#33: Switch to mono-thread

datasets/raise/extract_noise.py

@@ -6,44 +6,29 @@ import numpy as np
 from PIL import Image
 import os
 from tqdm import tqdm
-from multiprocessing import Pool
-from threading import Lock
 
 imagesFolderPath = 'flat-field'
 npArrayFilePath = 'mean.npy'
 
-mutex = Lock()
-mean = None#np.zeros((3264, 4928, 3))
+mean = None
 numberOfImagesInMean = 0
 
-imagesFileNames = os.listdir(imagesFolderPath)
-
-pbar = tqdm(total = len(imagesFileNames))
+imagesFileNames = os.listdir(imagesFolderPath)#[:2]
 
 def treatImage(imageFileName):
-    global mean, numberOfImagesInMean, pbar
+    global mean, numberOfImagesInMean
     imageFilePath = f'{imagesFolderPath}/{imageFileName}'
     imagePil = Image.open(imageFilePath)
     imageNpArray = img_as_float(np.array(imagePil))
-    print('Before mean computation')
     imageNoiseNpArray = imageNpArray - denoise_tv_chambolle(imageNpArray, weight=0.2, channel_axis=-1)
-    print('After mean computation')
-    with mutex:
-        print('Start mutex section')
-        if mean is None:
-            print('Inter A')
-            mean = imageNoiseNpArray
-        else:
-            print('Inter B')
-            mean = ((mean * numberOfImagesInMean) + imageNoiseNpArray) / (numberOfImagesInMean + 1)
-        print('Intermediary 0')
-        numberOfImagesInMean += 1
-        print('Intermediary 1')
-        pbar.update(numberOfImagesInMean)
-        print('End mutex section')
+    if mean is None:
+        mean = imageNoiseNpArray
+    else:
+        mean = ((mean * numberOfImagesInMean) + imageNoiseNpArray) / (numberOfImagesInMean + 1)
+    numberOfImagesInMean += 1
 
-with Pool(5) as p:
-    p.map(treatImage, imagesFileNames)
+for imageFileName in tqdm(imagesFileNames):
+    treatImage(imageFileName)
 
 with open(npArrayFilePath, 'wb') as f:
     np.save(f, mean)