Upload Ambrosia.py

Ambrosia.py

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+# CLASS: 
+    # pre_process_image
+    # METHODS:
+        # __init__
+            # INPUT:
+                # image_dir = (str) a full path to an image with multiple beetles and possibly a size reference circle
+                # manual_thresh_buffer (float) {optional} this is a manual way to control the binarizxing threshold. 
+                    # use this when beetles are broken up into multiple images\
+                    # inputs should range from -1 to 1. higehr vlaues include lighter colors into the blobs and lower values reduce blob size
+            # OUTPUT(ATTRIBUTES):
+                # image_dir = (str) the same directory as is given as an input to the iamge that is being processed
+                # image = (np.array) the original compound image
+                # grey_image = (np.array) the original compound image in greyscale
+                # bw_image = (np.array) the original image in binary black and white
+                # inv_bw_image = (np.array) the original image inverted black and white binary
+                # clear_inv_bw_image = (np.array) the inverted black and white binary original image with all components touching the border removed
+        # segment
+            # INPUT:
+                # cluster_num = (int) {default=2} the number of clusters used for kmeans to pick only the cluster with alrgest blobs
+                # image_edge_buffer = (int) {default=50} number of pixels to add to box borders
+            # OUTPUT(ATTRIBUTES):
+                # cluster_num = (int) the same as the input
+                # image_edge_buffer = (int) the same as the input
+                # labeled_image = (np.array) the original compound image that is labelled
+                # max_kmeans_label = (int) the label of the cluster with the largest object/blob
+                # image_selected_df = (pd.DataFrame) a dataframe with columns describing each segmented image: 
+                                                                                # 'centroid' = centre of the image
+                                                                                # 'bbox-0' = border 0
+                                                                                # 'bbox-1' = border 1
+                                                                                # 'bbox-2' = border 2
+                                                                                # 'bbox-3' = border 3
+                                                                                # 'orientation' = angle of image segment
+                                                                                # 'axis_major_length'
+                                                                                # 'axis_minor_length'
+                                                                                # 'area'
+                                                                                # 'area_filled'
+                # image_properties_df = (pd.DataFrame) similar to the image_selected_df, but inlcudes all the artefacts that are picked up
+                # col_image_lst = (list) a list with all the segmented images in color
+                # inv_bw_image_lst = (list) a list with all the segmented images in inverted binary black and white
+                # image_segment_count = (int) number of segmented images extracted from the compound image
+        # detect_outlier
+            # INPUT:
+                # None
+            # OUTPUT(ATTRIBUTES):
+                # image_array = (np.array) an array of the list of color segemented images (number of images, (R,G,B))
+                # r_ar_lst = (list) a list of arrays with flattened images red values
+                # g_ar_lst = (list) a list of arrays with flattened images green values
+                # b_ar_lst = (list) a list of arrays with flattened images blue values
+                # all_ar_lst = (list) a list of arrays with flattened images all red, green, and blue values
+                # px_dens_dist = (np.array) frequency distribution at 0-255 of all the values for each pixel
+                # corr_coef = (np.array) a square array of length equal to the number of segmented images showing the spearman correlation bewteen images
+                # corr_pval = (np.array) the pvalues associatedwith each correlation
+                # corr_coef_sum = (np.array) the sum of the correlations across each iamge compared to all others
+                # outlier_idx = (int) the index of the image with the lowest spearman correlation sum
+                # outlier_val = (float) the lowest sum correlation value
+                # outlier_col_image = (np.array) the color image of what is detected as the outlier
+                # outlier_inv_bw_image = (np.array) the inverted black on white image of the outlier segmented image
+                # outlier_bw_image = (np.array) the white on black image of the outlier segmented image
+                # image_selected_df = (pd.DataFrame) an updated dataframe that contains the circle identification data
+        # estimate_size
+            # INPUT:
+                # known_radius = (int) {default=1} the radius of the reference circle (shoudl be approximately the same size as the specimens to work best)
+                # canny_sigma = (int) {default=5} this describes how strict the cleaning border is for identifying the circle to place over the reference circle
+                # outlier_idx = (int) {default should be self.outlier_idx} change this when the circle is falsely detected
+            # OUTPUT(ATTRIBUTES):
+                # outlier_bw_image = (np.array) an updated version of the outlier iamge with a clean circle clear of artifacts
+                # outlier_idx = (int) same as the input
+                # clean_inv_bw_image_lst = (list) a list of cleaned white on black images no blobs touching hte border
+                # image_selected_df = (pd.DataFrame) an update to the dataframe of metadata containing pixel counts and relative area in mm^2 of all segmented images
+# *black and white is white on black
+
+# import requirements
+import os
+os.environ["OMP_NUM_THREADS"] = '1' #use this line on windows machines to avoid memory leaks
+import numpy as np
+import pandas as pd
+from math import ceil
+from skimage import io
+from skimage.filters import threshold_otsu
+from skimage.color import rgb2gray
+from skimage.segmentation import clear_border
+from skimage.measure import label, regionprops_table
+from skimage.transform import hough_circle, hough_circle_peaks
+from skimage.feature import canny
+from skimage.draw import disk
+from sklearn.cluster import KMeans
+from scipy.stats import spearmanr
+
+class pre_process_image:
+    # initialize image to be segmented from path
+    def __init__(self, image=None, image_dir=None, manual_thresh_buffer=0):
+        if image_dir is not None:
+            self.image_dir = image_dir.replace('\\','/') # full directory path to image
+            self.image = io.imread(image_dir) # read image from directory
+        elif image is not None:
+            self.image = image
+        else:
+            print("No image given to function")
+        self.grey_image = rgb2gray(self.image) #convert image to greyscale
+        self.bw_image = self.grey_image > threshold_otsu(self.grey_image) + manual_thresh_buffer # binarize image to be black & white
+        self.inv_bw_image = np.invert(self.bw_image) # invert black and white image
+        self.clear_inv_bw_image = clear_border(self.inv_bw_image) # remove anything touching image border
+    
+    # segment the image into smaller images
+    def segment(self, cluster_num=2, image_edge_buffer=50):
+        self.cluster_num = cluster_num
+        self.image_edge_buffer = image_edge_buffer
+        self.labeled_image = label(self.clear_inv_bw_image) #label image
+        image_properties_df = pd.DataFrame( # get the properties of each image used to segment blobs in image
+            regionprops_table(
+                self.labeled_image, 
+                properties=('centroid',
+                           'bbox',
+                           'orientation',
+                           'axis_major_length',
+                           'axis_minor_length',
+                           'area',
+                           'area_filled')
+                                    )
+                                )
+        # cluster boxes of blobs by size
+        kmean_result = KMeans(n_clusters=cluster_num, n_init='auto').fit(
+            np.array(
+                image_properties_df[['axis_major_length', 'axis_minor_length']]
+            )
+        )
+        image_properties_df['kmeans_label'] = kmean_result.labels_
+        # keep only the largest cluster (ball bearing needs to be a similar size as the beetles)
+        self.max_kmeans_label = int(image_properties_df.kmeans_label[image_properties_df['area'] == image_properties_df['area'].max()])
+        image_selected_df = image_properties_df[image_properties_df['kmeans_label']==self.max_kmeans_label]
+        self.image_properties_df = image_properties_df
+        # enlarge the boxes around blobs with buffer
+        coord_df = image_selected_df.loc[:,['bbox-0','bbox-1','bbox-2','bbox-3']].copy()
+        coord_df = coord_df.reset_index(drop = True)
+        image_selected_df = image_selected_df.reset_index(drop = True)
+        coord_df.loc[:,['bbox-0','bbox-1']] = coord_df.loc[:,['bbox-0','bbox-1']]-self.image_edge_buffer
+        coord_df.loc[:,['bbox-2','bbox-3']] = coord_df.loc[:,['bbox-2','bbox-3']]+self.image_edge_buffer
+        image_selected_df.loc[:,['bbox-0','bbox-1','bbox-2','bbox-3']] = coord_df.loc[:,['bbox-0','bbox-1','bbox-2','bbox-3']]
+        # limit boundaries to the initial image size without this the iamge size bugs out when the boundaries are negative and it removes the image
+        mask = image_selected_df[['bbox-0','bbox-1','bbox-2','bbox-3']]>=0
+        image_selected_df[['bbox-0','bbox-1','bbox-2','bbox-3']] = image_selected_df[['bbox-0','bbox-1','bbox-2','bbox-3']].where(mask, other=0)
+        self.image_selected_df = image_selected_df
+        # crop blobs from image based on box sizes and add to list
+        col_image_lst = []
+        inv_bw_image_lst = []
+        for i in range(len(image_selected_df)):
+            coord_i = image_selected_df.iloc[i]
+            # color images
+            crop_img = self.image[int(coord_i['bbox-0']):int(coord_i['bbox-2']), int(coord_i['bbox-1']):int(coord_i['bbox-3'])]
+            col_image_lst.append(crop_img)
+            # inverted black and white images
+            crop_bw_img = self.inv_bw_image[int(coord_i['bbox-0']):int(coord_i['bbox-2']), int(coord_i['bbox-1']):int(coord_i['bbox-3'])]
+            inv_bw_image_lst.append(crop_bw_img)
+        
+        #clear all images that are empty
+        # col_image_lst = [x for x in col_image_lst if x.shape[0] != 0] 
+        # inv_bw_image_lst = [x for x in inv_bw_image_lst if x.shape[0] != 0]
+        
+        self.col_image_lst = col_image_lst
+        self.inv_bw_image_lst = inv_bw_image_lst
+        self.image_segment_count = len(col_image_lst)
+        
+    def detect_outlier(self):
+        # convert list to numpy array
+        self.image_array = np.copy(np.array(self.col_image_lst, dtype='object'))
+        # initialize lists to store data in
+        r_ar_lst = []
+        g_ar_lst = []
+        b_ar_lst = []
+        all_ar_lst = []
+        for l in range(self.image_segment_count):
+            # flatten arrays
+            img_var = self.image_array[l]
+            r_ar = img_var[:,:,0].flatten() # red
+            g_ar = img_var[:,:,1].flatten() # green
+            b_ar = img_var[:,:,2].flatten() # blue
+            all_ar = img_var.flatten() # all
+            # collect data in lists
+            r_ar_lst.append(r_ar)
+            g_ar_lst.append(g_ar)
+            b_ar_lst.append(b_ar)
+            all_ar_lst.append(all_ar)
+        self.r_ar_lst = r_ar_lst
+        self.g_ar_lst = g_ar_lst
+        self.b_ar_lst = b_ar_lst
+        self.all_ar_lst = all_ar_lst
+        # get frequency of values at each rgb value(0-255)
+        values_array = all_ar_lst # use all, but can use any color
+        temp_dist_ar = np.zeros(shape=(255, self.image_segment_count))
+        for i in range(self.image_segment_count):
+            unique, counts = np.unique(values_array[i], return_counts=True)
+            temp_dict = dict(zip(unique, counts))
+            for j in temp_dict.keys():
+                temp_dist_ar[j-1][i] = temp_dict[j]
+        self.px_dens_dist = temp_dist_ar
+        # calculate the spearman correlation of distributions between images
+        # use spearman because it is a non-parametric measures
+        # use the sum of the correlation coefficients to identify the outlier image
+        corr_ar = np.array(spearmanr(temp_dist_ar, axis=0))
+        corr_coef_ar = corr_ar[0,:,:]
+        corr_pval_ar = corr_ar[1,:,:]
+        corr_sum_ar = corr_coef_ar.sum(axis=0)
+        self.corr_coef = corr_coef_ar
+        self.corr_pval = corr_pval_ar
+        self.corr_coef_sum = corr_sum_ar
+        self.outlier_idx = corr_sum_ar.argmin()
+        self.outlier_val = corr_sum_ar.min()
+        self.outlier_col_image = self.col_image_lst[self.outlier_idx]
+        self.outlier_inv_bw_image = self.inv_bw_image_lst[self.outlier_idx]
+        self.outlier_bw_image = np.invert(self.outlier_inv_bw_image)
+        # update metadata dataframe
+        self.image_selected_df['circle_class'] = 'non_circle'
+        self.image_selected_df.loc[self.outlier_idx, 'circle_class'] = 'circle'
+        
+    def estimate_size(self, outlier_idx, known_radius=1, canny_sigma=5):
+        for i in range(len(self.corr_coef_sum)):
+            # add appropriate data to dataframe when circle not detected at all
+            if i == (len(self.corr_coef_sum)-1):
+                self.outlier_idx = None
+                self.outlier_val = None
+                self.outlier_col_image = None
+                self.outlier_inv_bw_image = None
+                self.outlier_bw_image = None
+                # update metadata dataframe
+                self.image_selected_df['circle_class'] = 'non_circle'
+                self.image_selected_df['real_area'] = 0
+                clean_inv_bw_image_lst = []
+                for inv_bw_image in self.inv_bw_image_lst:
+                    # bw_image = np.invert(inv_bw_image)
+                    clean_inv_bw_image = clear_border(inv_bw_image)
+                    clean_inv_bw_image_lst.append(clean_inv_bw_image)
+                px_count_lst = []
+                for bw_img in clean_inv_bw_image_lst:
+                    unique_px_count = np.unique(bw_img, return_counts=True)
+                    px_dict = dict(zip(list(unique_px_count[0]), list(unique_px_count[1])))
+                    if len(px_dict) == 1:
+                        px_count = 0
+                    else:
+                        px_count = px_dict[True]
+                    px_count_lst.append(px_count)
+                self.image_selected_df['pixel_count'] = px_count_lst
+                print("Circle could not be found: "+str(self.image_dir))
+            else:
+                try:
+                    self.outlier_idx = np.argsort(self.corr_coef_sum)[i]
+                    self.outlier_val = np.sort(self.corr_coef_sum)[i]
+                    self.outlier_col_image = self.col_image_lst[self.outlier_idx]
+                    self.outlier_inv_bw_image = self.inv_bw_image_lst[self.outlier_idx]
+                    self.outlier_bw_image = np.invert(self.outlier_inv_bw_image)
+                    # update metadata dataframe
+                    self.image_selected_df['circle_class'] = 'non_circle'
+                    self.image_selected_df.loc[self.outlier_idx, 'circle_class'] = 'circle'
+                    outlier_inv_bw_image = np.invert(self.outlier_bw_image)
+                    # remove the border touching blobs of all b&w images
+                    clean_inv_bw_image_lst = []
+                    for inv_bw_image in self.inv_bw_image_lst:
+                        # bw_image = np.invert(inv_bw_image)
+                        clean_inv_bw_image = clear_border(inv_bw_image)
+                        clean_inv_bw_image_lst.append(clean_inv_bw_image)
+                    # default is the image detected with detect_outlier
+                    # change outlier_bw_image if this is not the ball bearing
+                    edges = canny(self.outlier_bw_image, sigma=canny_sigma)
+                    # Detect radius
+                    max_r = int((max(outlier_inv_bw_image.shape)/2) + (self.image_edge_buffer/2)) # max radius
+                    min_r = int((max_r-self.image_edge_buffer) - (self.image_edge_buffer/2)) # min radius
+                    hough_radii = np.arange(min_r, max_r, 10)
+                    hough_res = hough_circle(edges, hough_radii)
+                    # Select the most prominent circle
+                    accums, cx, cy, radii = hough_circle_peaks(hough_res, hough_radii, total_num_peaks=1)
+                    circy, circx = disk((cy[0], cx[0]), radii[0])
+                    # change the outlier image to fill in the circle
+                    outlier_inv_bw_image[circy, circx] = True # this index error occurs when the outlier object circle does not fit into the image
+
+                    self.outlier_inv_bw_image = clear_border(outlier_inv_bw_image)
+                    clean_inv_bw_image_lst[self.outlier_idx] = self.outlier_inv_bw_image
+                    self.clean_inv_bw_image_lst = clean_inv_bw_image_lst
+                    # get the area of the ball bearing based on the known radius
+                    circle_area = np.pi*(known_radius**2)
+                    px_count_lst = []
+                    for bw_img in clean_inv_bw_image_lst:
+                        px_count = np.unique(bw_img, return_counts=True)[1][1] # this index error occurs when the outlier object touches the edge of the image (forces recalculation of outlier)
+                        px_count_lst.append(px_count)
+                    self.image_selected_df['pixel_count'] = px_count_lst
+                    circle_px_count = px_count_lst[self.outlier_idx]
+                    area_ar = (np.array(px_count_lst)/circle_px_count)*circle_area
+                    self.image_selected_df['real_area'] = area_ar
+
+                    break
+
+                except IndexError:
+                    print('Updating circle classification for image: '+ str(self.image_dir))
+
+                else:
+                    print("No circle was found to estimate beetle size")
+                
+# add a section at line 219 that labels all area as 0 and all circle_class as non_circle when the least outlying object is considered.