img_int = np.array(
Image.open(
"/Users/paix/Desktop/Python_lab/calibration/time_average/colorimg/CMOS_int8_1600_B.bmp"
))
img_pyrometer = np.array(
Image.open(
"/Users/paix/Desktop/Python_lab/calibration/time_average/colorimg/1600_11.bmp"
))
print(np.all(img_int == img_uint))
img_error = img_int - img_pyrometer
ic(img_error.size)
ic(np.count_nonzero(img_error))
error_rate = np.count_nonzero(img_error) / img_error.size * 100
ic(error_rate)
temp_CMOS = tc.totemperature(img_int)
temp_CMOS = np.where(temp_CMOS > 2200, 0, temp_CMOS)
temp_CMOS = np.where(temp_CMOS < 1800, 0, temp_CMOS)
temp_pyrometer = tc.totemperature(img_pyrometer)
temp_pyrometer = np.where(temp_pyrometer > 2200, 0, temp_pyrometer)
temp_pyrometer = np.where(temp_pyrometer < 1800, 0, temp_pyrometer)
temp_error = temp_pyrometer - temp_CMOS
ic(np.sum(temp_error)) # これ絶対値とる必要あるかも
ic(np.count_nonzero(temp_CMOS))
ic(np.sum(temp_error) / np.count_nonzero(temp_CMOS))
ic(np.max(temp_error))
ic(np.min(temp_error))
# print(np.isclose(img_int, img_pyrometer))
print('laser power? 1400 / 1600 / 1800')
laser_power = int(input())
frames = 100
RGBvalue = 0
height = 90
width = 120
RGBmin = 25
RGBmax = 255
# pathの設定
color_img_path = 'calibration/time_average/colorimg/20210421/' + str(
laser_power) + "_101.bmp"
if os.path.exists(color_img_path):
img_pil = Image.open(color_img_path)
pyro_np = np.array(img_pil)
temperature_ave = tc.totemperature(pyro_np)
pyro_x, pyro_y = gp.get_gravitypoint_img(pyro_np)
pyro_temp_g = temperature_ave
TEMP = temperature_ave # 次元配列
TEMPplt = temperature_ave.reshape(90 * 120) # 2次元配列
# # # 温度ヒストグラム表示
# plt.hist(TEMPplt, color="red", bins=128)
# plt.ylim(0, 200)
# plt.xlim(1300, 3000)
# plt.xlabel("temperature")
# plt.show()
# print('after / before projection?')
# afbf = input()
afbf = 'after'
# print('Average range')
width = 120
RGBmin = 20
RGBmax = 255
# pathの設定
total_frame_number = end_frame_number - begin_frame_number + 1
directory_name = "/Users/paix/Desktop/Python_lab/frame_data/20201123/10/twocolors/"
base_name = directory_name + str(laser_power) + "_600/" + str(
laser_power) + "0OUT"
color_img_path = 'calibration/time_average/colorimg/' + str(
laser_power) + "_" + str(total_frame_number) + ".bmp"
if os.path.exists(color_img_path):
# if False:
img_pil = Image.open(color_img_path)
img_np = np.array(img_pil)
temperature_ave = tc.totemperature(img_np)
TEMP = temperature_ave # 1次元配列
TEMPplt = temperature_ave.reshape(90 * 120) # 2次元配列
else:
# 温度の時間平均算出開始
# 1枚目の溶融池画像を格納
img_pil = Image.open(base_name + str(begin_frame_number) + ".BMP")
img_np = np.array(img_pil)
rough_trimmed_img = tr.img_trimming(img_np, 220, 310, 640, 1279)
x, y = gp.get_gravitypoint_img(rough_trimmed_img)
trimmed_img = rough_trimmed_img[:, x - 60:x + 60]
# 温度データに変換 ######
temperature_sum = tc.totemperature(trimmed_img)
for frame_number in range(begin_frame_number + 1,
end_frame_number + 1):
import twocolor_convert as tc
from matplotlib import pyplot as plt
import os
# numpyの配列表示数の設定
np.set_printoptions(threshold=np.inf)
if __name__ == '__main__':
# レーザ出力とフレーム数の設定
# pathの設定
print("ヒストグラム表示したい画像のパスを入力")
color_img_path = input()
if os.path.exists(color_img_path):
# if False:
img_pil = Image.open(color_img_path)
img_np = np.array(img_pil)
h, w = img_np[:,:,0].shape
ic(h)
ic(w)
temperature_ave = tc.totemperature(img_np)
TEMP = temperature_ave# 1次元配列
TEMPplt = temperature_ave.reshape(h*w)# 2次元配列
# # 温度ヒストグラム表示
plt.hist(TEMPplt, color="red", bins=128)
# plt.ylim(0, 200)
plt.ylim(0, 2500)
plt.xlim(1300, 3000)
plt.xlabel("temperature")
plt.show()
def timeAverage(laser_power, middle_frame_number, frames):
"""
二色温度計画像を時間平均する
Parameters
----------
laser_power : int
レーザ出力
begin_frame_number : int
平均するフレームの開始点
end_frame_number : int
平均するフレームの終了点
Returns
-------
TEMP : numpyの二次元配列
二色温度計の時間平均値
"""
# pathの設定
total_frame_number = frames + 1
directory_name = "/Users/paix/Desktop/Python_lab/frame_data/20210706/pyrometer_short/"
base_name = directory_name + str(laser_power) + "/" + str(
laser_power) + "0OUT"
os.makedirs('frame_data/20210706/colorimg/', exist_ok=True)
color_img_path = 'frame_data/20210706/colorimg/' + str(
laser_power) + "_" + str(total_frame_number) + ".bmp"
if os.path.exists(color_img_path):
# if False:
img_pil = Image.open(color_img_path)
img_np = np.array(img_pil)
temperature_ave = tc.totemperature(img_np)
TEMP = temperature_ave # 1次元配列
TEMPplt = temperature_ave.reshape(90 * 120) # 2次元配列
# # 温度ヒストグラム表示
# plt.hist(TEMPplt, color="red", bins=128)
# plt.ylim(0, 200)
# plt.xlim(1300, 3000)
# plt.xlabel("temperature")
# plt.show()
else:
# 温度の時間平均算出開始
# 1枚目の溶融池画像を格納
begin_frame_number = middle_frame_number - int(frames / 2)
end_frame_number = middle_frame_number + int(frames / 2)
img_pil = Image.open(base_name + str(begin_frame_number) + ".BMP")
img_np = np.array(img_pil)
rough_trimmed_img = tr.img_trimming(img_np, 240, 330, 640, 1279)
x, y = gp.get_gravitypoint_img(rough_trimmed_img)
trimmed_img = rough_trimmed_img[:, x - 60:x + 60]
# 温度データに変換 ######
temperature_sum = tc.totemperature(trimmed_img)
for frame_number in range(begin_frame_number + 1,
end_frame_number + 1):
ic(frame_number)
# 1枚ずつ重心周りでトリミングしていく
img_pil = Image.open(base_name + str(frame_number) + ".BMP")
img_np = np.array(img_pil)
rough_trimmed_img = tr.img_trimming(img_np, 240, 330, 640, 1279)
x, y = gp.get_gravitypoint_img(rough_trimmed_img, False) # うまくいった
# trimmed_img = rough_trimmed_img[ : , x - 60 : x + 60]
trimmed_img = img_np[240 + y - 45:240 + y + 45,
640 + x - 60:640 + x + 60]
# 色データから温度データに変換 ######
temperature_np = tc.totemperature(trimmed_img)
temperature_sum += temperature_np
total_frame_number = end_frame_number - begin_frame_number + 1
temperature_ave = temperature_sum / total_frame_number
TEMP = temperature_ave # 1次元配列
TEMPplt = temperature_ave.reshape(90 * 120) # 2次元配列
# # 温度ヒストグラム表示
# plt.hist(TEMPplt, color="red", bins=128)
# plt.ylim(0, 200)
# plt.xlim(1300, 3000)
# plt.show()
ic(temperature_ave.shape)
# # 温度データから色データに再変換
img_ave = tc.tocolor(temperature_ave)
# 平均化した溶融池温度分布の色画像を表示
img_ave = np.array(img_ave, dtype=np.int8)
pil_img = Image.fromarray(img_ave, mode="RGB")
save_name = str(laser_power) + "_" + str(total_frame_number) + ".bmp"
pil_img.save('frame_data/20210706/colorimg/' + save_name)
# pil_img.save('calibration/time_average/colorimg/20210421/' + save_name)
# 温度の時間平均算出終了
return TEMP
# for layer_number in ['1', '5', '11']:
dir_name = "/Users/paix/Desktop/Python_lab/frame_data/20210610/temperature/" + laser_power + "_" + feed_rate + "_" + layer_number + "/"
if os.path.exists(dir_name):
files = os.listdir(dir_name)
count = len(files)
all_peak = np.array([])
all_temp = np.array([])
all_width = np.array([])
all_area = np.array([])
for file in sorted(glob.glob(dir_name + "*")):
# ic(i)
ic(file)
# img = np.array(Image.open(dir_name + "/img_" + str(i) + ".bmp"))
img = np.array(Image.open(file))
temperature_np = np.round(tc.totemperature(img), -1)
all_peak = np.append(all_peak, temperature_np.max())
all_temp = np.append(all_temp, temperature_np.reshape(-1))
all_width = np.append(
all_width,
np.amax(np.sum(np.where(temperature_np > 20, 1, 0), axis=1)))
all_area = np.append(
all_area,
np.sum(np.sum(np.where(temperature_np > 20, 1, 0), axis=1)))
outliers = 3 #外れ値の指定（最初と最後の数フレームは外れ値になる）
all_temp = all_temp[outliers * 90000:count - outliers * 90000]
all_peak = all_peak[outliers:count - outliers]
all_width = all_width[outliers:count - outliers]
all_area = all_area[outliers:count - outliers]
ic(all_temp.shape)
ave = save_3D_hist(all_temp,