diff --git a/.github/workflows/test-install.yml b/.github/workflows/test-install.yml
index 69732a4b7..a26ac2d45 100644
--- a/.github/workflows/test-install.yml
+++ b/.github/workflows/test-install.yml
@@ -198,11 +198,3 @@ jobs:
       run: |
         cd tests
         uv run python -m pytest --disable-pytest-warnings -v dataset_test.py filesindex_test.py datasetindex_test.py
-    - name: Run basic tests
-      run: |
-        cd tests
-        uv run python -m pytest --disable-pytest-warnings -v dataset_test.py filesindex_test.py datasetindex_test.py
-    - name: Run basic tests
-      run: |
-        cd tests
-        uv run python -m pytest --disable-pytest-warnings -v dataset_test.py filesindex_test.py datasetindex_test.py
diff --git a/batchflow/plotter/morphology.py b/batchflow/plotter/morphology.py
new file mode 100644
index 000000000..3f3c09d48
--- /dev/null
+++ b/batchflow/plotter/morphology.py
@@ -0,0 +1,125 @@
+"""Morphological operations implemented with numba to replace cv2 dependency."""
+
+import numpy as np
+from numba import njit, prange
+
+
+@njit
+def dilate(image, kernel, iterations=1):
+    """Dilate an image using a structuring element.
+
+    Parameters
+    ----------
+    image : numpy.ndarray
+        Input image to dilate.
+    kernel : numpy.ndarray
+        Structuring element (kernel) for dilation. Should contain 1s where
+        the structuring element is active and 0s elsewhere.
+    iterations : int, optional
+        Number of times to apply the dilation. Default is 1.
+
+    Returns
+    -------
+    numpy.ndarray
+        Dilated image with the same shape and dtype as input.
+
+    """
+    result = image.copy()
+
+    for _ in range(iterations):
+        result = _single_dilate(result, kernel)
+
+    return result
+
+@njit
+def erode(image, kernel, iterations=1):
+    """Erode an image using a structuring element.
+
+    Parameters
+    ----------
+    image : numpy.ndarray
+        Input image to erode.
+    kernel : numpy.ndarray
+        Structuring element (kernel) for erosion. Should contain 1s where
+        the structuring element is active and 0s elsewhere.
+    iterations : int, optional
+        Number of times to apply the erosion. Default is 1.
+
+    Returns
+    -------
+    numpy.ndarray
+        Eroded image with the same shape and dtype as input.
+
+    """
+    result = image.copy()
+
+    for _ in range(iterations):
+        result = _single_erode(result, kernel)
+
+    return result
+
+@njit(parallel=True)
+def _single_dilate(image, kernel):
+    """Single iteration of dilation operation."""
+    height, width = image.shape
+    kh, kw = kernel.shape
+    kh_half, kw_half = kh // 2, kw // 2
+
+    # Create output array
+    result = np.zeros_like(image)
+
+    # Apply dilation - for each output pixel, find max in kernel neighborhood
+    for i in prange(height):
+        for j in range(width):
+            max_val = image[i, j]  # Start with current pixel value
+
+            for ki in range(kh):
+                for kj in range(kw):
+                    if kernel[ki, kj] > 0:  # Only consider active kernel elements
+                        # Calculate the source image coordinates
+                        img_i = i + ki - kh_half
+                        img_j = j + kj - kw_half
+
+                        # Check bounds
+                        if 0 <= img_i < height and 0 <= img_j < width:
+                            if image[img_i, img_j] > max_val:
+                                max_val = image[img_i, img_j]
+
+            result[i, j] = max_val
+
+    return result
+
+@njit(parallel=True)
+def _single_erode(image, kernel):
+    """Single iteration of erosion operation."""
+    height, width = image.shape
+    kh, kw = kernel.shape
+    kh_half, kw_half = kh // 2, kw // 2
+
+    # Create output array
+    result = np.zeros_like(image)
+
+    # Apply erosion - for each output pixel, find min in kernel neighborhood
+    for i in prange(height):
+        for j in range(width):
+            min_val = image[i, j]  # Start with current pixel value
+
+            for ki in range(kh):
+                for kj in range(kw):
+                    if kernel[ki, kj] > 0:  # Only consider active kernel elements
+                        # Calculate the source image coordinates
+                        img_i = i + ki - kh_half
+                        img_j = j + kj - kw_half
+
+                        # Check bounds - treat out of bounds as 0 for erosion
+                        if 0 <= img_i < height and 0 <= img_j < width:
+                            if image[img_i, img_j] < min_val:
+                                min_val = image[img_i, img_j]
+                        else:
+                            # Outside bounds treated as 0, so erosion result should be 0
+                            min_val = 0
+                            break
+
+            result[i, j] = min_val
+
+    return result
diff --git a/batchflow/plotter/plot.py b/batchflow/plotter/plot.py
index d05d3fb3d..bba584351 100644
--- a/batchflow/plotter/plot.py
+++ b/batchflow/plotter/plot.py
@@ -103,7 +103,7 @@ def flatten(self, data):
 
     def dilate(self, data):
         """ Apply dilation to array. """
-        import cv2
+        from .morphology import dilate
         dilation_config = self.config.get('dilate', False)
 
         default_kernel = np.ones((3, 1), dtype=np.uint8)
@@ -116,7 +116,7 @@ def dilate(self, data):
                 dilation_config = {'kernel': np.ones(dilation_config, dtype=np.uint8)}
             elif 'kernel' in dilation_config and isinstance(dilation_config['kernel'], tuple):
                 dilation_config['kernel'] = np.ones(dilation_config['kernel'], dtype=np.uint8)
-            data = cv2.dilate(data.astype(np.float32), **dilation_config)
+            data = dilate(data.astype(np.float32), **dilation_config)
         return data
 
     def mask(self, data):
diff --git a/pyproject.toml b/pyproject.toml
index 4b5acc7cc..6bd6d1cf6 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "batchflow"
-version = "0.8.11"
+version = "0.8.12"
 description = "ML pipelines, model configuration and batch management"
 authors = [{ name = "Roman Kh", email = "rhudor@gmail.com" }]
 license = {text = "Apache License 2.0"}