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   "source": [
    "# Quantum Edge Detection (Placeholder Example)\\n",
    "\\n",
    "This notebook demonstrates the pipeline using the INEQR encoding and the placeholder `quantum_edge_detection` function.\\n",
    "\\n",
    "**Expected Outcome:** The edge detection will currently return a black image because the gradient calculation and interpretation logic within `quantum_edge_detection` are not yet implemented."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\\n",
    "import numpy as np\\n",
    "from PIL import Image\\n",
    "import os\\n",
    "\\n",
    "# Ensure plots are displayed inline\\n",
    "%matplotlib inline\\n",
    "\\n",
    "# Assuming the notebook is run from the 'notebooks' directory\\n",
    "# Adjust path if running from project root\\n",
    "import sys\\n",
    "project_root = os.path.abspath('..') \\n",
    "if project_root not in sys.path:\\n",
    "    sys.path.insert(0, project_root)\\n",
    "\\n",
    "try:\\n",
    "    from quscope.image_processing import preprocess_image, quantum_edge_detection\\n",
    "    from quscope.qml import encode_image_ineqr \\n",
    "except ImportError as e:\\n",
    "    print(f\"ImportError: {e}\\nEnsure the 'quscope' package is installed correctly (pip install -e .)\")\\n",
    "    # You might need to restart the kernel after installation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Load and Preprocess Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define image path and preprocessing parameters\\n",
    "image_filename = 'duck_image.jpeg'\\n",
    "image_path = os.path.join('..', 'data', 'images', image_filename)\\n",
    "resize_shape = (8, 8) # Small size for quicker simulation\\n",
    "\\n",
    "# Load and preprocess\\n",
    "try:\\n",
    "    original_image = Image.open(image_path)\\n",
    "    img_array_gray = preprocess_image(image_path, size=resize_shape, grayscale=True)\\n",
    "    print(f\"Image loaded and preprocessed to shape: {img_array_gray.shape}\")\\n",
    "    \\n",
    "    # Display the preprocessed image\\n",
    "    plt.figure(figsize=(3, 3))\\n",
    "    plt.imshow(img_array_gray, cmap='gray')\\n",
    "    plt.title(f'Preprocessed Image ({resize_shape[0]}x{resize_shape[1]})')\\n",
    "    plt.axis('off')\\n",
    "    plt.show()\\n",
    "except FileNotFoundError:\\n",
    "    print(f\"Error: Image file not found at {image_path}\")\\n",
    "except Exception as e:\\n",
    "     print(f\"An error occurred during preprocessing: {e}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Encode Image using INEQR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encode the preprocessed image into a quantum circuit using INEQR\\n",
    "try:\\n",
    "    ineqr_circuit = encode_image_ineqr(img_array_gray)\\n",
    "    print(f\"Image encoded using INEQR.\\nCircuit Name: {ineqr_circuit.name}\\nNumber of Qubits: {ineqr_circuit.num_qubits}\")\\n",
    "    \\n",
    "    # Optional: Draw the circuit (can be very large)\\n",
    "    # print(\`\`\`Circuit Diagram:\`\`\`)\\n",
    "    # print(ineqr_circuit.draw(output='text', fold=120)) \\n",
    "except NameError: # If img_array_gray wasn't created due to previous error\\n",
    "    print(\"Skipping encoding because image preprocessing failed.\")\\n",
    "except Exception as e:\\n",
    "    print(f\"An error occurred during INEQR encoding: {e}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Apply Quantum Edge Detection (Placeholder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply the placeholder edge detection function\\n",
    "try:\\n",
    "    image_shape = img_array_gray.shape\\n",
    "    threshold = 0.5 # Example threshold (won't have effect yet)\\n",
    "    \\n",
    "    print(\"Running placeholder quantum_edge_detection...\")\\n",
    "    edge_map = quantum_edge_detection(ineqr_circuit, image_shape, threshold=threshold)\\n",
    "    print(\"Placeholder edge detection finished.\")\\n",
    "    \\n",
    "    # Display the result\\n",
    "    plt.figure(figsize=(3, 3))\\n",
    "    plt.imshow(edge_map, cmap='gray')\\n",
    "    plt.title(f'Edge Detection Output (Placeholder)')\\n",
    "    plt.axis('off')\\n",
    "    plt.show()\\n",
    "    \\n",
    "    print(f\"Output shape: {edge_map.shape}\")\\n",
    "    print(f\"Unique values in output: {np.unique(edge_map)}\")\\n",
    "except NameError: # If ineqr_circuit or img_array_gray wasn't created\\n",
    "     print(\"Skipping edge detection because encoding or preprocessing failed.\")\\n",
    "except Exception as e:\\n",
    "    print(f\"An error occurred during edge detection: {e}\")"
   ]
  }
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