added new filters

operators/ApplyBlurFilterOperator/metadata.json

@@ -0,0 +1,9 @@
+{
+    "name": "ApplyCV2FilterOperator",
+    "dependency": {
+        "dockerfile": "Dockerfile_0"
+    },
+    "tags": [
+        "Example"
+    ]
+}

operators/ApplyBlurFilterOperator/models.py

@@ -0,0 +1,35 @@
+from pydantic import BaseModel, Field
+from enum import Enum
+
+class EffectType(str, Enum):
+    averaging = "averaging"
+    gaussianblurring = "gaussianblurring"
+    medianblurring = "medianblurring"
+    bilateralfiltering = "bilateralfiltering"
+
+class InputModel(BaseModel):
+    """
+    Apply effect to image
+    """
+
+    input_file_path: str = Field(
+        description="Path to the input file"
+    )
+    effect: EffectType = Field(
+        default='gaussianblurring',
+        description='Effect to be applied'
+    )
+
+
+class OutputModel(BaseModel):
+    """
+    Apply effect to image
+    """
+
+    message: str = Field(
+        default="",
+        description="Output message to log"
+    )
+    output_file_path: str = Field(
+        description="Path to the output file"
+    )

operators/ApplyBlurFilterOperator/operator.py

@@ -0,0 +1,44 @@
+from flowui.base_operator import BaseOperator
+from .models import InputModel, OutputModel
+from pathlib import Path
+import cv2
+
+
+# greyscale filter
+def apply_averaging(img):
+    return cv2.blur(img,(5,5))
+
+def apply_gaussianblurring(img):
+    return cv2.GaussianBlur(img,(5,5),0)
+
+def apply_medianblurring(img):
+    return cv2.medianBlur(img,5)
+
+def apply_bilateralfiltering(img):
+    return cv2.bilateralFilter(img,9,75,75)
+
+class ApplyBlurFilter(BaseOperator):
+
+    def operator_function(self, input_model: InputModel):
+        #Read the image
+        image = cv2.imread(input_model.input_file_path)
+
+        effect_types_map = dict(
+            averaging = apply_averaging,
+            gaussianblurring = apply_gaussianblurring,
+            medianblurring = apply_medianblurring,
+            bilateralfiltering = apply_bilateralfiltering,
+        )
+
+        chosen_effect = input_model.effect
+
+        image_processed = effect_types_map[chosen_effect](img=image)
+
+        # Save result
+        out_file_path = str(Path(self.results_path) / Path(input_model.input_file_path).name)
+        cv2.imwrite(out_file_path, image_processed)
+
+        return OutputModel(
+            message=f"Filtered image successfully saved to: {out_file_path}",
+            output_file_path=str(out_file_path)
+        )

operators/ApplyCV2FilterOperator/operator.py

@@ -45,7 +45,7 @@ def apply_sepia(img):
     ) 
     img_sepia[np.where(img_sepia > 255)] = 255  # normalizing values greater than 255 to 255
     img_sepia = np.array(img_sepia, dtype=np.uint8)
-    return
+    return img_sepia
 
 # pencil filter
 def apply_pencil(img):
@@ -118,4 +118,4 @@ def operator_function(self, input_model: InputModel):
         return OutputModel(
             message=f"Filtered image successfully saved to: {out_file_path}",
             output_file_path=str(out_file_path)
-        )
+        )

operators/ApplyEdgeFilterOperator/metadata.json

@@ -0,0 +1,9 @@
+{
+    "name": "ApplyCV2FilterOperator",
+    "dependency": {
+        "dockerfile": "Dockerfile_0"
+    },
+    "tags": [
+        "Example"
+    ],
+}

operators/ApplyEdgeFilterOperator/models.py

@@ -0,0 +1,25 @@
+from pydantic import BaseModel, Field
+
+
+class InputModel(BaseModel):
+    """
+    Apply effect to image
+    """
+
+    input_file_path: str = Field(
+        description="Path to the input file"
+    )
+
+
+class OutputModel(BaseModel):
+    """
+    Apply effect to image
+    """
+
+    message: str = Field(
+        default="",
+        description="Output message to log"
+    )
+    output_file_path: str = Field(
+        description="Path to the output file"
+    )

operators/ApplyEdgeFilterOperator/operator.py

@@ -0,0 +1,25 @@
+from flowui.base_operator import BaseOperator
+from .models import InputModel, OutputModel
+
+from pathlib import Path
+import random
+import cv2
+import numpy as np
+from scipy.interpolate import UnivariateSpline
+
+
+class ApplyEdgeFilter(BaseOperator):
+
+    def operator_function(self, input_model: InputModel):
+        #Read the image
+        image = cv2.imread(input_model.input_file_path)
+        image_processed = cv2.Canny(image,100,200)
+
+        # Save result
+        out_file_path = str(Path(self.results_path) / Path(input_model.input_file_path).name)
+        cv2.imwrite(out_file_path, image_processed)
+
+        return OutputModel(
+            message=f"Filtered image successfully saved to: {out_file_path}",
+            output_file_path=str(out_file_path)
+        )

operators/ApplyGradientFilterOperator/metadata.json

@@ -0,0 +1,9 @@
+{
+    "name": "ApplyCV2FilterOperator",
+    "dependency": {
+        "dockerfile": "Dockerfile_0"
+    },
+    "tags": [
+        "Example"
+    ]
+}

operators/ApplyGradientFilterOperator/models.py

@@ -0,0 +1,34 @@
+from pydantic import BaseModel, Field
+from enum import Enum
+
+class EffectType(str, Enum):
+    laplacian = "laplacian"
+    sobelx = "sobelx"
+    sobely = "sobely"
+
+class InputModel(BaseModel):
+    """
+    Apply effect to image
+    """
+
+    input_file_path: str = Field(
+        description="Path to the input file"
+    )
+    effect: EffectType = Field(
+        default='laplacian',
+        description='Effect to be applied'
+    )
+
+
+class OutputModel(BaseModel):
+    """
+    Apply effect to image
+    """
+
+    message: str = Field(
+        default="",
+        description="Output message to log"
+    )
+    output_file_path: str = Field(
+        description="Path to the output file"
+    )

operators/ApplyGradientFilterOperator/operator.py

@@ -0,0 +1,41 @@
+from flowui.base_operator import BaseOperator
+from .models import InputModel, OutputModel
+
+from pathlib import Path
+import cv2
+
+def apply_laplacian(img):
+    return cv2.Laplacian(img,cv2.CV_64F)
+
+def apply_sobelx(img):
+    return cv2.Sobel(img,cv2.CV_64F,1,0,ksize=5)
+
+def apply_sobely(img):
+    return cv2.Sobel(img,cv2.CV_64F,0,1,ksize=5)
+
+
+
+class ApplyGradientFilter(BaseOperator):
+
+    def operator_function(self, input_model: InputModel):
+        #Read the image
+        image = cv2.imread(input_model.input_file_path)
+
+        effect_types_map = dict(
+            laplacian = apply_laplacian,
+            sobelx = apply_sobelx,
+            sobely = apply_sobely,
+        )
+
+        chosen_effect = input_model.effect
+
+        image_processed = effect_types_map[chosen_effect](img=image)
+
+        # Save result
+        out_file_path = str(Path(self.results_path) / Path(input_model.input_file_path).name)
+        cv2.imwrite(out_file_path, image_processed)
+
+        return OutputModel(
+            message=f"Filtered image successfully saved to: {out_file_path}",
+            output_file_path=str(out_file_path)
+        )

operators/ApplyGrayscaleFilterOperator/metadata.json

@@ -0,0 +1,9 @@
+{
+    "name": "ApplyCV2FilterOperator",
+    "dependency": {
+        "dockerfile": "Dockerfile_0"
+    },
+    "tags": [
+        "Example"
+    ]
+}

operators/ApplyGrayscaleFilterOperator/models.py

@@ -0,0 +1,25 @@
+from pydantic import BaseModel, Field
+
+
+class InputModel(BaseModel):
+    """
+    Apply effect to image
+    """
+
+    input_file_path: str = Field(
+        description="Path to the input file"
+    )
+
+
+class OutputModel(BaseModel):
+    """
+    Apply effect to image
+    """
+
+    message: str = Field(
+        default="",
+        description="Output message to log"
+    )
+    output_file_path: str = Field(
+        description="Path to the output file"
+    )

operators/ApplyGrayscaleFilterOperator/operator.py

@@ -0,0 +1,23 @@
+from flowui.base_operator import BaseOperator
+from .models import InputModel, OutputModel
+
+from pathlib import Path
+import cv2
+
+
+class ApplyGrayscaleFilter(BaseOperator):
+
+    def operator_function(self, input_model: InputModel):
+        #Read the image
+        image = cv2.imread(input_model.input_file_path)
+
+        image_processed = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+        # Save result
+        out_file_path = str(Path(self.results_path) / Path(input_model.input_file_path).name)
+        cv2.imwrite(out_file_path, image_processed)
+
+        return OutputModel(
+            message=f"Filtered image successfully saved to: {out_file_path}",
+            output_file_path=str(out_file_path)
+        )

operators/ApplyMorphologicalFilterOperator/metadata.json

@@ -0,0 +1,9 @@
+{
+    "name": "ApplyCV2FilterOperator",
+    "dependency": {
+        "dockerfile": "Dockerfile_0"
+    },
+    "tags": [
+        "Example"
+    ]
+}

operators/ApplyMorphologicalFilterOperator/models.py

@@ -0,0 +1,38 @@
+from pydantic import BaseModel, Field
+from enum import Enum
+
+class EffectType(str, Enum):
+    erosion = "erosion"
+    dilation = "dilation"
+    opening = "opening"
+    closing = "closing"
+    morphologicalgradient = "morphologicalgradient"
+    tophat = "tophat"
+    blackhat = "blackhat"
+
+class InputModel(BaseModel):
+    """
+    Apply effect to image
+    """
+
+    input_file_path: str = Field(
+        description="Path to the input file"
+    )
+    effect: EffectType = Field(
+        default='erosion',
+        description='Effect to be applied'
+    )
+
+
+class OutputModel(BaseModel):
+    """
+    Apply effect to image
+    """
+
+    message: str = Field(
+        default="",
+        description="Output message to log"
+    )
+    output_file_path: str = Field(
+        description="Path to the output file"
+    )