Upload Connect_Components_Preprocessing.py

Connect_Components_Preprocessing.py

@@ -0,0 +1,136 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Oct  3 16:09:11 2022
+
+@author: jpeeples
+"""
+import cv2
+from skimage.filters import threshold_li
+from skimage.morphology import (erosion, dilation, opening, closing,  # noqa
+                                white_tophat)
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.decomposition import PCA
+from sklearn.preprocessing import MinMaxScaler
+
+
+def CCA_Preprocess(composite_img, k=2):
+
+    #Use pca to reduce vector 
+    reshaped_composite_img = np.reshape(composite_img,(-1,3))
+    
+    #Apply PCA
+    pca = PCA(n_components=1, whiten=True)
+    gray_vector = pca.fit_transform(reshaped_composite_img)
+    
+    #Visualize image
+    gray_img = np.reshape(gray_vector,(512,512))
+    
+    #Normalize and scale between 0 and 255 (inclusive)
+    scaler = MinMaxScaler(feature_range=(0, 1))
+    scaler.fit(gray_img)
+    gray_img = scaler.transform(gray_img)
+    
+    # Applying 7x7 Gaussian Blur
+    blurred = cv2.GaussianBlur(np.uint8(gray_img*255), (5, 5), 0)
+    
+    #Sharpen Image
+    kernel3 = np.array([[0, -1,  0],
+                       [-1,  5, -1],
+                        [0, -1,  0]])
+    
+    blurred = cv2.filter2D(src=blurred, ddepth=-1, kernel=kernel3)
+    
+    
+    # #Threshold image from background for CCA
+    thresh = threshold_li(gray_img)
+    binary = gray_img > thresh
+
+    # kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 400))
+
+    # Mask = cv2.erode(np.uint8(binary*255), kernel)
+
+    # # Expand the mask in the vertical direction
+    # kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (10, 400))
+    # Mask = cv2.dilate(Mask, kernel)
+
+    # # Erase the connection by placing zeros
+    # binary[np.where(Mask != 0)] = 0
+
+    
+    img = np.repeat(np.expand_dims(binary * gray_img, axis = -1), 3, axis=-1)
+     
+    # Applying threshold
+    threshold = np.uint8(binary*255)
+     
+    # Apply the Component analysis function
+    analysis = cv2.connectedComponentsWithStats(threshold,
+                                                4,
+                                                cv2.CV_32S)
+    (totalLabels, label_ids, values, centroid) = analysis
+     
+    # Initialize a new image to
+    # store all the output components
+    output = np.zeros(gray_img.shape, dtype="uint8")
+     
+    # Loop through each component
+    for i in range(1, totalLabels):
+       
+        # if (area > 10000) and (area < 50000):
+        if i in np.argsort(values[:,4])[-k:]:
+            # Create a new image for bounding boxes
+            new_img=img.copy()
+             
+            # Now extract the coordinate points
+            x1 = values[i, cv2.CC_STAT_LEFT]
+            y1 = values[i, cv2.CC_STAT_TOP]
+            w = values[i, cv2.CC_STAT_WIDTH]
+            h = values[i, cv2.CC_STAT_HEIGHT]
+             
+            # Coordinate of the bounding box
+            pt1 = (x1, y1)
+            pt2 = (x1+ w, y1+ h)
+            (X, Y) = centroid[i]
+             
+            # Bounding boxes for each component
+            cv2.rectangle(new_img,pt1,pt2,
+                          (0, 255, 0), 3)
+            cv2.circle(new_img, (int(X),
+                                 int(Y)),
+                       4, (0, 0, 255), -1)
+     
+            # Create a new array to show individual component
+            component = np.zeros(gray_img.shape, dtype="uint8")
+            componentMask = (label_ids == i).astype("uint8") * 255
+     
+            # Apply the mask using the bitwise operator
+            component = cv2.bitwise_or(component,componentMask)
+            output = cv2.bitwise_or(output, componentMask)
+             
+#     fig, axes = plt.subplots(ncols=3, figsize=(8, 2.5))
+#     ax = axes.ravel()
+#     ax[0] = plt.subplot(1, 3, 1)
+#     ax[1] = plt.subplot(1, 3, 2, sharex=ax[0], sharey=ax[0])
+#     ax[2] = plt.subplot(1, 3, 3, sharex=ax[0], sharey=ax[0])
+#     # ax[3] = plt.subplot(1,4,4,sharex=ax[0], sharey=ax[0])
+    
+#     ax[0].imshow(gray_img, cmap='BuGn')
+#     ax[0].set_title('Original')
+#     ax[0].axis('off')
+    
+#     ax[1].imshow(output, cmap='BuGn')
+#     ax[1].set_title('Connected Components')
+#     ax[1].axis('off')
+    
+#     ax[2].imshow(gray_img*(output/255), cmap='BuGn')
+#     ax[2].set_title('ROI')
+#     ax[2].axis('off')
+    
+#     plt.suptitle(title)
+    
+#     plt.tight_layout()
+#     fig.savefig('{}/Img_{}.png'.format(folder,img_index))
+#     plt.close()
+    
+    #Return gray image and mask
+    return gray_img, output/255