def test_apply_multiple_filters_nochunk_sum_to_total_uneven(self):
img_list = []
lables_list = []
Total_entries = 101
i = 0
for i in range(Total_entries):
img_list.append(
Image.open("Unit_tests/Tiny_test_img/00002_00000.ppm"))
lables_list.append("yeet")
filters = {
"snow": premade_single_filter("snow"),
"rain": premade_single_filter("rain")
}
imgs = apply_multiple_filters((img_list, lables_list),
mode="linear",
KeepOriginal=False,
filters=filters,
chungus=0)
noises = [item[1] for item in imgs]
snow = sum([1 if "snow" == x else 0 for x in noises])
rain = sum([1 if "rain" == x else 0 for x in noises])
result = snow + rain
self.assertEqual(Total_entries, result)
def add_noise(imgs, noise_filter, image_size, chungus, keep_original):
pil_imgs = convert_between_pill_numpy(
imgs[0] * 255,
mode='numpy->pil') #converts numpy_img list to pill imges in a list
lables = [lable for lable in imgs[1]] #extracts the lables for the images
image_tuples = apply_multiple_filters(
(pil_imgs, lables),
filters=noise_filter,
mode='linear',
chungus=chungus,
KeepOriginal=keep_original
) #applies the diffrent noises to the images in a linear distribution, based on the number of filters inputet
RGB_img = [
changeImageSize(image_size[0], image_size[1], im[0].convert('RGB'))
for im in image_tuples
] #TODO find out what this line is used for other that its length. potentialy a useless computation
#numpy_imgs = convert_between_pill_numpy(RGB_img,mode='pil->numpy')
for i in range(len(RGB_img)):
image_tuples[i] = (
image_tuples[i][0], image_tuples[i][2], image_tuples[i][1]
) #rearranges the img tuple and overwrites the old tuple
return image_tuples
def add_noise(chungus, imgs: list, noise_filter: dict) -> list: #*DONE
return apply_multiple_filters(imgs,
filters=noise_filter,
mode='rand',
chungus=chungus,
KeepOriginal=True)