From 11eb335a74706f69905748ed3e5139b77cf33220 Mon Sep 17 00:00:00 2001
From: Gasper Spagnolo <gasper.spagnolo@outlook.com>
Date: Thu, 20 Oct 2022 16:12:02 +0200
Subject: [PATCH] Implemented my hist with image type

---
 assignment1/solution.py | 29 +++++++++++++++--------------
 1 file changed, 15 insertions(+), 14 deletions(-)

diff --git a/assignment1/solution.py b/assignment1/solution.py
index 72c9fdf..286a117 100644
--- a/assignment1/solution.py
+++ b/assignment1/solution.py
@@ -9,12 +9,12 @@ from PIL import Image
 # EXCERCISE 1: Basic image processing #
 #######################################
 
-def excercise_one() -> None:
-    image = one_a()
-    #one_b(image)
-    #one_c(image)
-    #one_d(100, 200, 50, 200, image)
-    one_e()
+#def excercise_one() -> None:
+#    image = one_a()
+#    one_b(image)
+#    one_c(image)
+#    one_d(100, 200, 50, 200, image)
+#    one_e()
     
 def one_a() -> npt.NDArray[np.float64]:
     """
@@ -134,7 +134,7 @@ def excercise_two() -> None:
     """
     #two_a()
     two_b('./images/bird.jpg', 100, 20)
-    two_d()
+    #two_d()
     #two_e(uz.imread_gray("./images/bird.jpg", uz.ImageType.float64))
 
 
@@ -184,8 +184,12 @@ def my_hist_for_loop(image: npt.NDArray[np.float64], number_of_bins: int) -> npt
     return bins / np.sum(bins) 
 
 # Much faster implementation than for loop
-def my_hist(image: npt.NDArray[np.float64], number_of_bins: int) -> npt.NDArray[np.float64]:
-    bins = np.arange(0, 1, 1 / number_of_bins)
+def my_hist(image: npt.NDArray[np.float64], number_of_bins: int, img_typ: uz.ImageType) -> npt.NDArray[np.float64]:
+    if img_typ == uz.ImageType.float64: 
+        bins = np.arange(0, 1, 1 / number_of_bins)
+    elif img_typ == uz.ImageType.uint8:
+        bins = np.arange(0, 255, 255/number_of_bins)
+
     # Put pixels into classes
     # ex. binsize = 10 then 0.4 would map into 4
     binarray = np.digitize(image.reshape(-1), bins).astype(np.uint8)
@@ -226,9 +230,8 @@ def two_b(image_path: str, number_of_bins_first: int, number_of_bins_second: int
     """
     image = uz.imread_gray(image_path, uz.ImageType.uint8)
 
-
-    H1 = my_hist(image, number_of_bins_first)
-    H2 = my_hist(image, number_of_bins_second)
+    H1 = my_hist(image, number_of_bins_first, uz.ImageType.uint8)
+    H2 = my_hist(image, number_of_bins_second, uz.ImageType.uint8)
 
     fig, (ax0, ax1, ax2) = plt.subplots(1, 3)
     fig.suptitle("Birdie and histgrams")
@@ -337,8 +340,6 @@ def two_e(image: npt.NDArray[np.float64]) -> None:
         criterias.append( weight0 * var0 + weight1 * var1)
 
     best_threshold = treshold_range[np.argmin(criterias)]
-    print(best_threshold)
-    print("h")
 
 def main() -> None:
     #excercise_one()