model_path = script_dir / 'binary_image_classifier_model.keras'
+model = keras.models.load_model(str(model_path))
+
+# Get image path from command line
+img_path = sys.argv[1]
+
+# Load image and convert to array
+img = load_img(img_path, target_size=(180, 180))
+img_array = img_to_array(img)
+img_array = np.expand_dims(img_array, axis=0)
+
+# Make prediction
+prediction = model.predict(img_array, verbose=0)
+score = prediction[0][0]
+
+# Display results
+print(f"\nImage: {Path(img_path).name}")
+print(f"Raw prediction score: {score:.4f}")
+print(f"Threshold: 0.5\n")
+if score > 0.5:
+    print("[+] FLOOR PLAN DETECTED")
+    print(f"Confidence: {score:.2%}")
+else:
+    print("[-] NOT A FLOOR PLAN")
+    print(f"Confidence: {(1-score):.2%}")