A-5 緻密な濃淡表現を実現する「バークス型誤差拡散法」
img_src = cv2.imread("data/img_A-3.bmp")
img_dst = np.empty_like(img_src)
#座標の大きさ
eight = img_src.shape[0]
width = img_src.shape[1]
for y in range(0,eight):
for x in range(0,width):
img_dst[y,x] = img_src[y,x]
for y in range(0,eight):
for x in range(0,width):
temp = float(img_src[y,x,0])
if temp > 127:
e = temp - 255
else:
e = temp
if 2 <= x < width - 2 and 2 <= y < eight-1:
img_dst[y ,x+1,0] = float(img_dst[y,x+1,0]) + 0.25*e
img_dst[y ,x+2,0] = float(img_dst[y,x+2,0]) + 0.125*e
img_dst[y+1,x-2,0] = float(img_dst[y+1,x-2,0]) + 0.0625*e
img_dst[y+1,x-1,0] = float(img_dst[y+1,x-1,0]) + 0.25*e
img_dst[y+1,x ,0] = float(img_dst[y+1,x,0]) + 0.25*e
img_dst[y+1,x+1,0] = float(img_dst[y+1,x+1,0]) + 0.125*e
img_dst[y+1,x+2,0] = float(img_dst[y+1,x+2,0]) + 0.0625*e
for y in range(0,eight):
for x in range(0,width):
temp = img_dst[y,x,0]
img_dst[y,x] = [temp,temp,temp]
#描画する
cv2.imshow("img_src",img_src)
cv2.imshow("img_dst",img_dst)
cv2.waitKey(0)
cv2.destroyAllWindows()
#ヒストグラム
fig = plt.figure()
ax1 = fig.add_subplot(211) #総行数,総列数、サブプロット番号
ax2 = fig.add_subplot(212)
color_list = ["gray"]
ax2.set_ylim(0,5000)
for i,j in enumerate(color_list):
hist = cv2.calcHist([img_src],[i],None,[256],[0,256])
ax1.plot(hist,color = j)
color_list = ["gray"]
ax2.set_ylim(0,5000)
for i,j in enumerate(color_list):
hist = cv2.calcHist([img_dst],[i],None,[256],[0,256])
ax2.plot(hist,color = j)
今回もうまくいかない