3d blob detection - Difference of Gaussian (DoG) method

Added a class to perform blob detection by using the difference of gaussian method from skimage. In the task blob detection in QIM notion there is an example of usage

qim3d/processing/blob_detection.py

+from skimage.feature import blob_dog
+import matplotlib.pyplot as plt
+import numpy as np
+from skimage import img_as_float
+
+class blob_detection():
+    def __init__(self, volume, background=0, min_sigma=1, max_sigma=50, sigma_ratio=1.6, threshold=0.5, overlap=0.5, threshold_rel=None, exclude_border=False):
+        """
+        Initialize the blob detection object
+        Args:
+            volume: The volume to detect blobs in
+            background: 0 if background is darker than the blobs, 1 if background is lighter than the blobs
+            min_sigma: The minimum standard deviation for Gaussian kernel
+            max_sigma: The maximum standard deviation for Gaussian kernel
+            num_sigma: The number of standard deviations between min and max
+            threshold: The absolute lower bound for scale space maxima
+            overlap: The fraction of area of two blobs that overlap
+        """
+        self.volume = volume
+        if background != 0:
+            self.volume = np.invert(self.volume)
+        self.volume = img_as_float(self.volume)
+        self.min_sigma = min_sigma
+        self.max_sigma = max_sigma
+        self.sigma_ratio = sigma_ratio
+        self.threshold = threshold
+        self.overlap = overlap
+        self.threshold_rel = threshold_rel
+        self.exclude_border = exclude_border
+        
+
+    def detect(self):
+        """
+        Detect blobs in the volume
+        Args:
+            None
+        Returns:    
+            blobs: The blobs found in the volume
+        """
+        blobs = blob_dog(self.volume, min_sigma=self.min_sigma, max_sigma=self.max_sigma, sigma_ratio=self.sigma_ratio, threshold=self.threshold, overlap=self.overlap, threshold_rel=self.threshold_rel, exclude_border=self.exclude_border)
+        blobs[:, 3] = blobs[:, 3] * np.sqrt(3) # Change sigma to radius
+        return blobs
+
+    def plot(self, blobs, z_slice=64):
+        """
+        Plot the blobs found on a slice of the volume
+        Args:
+            blobs: The blobs found in the volume (p, r, c, radius)
+            z_slice: The slice to plot
+
+        """
+        plt.imshow(self.volume[z_slice], cmap='gray')
+        for blob in blobs:
+            z, y, x, s = blob
+            if abs(z - z_slice) < s:  # The blob is in the slice
+                c = plt.Circle((x, y), s, color='red', linewidth=2, fill=False)
+                plt.gca().add_patch(c)
+
+        plt.show()
+        return
\ No newline at end of file