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“cell_type”: “markdown”, “metadata”: {}, “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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