Works, used to test all the files

WorkingPyDemo.py

@@ -14,6 +14,14 @@ folder_name = "images"
 outputlocation = ""
 
 def file_extractor(dirname="images"):
+    """Find all the files in the directory
+    
+    Parameters:
+        dirname (str): the directory name
+        
+    Returns:
+        files (list): a list of all the files in the directory
+    """
     files = os.listdir(dirname)
     scenes = []
     for file in files:
@@ -24,6 +32,13 @@ def file_extractor(dirname="images"):
     return scenes
 
 def image_extractor(scenes):
+    """
+    This function gives the list of all of the valid images
+    Parameters:
+        scenes (list): a list of all the files in the directory
+    Returns:
+        images (list): a list of all the images in the directory
+    """
     image_folder = []
     for scene in scenes:
         files = os.listdir(scene)
@@ -34,29 +49,6 @@ def image_extractor(scenes):
                 image_folder.append(os.path.join(scene, file))
     return image_folder #returns a list of file paths to .tiff files in the specified directory given in file_extractor
 
-def im_distribution(images, num):
-    """
-    Function that extracts tiff files from specific cameras and returns a list of all
-    the tiff files corresponding to that camera. i.e. all pictures labeled "_7.tiff" or otherwise
-    specified camera numbers.
-    
-    Parameters:
-        images (list): list of all tiff files, regardless of classification. This is NOT a list of directories but
-        of specific tiff files that can be opened right away. This is the list that we iterate through and 
-        divide.
-        
-        num (str): a string designation for the camera number that we want to extract i.e. "14" for double digits
-        of "_1" for single digits.
-        
-    Returns:
-        tiff (list): A list of tiff files that have the specified designation from num. They are the files extracted
-        from the 'images' list that correspond to the given num.
-    """
-    tiff = []
-    for im in images:
-        if im[-7:-5] == num:
-            tiff.append(im)
-    return tiff
 
 def predict_pix(tiff_image_path, difference = True):
     """
@@ -74,7 +66,7 @@ def predict_pix(tiff_image_path, difference = True):
     
     Return:
     image   ndarray(512 X 640): original image 
-    predict ndarray(325380,): predicted image excluding the boundary
+
     diff.   ndarray(325380,): IF difference = TRUE, difference between the min and max of four neighbors exclude the boundary
                             ELSE: the residuals of the four nearest pixels to a fitted hyperplane
     error   ndarray(325380,): difference between the original image and predicted image
@@ -140,6 +132,7 @@ class NodeTree(object):
         return self.left, self.right
 
     def __str__(self):
+        "Technically does not return a string, cannot be used with print"
         return self.left, self.right
 
 
@@ -188,15 +181,8 @@ def make_dictionary(tiff_image_path_list, num_bins=4, difference = True):
     num_bins            (int): number of bins
     
     Return:
-    huffman_encoding_list  list    (num_bins + 1): a list of dictionary
-    image_array            ndarray (512, 640): original image
-    new_error              ndarray (512, 640): error that includes the boundary
-    diff                   ndarray (510, 638): difference of min and max of the 4 neighbors
-    boundary               ndarray (2300,): the boundary values after subtracting the very first pixel value
-    predict                ndarray (325380,): the list of predicted values
+    huffman_encoding_list  list    (num_bins + 1): a list of dictionary, each dictionary is a huffman encoding
     bins                   list    (num_bins - 1,): a list of threshold to cut the bins
-    A                      ndarray (3 X 3): system of equation
-    
     """
     list_of_all_vals = []
     huffman_encoding_list = []
@@ -289,10 +275,6 @@ def huffman(tiff_image_path, num_bins=4, difference = True):
     # get the image_as_array, etc
     image_as_array, diff, error= predict_pix(tiff_image_path, difference)
     
-    # calculate the number of points that will go in each bin
-
-
-    # sort the difference and create the bins
     bins = [21,32,48]
     # get the boundary 
     boundary = np.hstack((image_as_array[0,:],image_as_array[-1,:],image_as_array[1:-1,0],image_as_array[1:-1,-1]))
@@ -530,7 +512,3 @@ for i in range(len(images)):
 #     encoded_string2 = bytes_to_bitstring(read_from_file(item))
 #     reconstruct_image = decoder(encoded_string2, list_dic, bins, False)
 #     print(np.allclose(image, reconstruct_image))
-print(np.mean(file_size_ratios))
-print(np.max(file_size_ratios))
-print(np.min(file_size_ratios))
-print(np.argmax(file_size_ratios))
\ No newline at end of file