ai generated blocks (#271)

* ai generated blocks

* updated spec

frontend/core/blocks/apply-gaussian-blur-20241223-163019-869/Dockerfile

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+FROM python:3.9
+
+WORKDIR /app
+
+COPY . .
+
+RUN pip install --no-cache-dir -r requirements.txt

frontend/core/blocks/apply-gaussian-blur-20241223-163019-869/computations.py

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+
+def compute(image_path, kernel_size):
+    '''
+    Applies a Gaussian blur to an image using a specified kernel size.
+    
+    Parameters:
+    image_path (str): The file path to the input image that will be blurred.
+    kernel_size (int): The size of the kernel to be used for the Gaussian blur. It must be a positive odd integer.
+
+    Returns:
+    dict: A dictionary with the file path to the output image that has been blurred.
+    '''
+    import cv2
+    import os
+
+    # Read the image from the given path
+    image = cv2.imread(image_path)
+    if image is None:
+        raise ValueError('The image could not be loaded. Please check the file path.')
+
+    # Apply Gaussian blur to the image
+    blurred_image = cv2.GaussianBlur(image, (kernel_size, kernel_size), 0)
+
+    # Create the output file path
+    base, ext = os.path.splitext(image_path)
+    output_image_path = f"{base}_blurred{ext}"
+
+    # Save the blurred image to the output path
+    cv2.imwrite(output_image_path, blurred_image)
+
+    return {'blurred_image_path': output_image_path}
+
+
+def generate_fake_image():
+    import numpy as np
+    import cv2
+    # Create a fake image (e.g., 100x100 with 3 channels for RGB)
+    fake_image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
+    # Save the fake image to a temporary file
+    path = 'temp_fake_image.jpg'
+    cv2.imwrite(path, fake_image)
+    return path
+
+
+def test_compute():
+    import os
+    # Generate a fake image file path
+    image_path = generate_fake_image()
+    try:
+        # Test the compute function with the generated image and a kernel size
+        result = compute(image_path, 3)
+        print(f"Test passed. Blurred image saved at: {result['blurred_image_path']}")
+    except ValueError as e:
+        print(f"Test failed with error: {e}")
+    # Clean up generated files after the test
+    os.remove(image_path)
+    generated_blurred_path = 'temp_fake_image_blurred.jpg'
+    if os.path.exists(generated_blurred_path):
+        os.remove(generated_blurred_path)

frontend/core/blocks/apply-gaussian-blur-20241223-163019-869/requirements.txt

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+opencv-python-headless==4.10.0.84
+numpy==1.26.4

frontend/core/blocks/apply-gaussian-blur-20241223-163019-869/specs.json

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+{
+    "information": {
+        "id": "apply-gaussian-blur",
+        "name": "Apply gaussian blur",
+        "description": "Applies a Gaussian blur to an image using a specified kernel size.\n\nParameters:\nimage_path (str): The file path to the input image that will be blurred.\nkernel_size (int): The size of the kernel to be used for the Gaussian blur. It must be a positive odd integer.\n\nReturns:\ndict: A dictionary with the file path to the output image that has been blurred.",
+        "system_versions": [
+            "0.1"
+        ],
+        "block_version": "1.0",
+        "block_source": "core/blocks/generated-blocks/apply-gaussian-blur-20241223-163019-869",
+        "block_type": "compute"
+    },
+    "inputs": {
+        "image_path": {
+            "type": "file",
+            "connections": [
+                {
+                    "variable": "path",
+                    "block": "file-af7812m8dijr"
+                }
+            ],
+            "relays": []
+        },
+        "kernel_size": {
+            "type": "int",
+            "connections": [
+                {
+                    "variable": "parameter",
+                    "block": "parameter-rk3j8m1elpxq"
+                }
+            ],
+            "relays": []
+        }
+    },
+    "outputs": {
+        "blurred_image_path": {
+            "type": "str",
+            "connections": [],
+            "relays": []
+        }
+    },
+    "action": {
+        "container": {
+            "image": "apply-gaussian-blur",
+            "version": "latest",
+            "command_line": [
+                "python",
+                "entrypoint.py"
+            ]
+        }
+    },
+    "views": {
+        "node": {
+            "behavior": "modal",
+            "active": "True",
+            "title_bar": {
+                "background_color": "#228B22"
+            },
+            "preview": {},
+            "html": "",
+            "pos_x": "0",
+            "pos_y": "0",
+            "pos_z": "999"
+        }
+    },
+    "events": {}
+}

frontend/core/blocks/convert-colored-image-to-grayscale-20241223-163032-427/Dockerfile

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+FROM python:3.9
+
+WORKDIR /app
+
+COPY . .
+
+RUN pip install --no-cache-dir -r requirements.txt

frontend/core/blocks/convert-colored-image-to-grayscale-20241223-163032-427/computations.py

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+
+def compute(image_path):
+    '''
+    Convert a colored image to a grayscale image.
+
+    Parameters:
+    image_path (str): The file path to the colored image that needs to be converted to grayscale.
+
+    Returns:
+    dict: A dictionary with the key 'grayscale_image_path' and the value as the file path to the resulting grayscale image.
+    '''
+    import cv2
+    import os
+
+    # Read the colored image
+    colored_image = cv2.imread(image_path)
+
+    # Convert the image to grayscale
+    grayscale_image = cv2.cvtColor(colored_image, cv2.COLOR_BGR2GRAY)
+
+    # Create the output file path
+    base, ext = os.path.splitext(image_path)
+    grayscale_image_path = f"{base}_grayscale{ext}"
+
+    # Save the grayscale image
+    cv2.imwrite(grayscale_image_path, grayscale_image)
+
+    return {'grayscale_image_path': grayscale_image_path}
+
+
+def generate_fake_image_path():
+    import numpy as np
+    import cv2
+    import os
+
+    # Create a fake color image using numpy (e.g., 100x100 pixels with 3 color channels)
+    fake_image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
+
+    # Define the file path where this fake image will be saved
+    fake_image_path = "fake_image.jpg"
+
+    # Save the generated fake image to the file path
+    cv2.imwrite(fake_image_path, fake_image)
+
+    return fake_image_path
+
+
+def test_compute():
+    # Retrieve the generated fake image path
+    image_path = generate_fake_image_path()
+
+    # Run the compute function with the fake image path
+    result = compute(image_path)
+
+    # Print the result to verify that the function ran successfully
+    print(result)

frontend/core/blocks/convert-colored-image-to-grayscale-20241223-163032-427/requirements.txt

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+opencv-python-headless==4.10.0.84
+numpy==1.26.4

frontend/core/blocks/convert-colored-image-to-grayscale-20241223-163032-427/specs.json

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+{
+    "information": {
+        "id": "convert-colored-image-to-grayscale",
+        "name": "Convert colored image to grayscale",
+        "description": "Convert a colored image to a grayscale image.\n\nParameters:\nimage_path (str): The file path to the colored image that needs to be converted to grayscale.\n\nReturns:\ndict: A dictionary with the key 'grayscale_image_path' and the value as the file path to the resulting grayscale image.",
+        "system_versions": [
+            "0.1"
+        ],
+        "block_version": "1.0",
+        "block_source": "core/blocks/generated-blocks/convert-colored-image-to-grayscale-20241223-163032-427",
+        "block_type": "compute"
+    },
+    "inputs": {
+        "image_path": {
+            "type": "file",
+            "connections": [
+                {
+                    "variable": "path",
+                    "block": "file-af181m72dija"
+                }
+            ],
+            "relays": []
+        }
+    },
+    "outputs": {
+        "grayscale_image_path": {
+            "type": "str",
+            "connections": [],
+            "relays": []
+        }
+    },
+    "action": {
+        "container": {
+            "image": "convert-colored-image-to-grayscale",
+            "version": "latest",
+            "command_line": [
+                "python",
+                "entrypoint.py"
+            ]
+        }
+    },
+    "views": {
+        "node": {
+            "behavior": "modal",
+            "active": "True",
+            "title_bar": {
+                "background_color": "#228B22"
+            },
+            "preview": {},
+            "html": "",
+            "pos_x": "0",
+            "pos_y": "0",
+            "pos_z": "999"
+        }
+    },
+    "events": {}
+}

frontend/core/blocks/convert-image-to-binary-20241223-163041-154/Dockerfile

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+FROM python:3.9
+
+WORKDIR /app
+
+COPY . .
+
+RUN pip install --no-cache-dir -r requirements.txt

frontend/core/blocks/convert-image-to-binary-20241223-163041-154/computations.py

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+def compute(image_path, threshold):
+    '''
+    Convert a grayscale image to a binary image using a specified threshold.
+
+    Parameters:
+    image_path (str): The file path to the grayscale image that needs to be converted to binary.
+    threshold (int): The threshold value used to convert the image to binary. Pixel values above this threshold will be set to 255, and those below will be set to 0.
+
+    Returns:
+    dict: A dictionary with the key 'binary_image_path' and the value as the file path to the resulting binary image after conversion.
+    '''
+    import cv2
+    import os
+
+    # Read the grayscale image
+    image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
+
+    # Apply the threshold to convert the image to binary
+    _, binary_image = cv2.threshold(image, threshold, 255, cv2.THRESH_BINARY)
+
+    # Define the output path for the binary image
+    base, ext = os.path.splitext(image_path)
+    binary_image_path = f"{base}_binary{ext}"
+
+    # Save the binary image
+    cv2.imwrite(binary_image_path, binary_image)
+
+    return {'binary_image_path': binary_image_path}
+
+def generate_fake_image():
+    import numpy as np
+    import cv2
+
+    # Create a fake grayscale image using a numpy array
+    fake_image = np.random.randint(0, 256, (100, 100), dtype=np.uint8)
+    fake_image_path = 'fake_image.png'
+    cv2.imwrite(fake_image_path, fake_image)
+    return fake_image_path
+
+def test_compute():
+    image_path = generate_fake_image()
+    threshold = 128
+    output = compute(image_path, threshold)
+    print(output)

frontend/core/blocks/convert-image-to-binary-20241223-163041-154/requirements.txt

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+opencv-python-headless==4.10.0.84
+numpy==1.26.4

frontend/core/blocks/convert-image-to-binary-20241223-163041-154/specs.json

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+{
+    "information": {
+        "id": "convert-image-to-binary",
+        "name": "Convert image to binary",
+        "description": "Convert a grayscale image to a binary image using a specified threshold.\n\nParameters:\nimage_path (str): The file path to the grayscale image that needs to be converted to binary.\nthreshold (int): The threshold value used to convert the image to binary. Pixel values above this threshold will be set to 255, and those below will be set to 0.\n\nReturns:\ndict: A dictionary with the key 'binary_image_path' and the value as the file path to the resulting binary image after conversion.",
+        "system_versions": [
+            "0.1"
+        ],
+        "block_version": "1.0",
+        "block_source": "core/blocks/generated-blocks/convert-image-to-binary-20241223-163041-154",
+        "block_type": "compute"
+    },
+    "inputs": {
+        "image_path": {
+            "type": "file",
+            "connections": [
+                {
+                    "variable": "path",
+                    "block": "file-af181m72dija"
+                }
+            ],
+            "relays": []
+        },
+        "threshold": {
+            "type": "int",
+            "connections": [
+                {
+                    "variable": "parameter",
+                    "block": "parameter-adzakalp9mlc"
+                }
+            ],
+            "relays": []
+        }
+    },
+    "outputs": {
+        "binary_image_path": {
+            "type": "str",
+            "connections": [],
+            "relays": []
+        }
+    },
+    "action": {
+        "container": {
+            "image": "convert-image-to-binary",
+            "version": "latest",
+            "command_line": [
+                "python",
+                "entrypoint.py"
+            ]
+        }
+    },
+    "views": {
+        "node": {
+            "behavior": "modal",
+            "active": "True",
+            "title_bar": {
+                "background_color": "#228B22"
+            },
+            "preview": {},
+            "html": "",
+            "pos_x": "0",
+            "pos_y": "0",
+            "pos_z": "999"
+        }
+    },
+    "events": {}
+}