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kit/mics.py

+import pandas as pd
+import os
+import random
+import time
+import matplotlib.pyplot as plt
+import seaborn as sns
+import numpy as np
+from scipy.ndimage import gaussian_filter
+import torchvision.transforms.functional as TF
+from sklearn.decomposition import PCA
+import cv2
+import torchvision.transforms as T
+
+def read_material_csv(csv_folder,merge=None,pick=None):
+  '''
+  csv files in data_path/material_csv/name.csv
+  merge = [('a','b'),('c','d')] or merege == 'no'
+  pick = ['a','b','c','d'] or pick == 'all'
+  '''
+  name_list = [x.split(".")[0] for x in os.listdir(csv_folder)]
+  df = pd.DataFrame()
+  if pick =='all' and merge =='no':
+    for name in name_list:
+      tmp_df = pd.read_csv(os.path.join(csv_folder,f'{name}.csv')).drop('Unnamed: 0',axis=1)
+      df = pd.concat([df,tmp_df],ignore_index=True)
+    return df
+  print('*'*20)
+  print('All materials below:')
+  print(sorted(name_list))
+  print('*'*20)
+  if merge ==None or pick ==None: 
+    raise ValueError('Please input merge list or pick list')
+
+
+  def merge_material(merge_l1):
+    df = pd.DataFrame()
+    add_num = 0
+    for idx,name in enumerate(merge_l1):
+      tmp_df = pd.read_csv(f'{csv_folder}{name}.csv').drop('Unnamed: 0',axis=1)
+      tmp_df['Sample No'] += add_num
+      add_num += tmp_df['Sample No'].max() + 1
+      tmp_df['Material'] = merge_l1[0]
+      df = pd.concat([df,tmp_df],ignore_index=True)
+    return df
+  
+  if merge == 'no':
+    merge_df = pd.DataFrame()
+  else:
+    merge_df = pd.DataFrame()
+    for merge_l1 in merge:
+      if isinstance(merge_l1,list):
+        tmp_df = merge_material(merge_l1)
+        merge_df = pd.concat([merge_df,tmp_df],ignore_index=True)
+      else:
+        merge_df = merge_material(merge)
+        break
+      
+  for p_name in pick:
+    tmp_df = pd.read_csv(f'{csv_folder}{p_name}.csv').drop('Unnamed: 0',axis=1)
+    df = pd.concat([df,tmp_df],ignore_index=True)
+
+  df = pd.concat([df,merge_df],ignore_index=True)
+  print('Materials in the df:')
+  print(df['Material'].unique())
+  print('*'*20)
+  return df
+
+
+def seperate_pca_ellipse_results(df):
+    '''
+    input has old name condition
+    return df with a new column methods to seperate pca and ellipse
+    '''
+    df.loc[df.condition.str.contains('elps'),'methods'] = 'ellipse'
+    df.loc[df.condition.str.contains('pca'),'methods'] = 'pca'
+    for rps in ['pca_','elps_']:
+        df.loc[df.condition.str.contains(rps),'condition'] = df.condition.str.replace(rps,'')
+
+    return df
+
+
+def randsamp(s:int,e:int,n:int,seed:int = None):
+  """
+  s:start, e:end, n: pick n samples
+  retrun picked, rest
+  """
+  if seed:
+    random.seed(seed)
+  rest = [i for i in range(s,e)]
+  picked = sorted(random.sample(range(s,e),n))
+  for i in picked:
+    rest.remove(i)
+  return picked,rest
+
+
+def save_fig(df,save_path,x=None,y=None,filename=None):
+    t = time.strftime("%Y%m%d-%H%M", time.localtime()) 
+    if x ==None and y==None:
+        print(df.columns)
+        x = input('select x: ')
+        y = input('select y: ')
+    plt.figure(figsize=(15,5))
+    try:
+        sns.lineplot(data = df,x =x, y=y,hue='condition',style='methods')
+    except:
+        sns.lineplot(data = df,x =x, y=y,hue='condition')
+    plt.xticks(rotation=-35)
+    plt.title(y)
+    if filename==None:
+        plt.savefig(os.path.join(save_path,'%s_%s.pdf'%(y,t)),bbox_inches='tight')
+    else:
+        plt.savefig(os.path.join(save_path,'%s_%s_%s.pdf'%(filename,y,t)),bbox_inches='tight')
+
+def mytransform(img4c,mode='color'):
+  '''
+  img has 4 chennel, (4,400,400)
+  '''
+  img4c = [TF.to_pil_image(-TF.to_tensor(x)) for x in img4c]
+
+  
+  #Random rotate
+  rotater = T.RandomRotation(degrees=(0, 360))
+  img4c = [rotater(i) for i in img4c]
+  
+  if mode == 'color':  #adjust color
+     jitter = T.ColorJitter(brightness=[0.5,1.5], contrast=[0.5,1.5],saturation=[0.5,1.5])
+     img4c = [jitter(i) for i in img4c]
+
+  #pil to tensor
+  img4c = [np.squeeze(TF.pil_to_tensor(x).numpy()) for x in img4c] 
+  
+  return img4c
+
+def get_intensity_range(img):
+  '''
+  img (400,400)
+  return [start, end, step]
+  '''
+  start,end = None, None
+  minp,maxp = int(np.min(img)),int(np.max(img))
+  for intensity in range(minp,maxp,5):
+    num_points = len(np.where(img>=intensity)[0])
+    if num_points <= 120000:
+      if start == None:
+        start = intensity
+    if num_points <= 10000:
+      if end == None:
+        end = intensity
+  if start ==None: start = minp
+  if end == None or end >240: end = maxp
+  step = (end-start)//10
+
+  return start,start+step*10+1,step
+
+def roibox(img,l = 400):
+    if img.shape[1] < 500: return img
+    tmp = np.where(img>=np.max(img)-10) 
+    x_var,y_var = np.var(tmp[0]),np.var(tmp[1])
+    if x_var>5000 or y_var > 5000:
+        img_resize = roibox(img[1000:2500,2000:4000])
+        plt.title('Auto selected roibox')
+        plt.imshow(img_resize)
+        plt.show()
+        loop = input('if it is correct, input t else f: ')
+        while loop != 't':
+            print('possible more than 1 cluster, please input x,y limit')
+            plt.title('Original image')
+            plt.scatter(tmp[1],tmp[0])
+            plt.imshow(img)
+            plt.show()
+            try:
+                a, b = [int(x) for x in input('x: a,b: ').split(',')]
+                c, d = [int(x) for x in input('y: c,d: ').split(',')]
+                img_resize = roibox(img[c:d,a:b])  #img x is vertical
+                plt.imshow(img_resize)
+                plt.show()
+                loop = input('if it is correct, input t else f: ')
+            except:
+                loop == 'f'
+            
+        return img_resize
+
+    y_mid = int(np.mean(tmp[0]))
+    x_mid = int(np.mean(tmp[1]))
+    hl = int(l/2)
+    img_resize = img[y_mid-hl: y_mid+hl, x_mid-hl: x_mid+hl]
+
+    return img_resize
+
+def get_ae_2d(img, material, ai, sample_no , tf=0, blur=0):
+  '''#input is raw image. (4,400,400)
+  blur =1, apply, blur=0 nothing
+  ai: isotropy=0, anisotropy=1
+  tf: tranform = 1 apply tranformation
+  return list ['Sample No',"Material","Band","Intensity",'Eccentricity','Ellipse angle','pca_eps','pca_angle','isotropy label','blur','transform']
+  '''
+
+  results = []
+  num = 0
+
+  if tf == 1:
+    step_check = [0]
+    while 0 in step_check:
+      step_check = []
+      img_tf = mytransform(img)
+      for im in img:
+        _,_,step_tmp = get_intensity_range(im)
+        step_check.append(step_tmp)
+  elif tf == 0:
+    img_tf = img
+
+  for im in img_tf:
+    im = roibox(im)
+    if blur==1:
+      im = gaussian_filter(im.reshape(400,400,1),[3,3,0]).reshape(400,400)
+    num += 1   #num is band
+    start,end,step = get_intensity_range(im)
+    if step == 0:
+      print(material)
+      plt.imshow(im)
+      plt.show()
+      print(start,end,step)
+    
+    for intensity_idx,intensity in enumerate(range(start,end,step)):
+      imgcopy = im.copy()
+      points = list(np.where(imgcopy>=intensity)) #(2,n)
+
+      points[0],points[1] = points[1], points[0] #img(H,W) to (W,H)
+      points = np.array(points)  #(2.n)
+      if points.shape[1] < 10:
+        plt.imshow(imgcopy)
+        print(start,end,step,intensity,points)
+        return imgcopy
+      #print(points.shape)
+      el_params = cv2.fitEllipse(points.T)
+      #for y, downside is +
+      #x,y is inversed
+      x,y = el_params[0] #ellipse center
+      b,a = el_params[1] # a,b is fixed
+      angle = el_params[2]
+      eps = (1-(b/a)**2)**0.5 #eccentricity
+
+      #PCA
+      x1,y1 = points.mean(axis=1)
+      pca = PCA(n_components=2).fit(points.T) 
+      comp, sv, sv1 = pca.components_[0], pca.singular_values_[0],pca.singular_values_[1]
+      evr = pca.explained_variance_ratio_
+      pradians = np.arctan(comp[1]/comp[0])
+      sv, sv1 = max(sv,sv1), min(sv,sv1)
+      pca_eps = (1-(sv1/sv)**2)**0.5
+      pca_angle = np.degrees(pradians)
+      if pca_angle < 90: pca_angle += 90
+
+      
+      results.append([sample_no,material,num,intensity,intensity_idx,eps,angle,pca_eps,pca_angle,ai,blur,tf,evr[0],evr[1]]) #band,intensity,eps,angle
+
+  return np.array(results)
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