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Second ex refactored

main
Gasper Spagnolo 2022-10-22 17:35:47 +02:00
parent 37c80f45cf
commit 6ced34f623
2 changed files with 111 additions and 151 deletions
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196
assignment1/solution.py
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@ -12,9 +12,9 @@ import UZ_utils as uz
def excercise_one() -> None:
image = one_a()
#one_b(image)
#one_c(image)
#one_d(100, 200, 200, 400, image)
one_b(image)
one_c(image)
one_d(100, 200, 200, 400, image)
one_e()
def one_a() -> npt.NDArray[np.float64]:
@ -143,10 +143,10 @@ 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_c('./images/bird.jpg', 20, 100)
#two_d()
#two_e(uz.imread_gray('./images/bird.jpg', uz.ImageType.uint8).astype(np.uint8))
two_b('./images/bird.jpg', 100, 20)
two_c('./images/bird.jpg', 20, 100)
two_d()
two_e(uz.imread_gray('./images/bird.jpg', uz.ImageType.uint8).astype(np.uint8))
def two_a() -> tuple[npt.NDArray[np.float64], npt.NDArray[np.uint8]]:
@ -180,48 +180,6 @@ def two_a() -> tuple[npt.NDArray[np.float64], npt.NDArray[np.uint8]]:
plt.show()
return (image, binary_mask)
def my_hist_for_loop(image: npt.NDArray[np.float64], number_of_bins: int) -> npt.NDArray[np.float64]:
bin_restrictions = np.arange(0, 1, 1 / number_of_bins)
bins = np.zeros(number_of_bins).astype(np.float64)
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
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)
# Now count those values
binarray = np.unique(binarray, return_counts=True)
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:
"""
Write a function myhist that accepts a grayscale image and the number of bins that
@ -239,20 +197,20 @@ def two_b(image_path: str, number_of_bins_first: int, number_of_bins_second: int
sum of all cells. Why is that?
Answer:
"""
image = uz.imread_gray(image_path, uz.ImageType.uint8)
image = uz_image.imread_gray(image_path, uz_image.ImageType.uint8)
H1 = my_hist(image, number_of_bins_first, uz.ImageType.uint8)
H2 = my_hist(image, number_of_bins_second, uz.ImageType.uint8)
H1 = uz_image.get_image_bins(image, number_of_bins_first)
H2 = uz_image.get_image_bins(image, number_of_bins_second)
fig, (ax0, ax1, ax2) = plt.subplots(1, 3)
fig.suptitle("Birdie and histgrams")
fig, axs = plt.subplots(1, 3)
fig.suptitle("Birdie and histograms")
ax0.imshow(image, cmap="gray")
ax0.set(title="Birdie image")
ax1.bar(np.arange(number_of_bins_first), H1)
ax1.set(title="100 bins")
ax2.bar(np.arange(number_of_bins_second), H2)
ax2.set(title="20 bins")
axs[0].imshow(image, cmap="gray")
axs[0].set(title="Birdie image")
axs[1].bar(np.arange(number_of_bins_first), H1)
axs[1].set(title="100 bins")
axs[2].bar(np.arange(number_of_bins_second), H2)
axs[2].set(title="20 bins")
plt.show()
@ -264,14 +222,14 @@ def two_c(image_path: str, number_of_bins_first: int, number_of_bins_second: int
difference between both versions of the function.
"""
image_uint8 = uz.imread_gray(image_path, uz.ImageType.uint8)
image_float64 = uz.imread_gray(image_path, uz.ImageType.float64)
image_uint8 = uz_image.imread_gray(image_path, uz_image.ImageType.uint8)
image_float64 = uz_image.imread_gray(image_path, uz_image.ImageType.float64)
H01 = my_hist(image_uint8, number_of_bins_first, uz.ImageType.uint8)
H02 = my_hist(image_uint8, number_of_bins_second, uz.ImageType.uint8)
H01 = uz_image.get_image_bins(image_uint8, number_of_bins_first)
H02 = uz_image.get_image_bins(image_uint8, number_of_bins_second)
H11 = my_hist(image_float64, number_of_bins_first, uz.ImageType.float64)
H12 = my_hist(image_float64, number_of_bins_second, uz.ImageType.float64)
H11 = uz_image.get_image_bins(image_float64, number_of_bins_first)
H12 = uz_image.get_image_bins(image_float64, number_of_bins_second)
fig, axs = plt.subplots(2, 3)
fig.suptitle("Comparison between two histograms")
@ -283,7 +241,7 @@ def two_c(image_path: str, number_of_bins_first: int, number_of_bins_second: int
axs[0, 2].bar(np.arange(number_of_bins_second), H02)
axs[0, 2].set(title=f'{number_of_bins_second} bins used')
axs[1, 0].imshow(image_float64, cmap="gray")
axs[1, 0].imshow(image_uint8, cmap="gray")
axs[1, 0].set(title="Grayscale image in uint8 representation")
axs[1, 1].bar(np.arange(number_of_bins_first), H11)
axs[1, 1].set(title=f'{number_of_bins_first} bins used')
@ -298,50 +256,27 @@ def two_d() -> None:
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", uz.ImageType.float64)
darker = uz.imread_gray("./images/ONE_ROOM_LIGH_ON.jpg", uz.ImageType.float64)
dark = uz.imread_gray("./images/DARK.jpg", uz.ImageType.float64)
imgs = []
bins = [20, 60, 100]
imgs.append(uz_image.imread_gray("./images/ROOM_LIGHTS_ON.jpg", uz_image.ImageType.float64)) # light
imgs.append(uz_image.imread_gray("./images/ONE_ROOM_LIGH_ON.jpg", uz_image.ImageType.float64)) # darker
imgs.append(uz_image.imread_gray("./images/DARK.jpg", uz_image.ImageType.float64)) # dark
H10 = my_hist(light, 20, uz.ImageType.float64)
H11 = my_hist(light, 60, uz.ImageType.float64)
H12 = my_hist(light, 100, uz.ImageType.float64)
H20 = my_hist(darker, 20, uz.ImageType.float64)
H21 = my_hist(darker, 60, uz.ImageType.float64)
H22 = my_hist(darker, 100, uz.ImageType.float64)
H30 = my_hist(dark, 20, uz.ImageType.float64)
H31 = my_hist(dark, 60, uz.ImageType.float64)
H32 = my_hist(dark, 100, uz.ImageType.float64)
fig, axs = plt.subplots(3, 4)
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(20), H10)
axs[0, 1].set(title="Using 20 bins")
axs[0, 2].bar(np.arange(60), H11)
axs[0, 2].set(title="Using 60 bins")
axs[0, 3].bar(np.arange(100), H12)
axs[0, 3].set(title="Using 100 bins")
axs[1, 0].imshow(darker, cmap="gray")
axs[1, 0].set(title="Image in darker conditions")
axs[1, 1].bar(np.arange(20), H20)
axs[1, 1].set(title="Using 20 bins")
axs[1, 2].bar(np.arange(60), H21)
axs[1, 2].set(title="Using 60 bins")
axs[1, 3].bar(np.arange(100), H22)
axs[1, 3].set(title="Using 100 bins")
axs[2, 0].imshow(dark, cmap="gray")
axs[2, 0].set(title="Image in dark conditions")
axs[2, 1].bar(np.arange(20), H30)
axs[2, 1].set(title="Using 20 bins")
axs[2, 2].bar(np.arange(60), H31)
axs[2, 2].set(title="Using 60 bins")
axs[2, 3].bar(np.arange(100), H32)
axs[2, 3].set(title="Using 100 bins")
fig.suptitle("Me and my histograms")
for i in range(3):
for j in range(3):
axs[i, j+1].bar(np.arange(bins[j]), uz_image.get_image_bins(imgs[i], bins[j]))
axs[i, j+1].set(title=f"Using {bins[j]} bins")
axs[0, 0].imshow(imgs[0], cmap="gray")
axs[0, 0].set(title="Image in light conditions")
axs[1, 0].imshow(imgs[1], cmap="gray")
axs[1, 0].set(title="Image in darker conditions")
axs[2, 0].imshow(imgs[2], cmap="gray")
axs[2, 0].set(title="Image in dark conditions")
plt.show()
def two_e(image: npt.NDArray[np.uint8]):
@ -352,39 +287,7 @@ def two_e(image: npt.NDArray[np.uint8]):
shows the algorithm’s results on different images.
References: https://en.wikipedia.org/wiki/Otsu%27s_method
"""
treshold_range = np.arange(np.max(image) + 1)
criterias = []
for treshold in treshold_range:
# create the thresholded image
thresholded_im = np.zeros(image.shape)
thresholded_im[image >= treshold] = 1
# compute weights
nb_pixels = image.size
nb_pixels1 = np.count_nonzero(thresholded_im)
weight1 = nb_pixels1 / nb_pixels
weight0 = 1 - weight1
# if one the classes is empty, eg all pixels are below or above the threshold, that threshold will not be considered
# in the search for the best threshold
if weight1 == 0 or weight0 == 0:
continue
# find all pixels belonging to each class
val_pixels1 = image[thresholded_im == 1]
val_pixels0 = image[thresholded_im == 0]
# compute variance of these classes
var0 = np.var(val_pixels0) if len(val_pixels0) > 0 else 0
var1 = np.var(val_pixels1) if len(val_pixels1) > 0 else 0
criterias.append( weight0 * var0 + weight1 * var1)
best_threshold = treshold_range[np.argmin(criterias)]
print(f'best treshold is: {best_threshold}')
return best_threshold
return uz_image.calculate_best_treshold_using_otsu_method(image)
######################################################
@ -392,7 +295,7 @@ def two_e(image: npt.NDArray[np.uint8]):
######################################################
def excercise_three() -> None:
#three_a()
three_a()
#mask1, _ = three_b()
#three_c(uz.imread('./images/bird.jpg', uz.ImageType.float64), mask1)
#three_d()
@ -524,9 +427,12 @@ def three_d():
TRESHOLD = two_e(eagle_img_gray)
binary_mask = eagle_img_gray.copy()
binary_mask = np.where(binary_mask < TRESHOLD, 0, 1)
binary_mask = np.where(binary_mask < TRESHOLD, 0, 1)
binary_mask = uz.convert_float64_array_to_uint8_array(binary_mask)
plt.imshow(binary_mask, cmap='gray')
plt.show()
# If I would invert image here, then we would get crap
# So workaround:
SE_CROSS = cv2.getStructuringElement(cv2.MORPH_CROSS, (2, 2))
@ -582,9 +488,9 @@ def three_e():
sizes = stats[:, -1]
for blob in range(n_blobs):
if sizes[blob] > 700:
if sizes[blob] > COIN_SIZE:
binary_mask[im_with_separated_blobs == blob] = 0
three_c(coin_img_color, binary_mask)

66
assignment1/uz_framework/image.py
View File

@ -11,7 +11,7 @@ class ImageType(enum.Enum):
uint8 = 0
float64 = 1
def imread(path: str, type: ImageType) -> npt.NDArray[np.float64] or npt.NDArray[np.uint8]:
def imread(path: str, type: ImageType) -> Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]]:
"""
Reads an image in RGB order. Image type is transformed from uint8 to float, and
range of values is reduced from [0, 255] to [0, 1].
@ -24,10 +24,10 @@ def imread(path: str, type: ImageType) -> npt.NDArray[np.float64] or npt.NDArray
elif type == ImageType.uint8:
return I
raise Exception("Unrecognized image format!")
raise Exception(f"Unrecognized image format! {type}")
def imread_gray(path: str, type: ImageType) -> npt.NDArray[np.float64] or npt.NDArray[np.uint8]:
def imread_gray(path: str, type: ImageType) -> Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]]:
"""
Reads an image in gray. Image type is transformed from uint8 to float, and
range of values is reduced from [0, 255] to [0, 1].
@ -85,7 +85,7 @@ def transform_coloured_image_to_grayscale(image: npt.NDArray[np.float64]) -> npt
return grayscale_image
def invert_coloured_image_part(image: npt.NDArray[np.float64] or npt.NDArray[np.uint8], startx: int, endx: int, starty: int, endy: int) -> npt.NDArray[np.float64] or npt.NDArray[np.uint8]:
def invert_coloured_image_part(image: Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]], startx: int, endx: int, starty: int, endy: int) -> Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]]:
"""
Accepts image, starting position end end position for axes x & y. Returns whole image with inverted part.
"""
@ -108,13 +108,13 @@ def invert_coloured_image_part(image: npt.NDArray[np.float64] or npt.NDArray[np.
raise Exception("Unrecognized image format!")
def invert_coloured_image(image: npt.NDArray[np.float64] or npt.NDArray[np.uint8]) -> npt.NDArray[np.float64] or npt.NDArray[np.uint8]:
def invert_coloured_image(image: Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]]) -> Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]]:
"""
Accepts image and inverts it
"""
return invert_coloured_image_part(image, 0, image.shape[0], 0, image.shape[1])
def calculate_best_treshold_using_otsu_method(image: npt.NDArray[np.float64] or npt.NDArray[np.uint8]) -> int:
def calculate_best_treshold_using_otsu_method(image: Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]]) -> int:
"""
Accepts image and returns best treshold using otsu method
"""
@ -159,3 +159,57 @@ def calculate_best_treshold_using_otsu_method(image: npt.NDArray[np.float64] or
best_threshold = treshold_range[np.argmin(criterias)]
return best_threshold
def get_image_bins_for_loop(image: Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]], number_of_bins: int) -> npt.NDArray[np.float64]:
"""
Accepts image in the float64 format or uint8, returns normailzed
image bins, histogram
"""
if image.dtype.type == np.float64 or image.dtype.type == np.uint8:
bin_restrictions = np.linspace(np.min(image), np.max(image), num=number_of_bins)
else:
raise Exception("Unrecognized image format!")
bins = np.zeros(number_of_bins).astype(np.float64)
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
def get_image_bins(image: Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]], number_of_bins: int) -> npt.NDArray[np.float64]:
"""
Accepts image in the float64 format or uint8, returns normailzed
image bins, histogram
"""
if image.dtype.type == np.float64 or image.dtype.type == np.uint8:
bins = np.linspace(np.min(image), np.max(image), num=number_of_bins)
else:
raise Exception("Unrecognized image format!")
# 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)
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)