Letters color changed using numpy and image management

Text To Handwriting.py

@@ -1,43 +1,86 @@
-#Importing Library
-from PIL import Image
+# Importing Library
+from PIL import Image, ImageEnhance
 from sys import argv
+import numpy as np
+import re #regex
 
+# List of colors
+black = (0, 0, 0)
+red = (255, 0, 0)
+blue = (0, 0, 255)
+green = (0, 255, 0)
+purple = (142, 68, 173)
 
 # if you'd rather not use the command line, put the path to your file here
-fileName = "dummy.txt" # path of your text file
+fileName = "dummy.txt"  # path of your text file
 
-# read file that user wants converted from command line. If file can't be read, assign 
+color = black  # RGB Color, by default black
+
+
+# read file that user wants converted from command line. If file can't be read, assign
 # the file to a file in the directory
 try:
-    txt=open(argv[1], "r")
+    txt = open(argv[1], "r")
 except IndexError:
     print("No file entered. Using default file...")
-    txt=open(fileName, "r")
+    txt = open(fileName, "r")
 except FileNotFoundError:
     print("Could not find file. Using default file...")
-    txt=open(fileName, "r")   
+    txt = open(fileName, "r")
 
 
-BG=Image.open("myfont/bg.png") #path of page(background)photo (I have used blank page)
-sheet_width=BG.width
+# path of page(background)photo (I have used blank page)
+BG = Image.open("myfont/bg.png")
+sheet_width = BG.width
 gap, ht = 0, 0
 
-
 # for each letter in the uploaded txt file, read the unicode value and replace it with
 # the corresponding handwritten file in the "myfont" folder.
-for i in txt.read().replace("\n",""):
+lines = txt.readlines()
+for line in lines:
+    if re.search("txtColor=", line) != None: ##Check if flag color is set
+        colortmp = line.split("=")[1].rstrip("\n")
+        print(colortmp)
+        if colortmp == "red": color = red
+        if colortmp == "blue": color = blue
+        if colortmp == "green": color = green
+        if colortmp == "purple": color = purple
+        if colortmp == "black": color = black
+        continue
+    for i in line:
+        if(i == "\n"):
+            print("Newline")  # Accepting newline
+            gap, ht = 0, ht+140
+            continue
         cases = Image.open("myfont/{}.png".format(str(ord(i))))
+        cases = cases.convert("RGBA")
+
+        #Add contrast to the image to make letters darkers
+        enhancer = ImageEnhance.Contrast(cases)
+        factor = 5
+        im_output = enhancer.enhance(factor)
+        data = np.array(im_output)
+        red, green, blue, alpha = data.T
+
+        #Getting black pixels
+        black_areas = (red == 0) & (blue == 0) & (green == 0)
+        data[..., :-1][black_areas.T] = black #Make the pixels black
+        try:
+            data[..., :-1][black_areas.T] = color #Change color of letter
+        except:
+            print("No change of color")
+        cases = Image.fromarray(data)
+
         BG.paste(cases, (gap, ht))
         size = cases.width
-        height=cases.height
-        #print(size)
+        height = cases.height
+        # print(size)
         print("Running...........")
-        gap+=size
+        gap += size
 
-        if sheet_width < gap or len(i)*115 >(sheet_width-gap):
-            gap,ht=0,ht+140
+        if sheet_width < gap or len(i)*115 > (sheet_width-gap):
+            gap, ht = 0, ht+140
 
 print(gap)
 print(sheet_width)
 BG.show()
-

dummy.txt

@@ -1,7 +1,7 @@
-Deep learning is an artificial intelligence function that imitates the workings of the
-human brain in processing data and creating patterns for use in decision making.
- Deep learning is a subset of machine learning in artificial intelligence (AI) that has
- networks capable of learning unsupervised from data that is unstructured or unlabeled.
- Also known as deep neural learning or deep neural network.
+txtColor=red
+REPORT 1. LAB
 
+txtColor=purple
+Swarm intelligence )SI( is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.
 
+SI systems consist typically of a population of simple agents or boids interacting locally with one another and with their environment. The inspiration often comes from nature, especially biological systems. The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of intelligent global behavior, unknown to the individual agents.