def convert_color_space_from_rgb(
np_image: np.ndarray, dest_color_space: Optional[str]) -> np.ndarray:
if dest_color_space is None or dest_color_space == "RGB":
return np_image
elif dest_color_space == "HSV":
return colour.RGB_to_HSV(np_image)
elif dest_color_space == "HSL":
return colour.RGB_to_HSL(np_image)
elif dest_color_space == "CMY":
return colour.RGB_to_CMY(np_image)
else:
raise ValueError("Undefined color space.")
def find_most_saturated(im, x, y, search_size):
if search_size < 1:
r, g, b = im.getpixel((x, y))
return (x, y, r, g, b)
highest_s = -1.0
highest = (-1, -1, 0, 0, 0)
for xp in range(x - search_size, x + search_size + 1):
for yp in range(y - search_size, y + search_size + 1):
r, g, b = im.getpixel((xp, yp))
rgb = np.array([r / 255.0, g / 255.0, b / 255.0])
s = colour.RGB_to_HSV(rgb)[1]
if s > highest_s:
highest_s = s
highest = (xp, yp, r, g, b)
return highest
def color_convert_hsv(RGB_color):
arr_RGB_color = np.array(RGB_color)
list_rgb_color = arr_RGB_color.tolist()
float_arr_RGB_color = arr_RGB_color / 255
float_tp_RGB_color = tuple(float_arr_RGB_color)
HSV_color = colour.RGB_to_HSV(float_tp_RGB_color)
HSV_color2 = np.array([
round(HSV_color[0] * 359, 3),
round(HSV_color[1] * 100, 3) - 4,
round(HSV_color[2] * 100, 3) + 8
])
# print("HSV 값 : ", HSV_color2)
HSV_color2 = list(HSV_color2)
HSV_color2[0] = round(HSV_color2[0], 2)
HSV_color2[1] = round(HSV_color2[1], 2)
HSV_color2[2] = round(HSV_color2[2], 2)
return HSV_color2
imgInputPath = "outimg/FinalResult/BlendImage/"
enhancedImgInputpath = "outimg/FinalResult/EnhancedImage/"
imgOutputPath = "outimg/FinalResult/FinalSignalImage/"
imgFileName = sys.argv[1]
enhancedFileName = sys.argv[2]
#ブレンドした画像の読み込み
RGB, imgH, imgW = getImageRGBFromPath(imgInputPath + imgFileName + ".jpg")
inputRGB = np.copy(RGB)
maskRGB = np.copy(RGB)
analyzedRGB = np.copy(RGB)
enhancedRGB, w, h = getImageRGBFromPath(enhancedImgInputpath +
enhancedFileName + ".jpg")
HSV = colour.RGB_to_HSV(enhancedRGB)
#低彩度値の画素を抽出
achromaticWhitePixelBool = np.where((HSV[:, 2] >= 0.86) & (HSV[:, 1] <= 0.27))
achromaticBlackPixelBool = np.where(HSV[:, 2] <= 0.25)
#対象領域の着色
maskRGB[achromaticWhitePixelBool] = np.array([1, 1, 0])
maskRGB[achromaticBlackPixelBool] = np.array([1, 0, 0])
#無彩色変換
analyzedRGB[achromaticWhitePixelBool] = HSV[achromaticWhitePixelBool,
2].reshape(
(-1,
1)) * np.transpose(Wtarget)
analyzedRGB[achromaticBlackPixelBool] = HSV[achromaticBlackPixelBool,
Showcases deprecated colour models computations.
"""
import numpy as np
import colour
from colour.utilities import message_box
message_box(('Deprecated Colour Models Computations\n'
'\nDon\'t use that! Seriously...'))
RGB = np.array([0.49019608, 0.98039216, 0.25098039])
message_box(('Converting to "HSV" colourspace from given "RGB" colourspace '
'values:\n'
'\n\t{0}'.format(RGB)))
print(colour.RGB_to_HSV(RGB))
print('\n')
HSV = np.array([0.27867384, 0.74400000, 0.98039216])
message_box(('Converting to "RGB" colourspace from given "HSV" colourspace '
'values:\n'
'\n\t{0}'.format(HSV)))
print(colour.HSV_to_RGB(HSV))
print('\n')
message_box(('Converting to "HSL" colourspace from given "RGB" colourspace '
'values:\n'
'\n\t{0}'.format(RGB)))
print(colour.RGB_to_HSL(RGB))
def initialize(self):
# fig_size = plt.rcParams["figure.figsize"]
# fig_size[0] = 14
# fig_size[1] = 5
# plt.rcParams["figure.figsize"] = fig_size
f = open('gt.txt', 'r')
cc = []
for video_id in range(1, 101):
print(video_id)
image = cv2.cvtColor(
Image.load(self.video_path + '/' + str(video_id) +
'/average10.jpg'), cv2.COLOR_BGR2RGB)
image = image[:int(image.shape[0] / 2), :]
w = image.shape[1] // 5
h = image.shape[0] // 5
image = cv2.resize(image, (w, h))
hsvIm = np.array(image)
for i in range(0, image.shape[0]):
for j in range(0, image.shape[1]):
rgb = image[i][j] / 255.0
hsv = colour.RGB_to_HSV(rgb)
hsv = [
int(hsv[0] * 360),
int(hsv[1] * 255),
int(hsv[2] * 255)
]
hsvIm[i][j] = hsv
hBin = 360
vBin = 255
#hHist, hX = np.histogram([hsvIm[x][y][0] for x in range(0, h) for y in range(0, w) if hsvIm[x][y][1] < 125], hBin, (0, 360))
hHist, hX = np.histogram(hsvIm[:, :, 0].flatten(), hBin, (0, 360))
vHist, vX = np.histogram(hsvIm[:, :, 2].flatten(), vBin, (0, 255))
nH = (np.sum(hHist[0:72]) + np.sum(hHist[288:])) / (h * w)
nV = np.sum(vHist[124:]) / (h * w)
print(nH, nV)
if nV < 0.289:
result = 'night'
else:
result = 'day'
gt = f.readline()[:-1]
print(result, gt)
#result = gt
cc.append((nH, nV, gt))
# if nH < 0.654 and nH > 0.04:
# if nV > 0.126 and nV < 0.197:
# result = 'day'
# else:
# result = 'night'
# else:
# if nV > 0.108:
# if nH < 0.57:
# result = 'day'
# else:
# result = 'night'
# else:
# result = 'night'
# if (result != gt):
# fig, (ax1, ax2, ax3) = plt.subplots(1, 3)
# ax1.plot(hX[:-1], hHist, color='r')
# #ax1.plot(_[:-1], sHist, color='g')
# ax2.plot(vX[:-1], vHist, color='b')
# ax3.set_title(result)
# ax3.imshow(image)
# plt.show()
dayList = [x for x in cc if (x[2] == 'day')]
nightList = [x for x in cc if (x[2] == 'night')]
plt.scatter([x[0] for x in dayList], [x[1] for x in dayList], c='r')
plt.scatter([x[0] for x in nightList], [x[1] for x in nightList],
c='b')
plt.show()
path = "outimg/continuity_hue/Natural/" + imgName + "_" + str(it) + ".jpg"
inputImg = cv2.imread(path, cv2.IMREAD_COLOR)
#正規化と整形
inputImg = cv2.cvtColor(inputImg, cv2.COLOR_BGR2RGB) / 255.
rgb = np.reshape(inputImg, (inputImg.shape[0] * inputImg.shape[1], 3))
#初期画像の保存
if (it == 0):
initialRGB = rgb
#色空間の変換
if COLOR_SPACE is "RGB":
pixel = rgb
elif COLOR_SPACE is "HSV":
pixel = colour.RGB_to_HSV(rgb)
elif COLOR_SPACE is "HSL":
pixel = colour.RGB_to_HSL(rgb)
aveGrads[it] = getAveGrad(pixel)
entropys[it] = getEntropy(pixel)
moment, cov = getColorMoment(pixel)
moments[it] = moment
colorDist[it] = getDistance(initialRGB, rgb)
measures[it] = getImageMeasure(initialRGB, rgb)
SatMeasures[it] = getSaturationMeasure(rgb)
colorfulness[it] = getColourFulness(rgb)
naturalness[it] = getNaturalness(rgb)
contrast[it] = getContasrtMeasure(rgb)
brightness[it] = getBrightnessMeasure(rgb)
def color_convert(cheek, file_name):
img = cheek
img = cv.cvtColor(img, cv.COLOR_BGR2RGB)
# plt.imshow(img)
# plt.show()
sum = 0
R = []
G = []
B = []
for i in img:
for j in i:
R.append(j[0])
G.append(j[1])
B.append(j[2])
R_sum = 0
G_sum = 0
B_sum = 0
# 각 R, G, B의 합계 구하기
for i in range(len(R)):
R_sum += R[i]
G_sum += G[i]
B_sum += B[i]
R_avg = int(round((R_sum / len(R)), 0)) # R값 평균
G_avg = int(round((G_sum / len(G)), 0)) # G값 평균
B_avg = int(round((B_sum / len(B)), 0)) # B값 평균
RGB_color = [R_avg, G_avg, B_avg]
# 평균 색만 그래프 그리기 위함 img_avg
img_avg = img
for i in img_avg:
for j in i:
j[0] = R_avg
j[1] = G_avg
j[2] = B_avg
# 기존
# plt.imshow(img)
# plt.show()
# 평균색
# plt.imshow(img_avg)
# plt.show()
bgr_img_avg = cv.cvtColor(img_avg, cv.COLOR_RGB2BGR)
# 저장
# cv.imwrite("../img/"+file_name+"_9.jpg", bgr_img_avg)
arr_RGB_color = np.array(RGB_color)
float_arr_RGB_color = arr_RGB_color / 255
float_tp_RGB_color = tuple(float_arr_RGB_color)
HSV_color = colour.RGB_to_HSV(float_tp_RGB_color)
HSV_color2 = np.array([
round(HSV_color[0] * 359, 3),
round(HSV_color[1] * 100, 3) - 4,
round(HSV_color[2] * 100, 3) + 8
])
HSV_color2 = list(HSV_color2)
HSV_color2[0] = round(HSV_color2[0], 2)
HSV_color2[1] = round(HSV_color2[1], 2)
HSV_color2[2] = round(HSV_color2[2], 2)
return HSV_color2
