def CMOStoTwoColor(laser_power, fg, coefs, save_path, threshold=20):
"""
CMOSカメラ画像→温度配列→二色温度計画像
Parameters
----------
laser_power : int
レーザ出力
fg : string
フィルタとゲインの組み合わせ
coefs : numpy
重回帰式の係数
save_path : string
保存する画像の絶対パス
Returns
-------
"""
height = 300
width = 300
if fg == 'f4-g16':
dir_name = "/Users/paix/Desktop/Python_lab/frame_data/20210706/after_projection/f4-g16/"
mid_frames = [28, 41, 31, 39, 36]
elif fg == 'f4-g24':
dir_name = "/Users/paix/Desktop/Python_lab/frame_data/20210706/after_projection/f4-g24/"
mid_frames = [32, 38, 32, 26, 57]
mid_frame = mid_frames[int((laser_power - 1400) / 100)]
img_CMOS = np.array(
Image.open(dir_name + str(laser_power) + '/img_' + str(mid_frame) +
'.bmp'))
x, y = gp.get_gravitypoint_CMOS(img_CMOS,
show_gp_img=False,
print_gp=False)
trimmed_img_r = img_CMOS[y - int(height / 2):y + int(height / 2),
x - int(width / 2):x + int(width / 2), 0]
trimmed_img_g = img_CMOS[y - int(height / 2):y + int(height / 2),
x - int(width / 2):x + int(width / 2), 1]
trimmed_img_b = img_CMOS[y - int(height / 2):y + int(height / 2),
x - int(width / 2):x + int(width / 2), 2]
img_zero = np.zeros(height * width)
img_red = trimmed_img_r[:, :].reshape(height * width)
ret, img_thresh = cv2.threshold(img_red, threshold, 255, cv2.THRESH_BINARY)
trimmed_img_r = trimmed_img_r.reshape(height * width)
trimmed_img_g = trimmed_img_g.reshape(height * width)
trimmed_img_b = trimmed_img_b.reshape(height * width)
for i in img_thresh.nonzero():
img_zero[i] += trimmed_img_r[i] * coefs[0] + trimmed_img_g[i] * coefs[
1] + trimmed_img_b[i] * coefs[2] + coefs[3]
# ic(img_zero)
twocolor_CMOS = tc.tocolor(img_zero, height,
width).reshape(height, width, 3)
# ic(twocolor_CMOS)
img_ave = np.array(twocolor_CMOS, dtype=np.int8)
pil_img = Image.fromarray(img_ave, mode="RGB")
# save_name = str(laser_power) + '_' + str(gain) + '_' + str(average) + '_' + nonzero + "_RGB" + ".bmp"
pil_img.save(save_path)
for laser_power in range(1400, 1900, 100):
for gain in [16, 24]:
for average in range(10, 60, 10):
for nonzero in ['nonzero', 'haszero']:
if gain == 16:
dir_name = "/Users/paix/Desktop/Python_lab/frame_data/20210706/after_projection/f4-g16/"
mid_frames = [28, 41, 31, 39, 36]
elif gain == 24:
dir_name = "/Users/paix/Desktop/Python_lab/frame_data/20210706/after_projection/f4-g24/"
mid_frames = [32, 38, 32, 26, 57]
mid_frame = mid_frames[int((laser_power - 1400) / 100)]
img_CMOS = np.array(
Image.open(dir_name + str(laser_power) + '/img_' +
str(mid_frame) + '.bmp'))
x, y = gp.get_gravitypoint_CMOS(img_CMOS,
show_gp_img=False)
trimmed_img_r = img_CMOS[y - int(height / 2):y +
int(height / 2), x -
int(width / 2):x + int(width / 2),
0]
trimmed_img_g = img_CMOS[y - int(height / 2):y +
int(height / 2), x -
int(width / 2):x + int(width / 2),
1]
trimmed_img_b = img_CMOS[y - int(height / 2):y +
int(height / 2), x -
int(width / 2):x + int(width / 2),
2]
img_zero = np.zeros(height * width)
laser_power = int(input())
height = 300
width = 300
print('after / before projection?')
afbf = input()
print('Average range')
average = int(input())
dir_name = "/Users/paix/Desktop/Python_lab/frame_data/20210421/CMOS/" + afbf + "_projection/"
if laser_power == 1400:
img_CMOS = np.array(Image.open(dir_name + '1400/img_72.bmp'))
elif laser_power == 1600:
img_CMOS = np.array(Image.open(dir_name + '1600/img_86.bmp'))
elif laser_power == 1800:
img_CMOS = np.array(Image.open(dir_name + '1800/img_95.bmp'))
# img_CMOS = np.array(Image.open('/Users/paix/Desktop/Python_lab/dstName.bmp'))
x,y = gp.get_gravitypoint_CMOS(img_CMOS)
trimmed_img_r = img_CMOS[ y - int(height/2) : y + int(height/2) , x - int(width/2) : x + int(width/2), 0]
trimmed_img_g = img_CMOS[ y - int(height/2) : y + int(height/2) , x - int(width/2) : x + int(width/2), 1]
trimmed_img_b = img_CMOS[ y - int(height/2) : y + int(height/2) , x - int(width/2) : x + int(width/2), 2]
img_zero = np.zeros(height*width)
threshold = 20
img_red = trimmed_img_r[:,:].reshape(height*width)
ret, img_thresh = cv2.threshold(img_red, threshold, 255, cv2.THRESH_BINARY)
trimmed_img_r = trimmed_img_r.reshape(height*width)
trimmed_img_g = trimmed_img_g.reshape(height*width)
trimmed_img_b = trimmed_img_b.reshape(height*width)
for i in img_thresh.nonzero():
if average == 50:
img_zero[i] += trimmed_img_r[i]*12.08469623 + trimmed_img_g[i]*0.38873001 + trimmed_img_b[i]*6.39120529 + 649.11480838
def calibration(fg,
TEMP,
haszero,
average,
samplenumber,
dir_name,
mid_frames,
laser_power=1600,
RGBmin=25,
RGBmax=255):
"""
二色温度計画像とCMOSカメラ画像の比較をする
その結果をcsvファイルに出力する
Parameters
----------
laser_power : int
レーザ出力
fg : string
フィルタとゲインの組み合わせ
TEMP : numpyの二次元配列
二色温度計の時間平均値
haszero : bool
温度とRGBの対応データに0を含めるかどうか
average : int
平均する温度範囲
samplenumber : int
データとして扱うサンプルの数の閾値
dir_name : str
溶融地画像があるディレクトリ
mid_frames : numpy
溶融地の真ん中のフレーム
Returns
-------
csv_path : string
結果を出力したcsvファイルの絶対パス
"""
height, width = TEMP.shape
# キャリブレーション
mid_frame = mid_frames[int((laser_power - 1400) / 100)]
img_CMOS = np.array(
Image.open(dir_name + str(laser_power) + '/img_' + str(mid_frame) +
'.bmp'))
x, y = gp.get_gravitypoint_CMOS(img_CMOS,
show_gp_img=False,
print_gp=False)
trimmed_img = img_CMOS[y - 45:y + 45, x - 60:x + 60]
# import cv2
# cv2.imshow('img', trimmed_img)
# cv2.waitKey(0)
CMOS_r = trimmed_img[:, :, 0]
CMOS_g = trimmed_img[:, :, 1]
CMOS_b = trimmed_img[:, :, 2]
# CMOS = trimmed_img[:, :, RGBvalue]
TEMP = TEMP.reshape(90 * 120)
CMOS_r = CMOS_r.reshape(90 * 120)[np.nonzero(TEMP)]
CMOS_g = CMOS_g.reshape(90 * 120)[np.nonzero(TEMP)]
CMOS_b = CMOS_b.reshape(90 * 120)[np.nonzero(TEMP)]
TEMP = TEMP.reshape(90 * 120)[np.nonzero(TEMP)]
CMOS_r_new = np.array([])
CMOS_g_new = np.array([])
CMOS_b_new = np.array([])
TEMP_new = np.arange(int(min(TEMP)), int(max(TEMP)) + 1, 5)
for temp in range(int(min(TEMP)), int(max(TEMP)) + 1, 5):
sum_g = 0
sum_r = 0
sum_b = 0
n_r = 0
n_g = 0
n_b = 0
# for i in range ((height-1)*(width-1)):
# for i in range (height+width-1):
for i in range(TEMP.size - 1):
# if TEMP[i] !=0 and CMOS_r[i] > RGBmin:
# if TEMP[i]<temp+average and TEMP[i]>temp-average:
# sum_r += CMOS_r[i]
# n_r += 1
# if TEMP[i] !=0 and CMOS_g[i] > RGBmin:
# if TEMP[i]<temp+average and TEMP[i]>temp-average:
# sum_g += CMOS_g[i]
# n_g += 1
# if TEMP[i] !=0 and CMOS_b[i] > RGBmin:
# if TEMP[i]<temp+average and TEMP[i]>temp-average:
# sum_b += CMOS_b[i]
# n_b += 1
if TEMP[i] != 0 and CMOS_r[i] > RGBmin:
if TEMP[i] < temp + average and TEMP[i] > temp - average:
sum_r += CMOS_r[i]
n_r += 1
if TEMP[i] != 0 and CMOS_g[i] > RGBmin:
if TEMP[i] < temp + average and TEMP[i] > temp - average:
sum_g += CMOS_g[i]
n_g += 1
if TEMP[i] != 0 and CMOS_b[i] > RGBmin:
if TEMP[i] < temp + average and TEMP[i] > temp - average:
sum_b += CMOS_b[i]
n_b += 1
if n_r > samplenumber:
CMOS_r_new = np.append(CMOS_r_new, sum_r / n_r)
else:
CMOS_r_new = np.append(CMOS_r_new, 0)
if n_g > samplenumber:
CMOS_g_new = np.append(CMOS_g_new, sum_g / n_g)
else:
CMOS_g_new = np.append(CMOS_g_new, 0)
if n_b > samplenumber:
CMOS_b_new = np.append(CMOS_b_new, sum_b / n_b)
else:
CMOS_b_new = np.append(CMOS_b_new, 0)
# xnew = np.append(xnew,sum/n)
# nnew = np.append(nnew, n)
# xnew = np.append(xnew,0)
# nnew = np.append(nnew,0)
data = np.vstack([TEMP_new, CMOS_r_new, CMOS_g_new, CMOS_b_new]).T
# ic(data)
np.savetxt('/Users/paix/Desktop/Python_lab/frame_data/20210706/csv/' + fg +
'/' + str(laser_power) + '.csv',
data,
delimiter=',',
fmt='%d')
header = ["temperature", "R", "G", "B"]
with open(
'/Users/paix/Desktop/Python_lab/frame_data/20210706/csv/' + fg +
'/' + str(laser_power) + 'to_csv_out.csv', 'w') as f:
writer = csv.writer(f)
writer.writerow(header)
with open('/Users/paix/Desktop/Python_lab/frame_data/20210706/csv/' +
fg + '/' + str(laser_power) + '.csv') as fr:
reader = csv.reader(fr)
writer.writerows(reader)
# ヘッダーに "temperature","R","G","B" を追加
# 2つ目
df = pd.read_csv(
'/Users/paix/Desktop/Python_lab/frame_data/20210706/csv/' + fg + '/' +
str(laser_power) + 'to_csv_out.csv',
sep=',')
if haszero:
df = df[(df.R != 0) | (df.G != 0) | (df.B != 0)] # RGBの全てが0なら削除
else:
df = df[(df.R != 0) & (df.G != 0) & (df.B != 0)] # RGBのどれか1つが0なら削除
# df = df[df.temperature > 1900]
# df = df[df.temperature < 2400]
# print(df)
csv_path = '/Users/paix/Desktop/Python_lab/frame_data/20210706/csv/' + fg + '/' + str(
laser_power) + 'to_csv_out_out.csv'
df.to_csv(csv_path)
return csv_path