Gremoooooooo

assignment1/solution.py

@@ -132,7 +132,9 @@ def excercise_two() -> None:
     pixels in the source image is greater or lower than the given threshold.
     """
     #two_a()
-    two_b('./images/bird.jpg', 100, 20)
+    #two_b('./images/bird.jpg', 100, 20)
+    two_d()
+
 
 def two_a() -> tuple[npt.NDArray[np.float64], npt.NDArray[np.uint8]]:
     """
@@ -176,6 +178,7 @@ def my_hist_for_loop(image: npt.NDArray[np.float64], number_of_bins: int) -> npt
     for pixel in image.reshape(-1):
         # https://stackoverflow.com/a/16244044
         bins[np.argmax(bin_restrictions > pixel)] += 1
+
     return bins / np.sum(bins) 
 
 # Much faster implementation than for loop
@@ -184,10 +187,25 @@ def my_hist(image: npt.NDArray[np.float64], number_of_bins: int) -> npt.NDArray[
     # Put pixels into classes
     # ex. binsize = 10 then 0.4 would map into 4
     binarray = np.digitize(image.reshape(-1), bins).astype(np.uint8)
+
     # Now count those values 
     binarray = np.unique(binarray, return_counts=True)
-    binarray = binarray[1].astype(np.float64) # Get the counts out of tuple
+
-    return binarray / np.sum(binarray)
+    counts = binarray[1].astype(np.float64) # Get the counts out of tuple
+
+    # Check if there is any empty bin
+    empty_bins = []
+    bins = binarray[0]
+    for i in range(1, number_of_bins + 1):
+        if i not in bins:
+            empty_bins.append(i)
+    
+    # Add empty bins with zeros
+    if empty_bins != []:
+        for i in empty_bins:
+            counts = np.insert(counts, i - 1, 0)
+
+    return counts / np.sum(counts)
     
 
 def two_b(image_path: str, number_of_bins_first: int, number_of_bins_second: int) -> None:
@@ -221,7 +239,41 @@ def two_b(image_path: str, number_of_bins_first: int, number_of_bins_second: int
     ax2.set(title="20 bins")
 
     plt.show()
- 
+
+def two_c():
+    assert("to be implemented", "to be implemented")
+
+def two_d():
+    """
+    Test myhist function on images (three or more) of the same scene in
+    different lighting conditions. One way to do this is to capture several images using
+    your web camera and change the lighting of the room. Visualize the histograms for
+    all images for different number of bins and interpret the results.
+    """
+    light = uz.imread_gray("./images/ROOM_LIGHTS_ON.jpg")
+    darker = uz.imread_gray("./images/ONE_ROOM_LIGH_ON.jpg")
+    dark = uz.imread_gray("./images/DARK.jpg")
+
+    H1 = my_hist(light, 100) 
+    H2 = my_hist(darker, 100)
+    H3 = my_hist(dark, 100)
+    
+    fig, axs = plt.subplots(3, 2)
+    fig.suptitle("spanskiduh and histgrams")
+
+    axs[0, 0].imshow(light, cmap="gray") 
+    axs[0, 0].set(title="Image in light conditions")
+    axs[0, 1].bar(np.arange(100), H1)
+
+    axs[1, 0].imshow(darker, cmap="gray") 
+    axs[1, 0].set(title="Image in darker conditions")
+    axs[1, 1].bar(np.arange(100), H2)
+
+    axs[2, 0].imshow(dark, cmap="gray") 
+    axs[2, 0].set(title="Image in dark conditions")
+    axs[2, 1].bar(np.arange(100), H3)
+    
+    plt.show()
 
 def main() -> None:
     #excercise_one()