def test_find_and_capture_lepton():
camera = Lepton()
image = camera.grab()
assert image is not None
assert len(image.shape) == 2
assert image.dtype == 'uint16'
camera.close()
def test_capture_speed():
with Lepton() as camera:
_ = camera.grab()
tstart = time.time()
for _ in range(20):
image = camera.grab()
tend = time.time()
assert tend - tstart < 2.5
bot = telepot.Bot(token)
# 구글 API 설정
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = r'ServiceAccountToken.json'
client = vision.ImageAnnotatorClient()
facenet = cv2.dnn.readNet(
'../training custom dataset/face_detector/deploy.prototxt',
'../training custom dataset/face_detector/res10_300x300_ssd_iter_140000.caffemodel'
)
# FaceDetector 모델 > OpenCv의 DNN
model = load_model('../training custom dataset/mask_detector.model')
# MaskDetector 모델 > Keras 모델
# cv2.VideoWriter(outputFile, fourcc, frame, size) : fourcc는 코덱 정보, frame은 초당 저장될 프레임, size는 저장될 사이즈를 뜻합니다 cv2.VideoWriter_fourcc('D','I','V','X') 이런식으로 사용
# 현재 테스트 동영상의 프레임은 25
thermal_camera = Lepton()
fir = flir_image_extractor.FlirImageExtractor()
cap = cv2.VideoCapture(0)
# 동영상 로드
ret, img = cap.read()
# ret이 True이면 영상이 있다는 뜻
fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
out = cv2.VideoWriter('output.mp4', fourcc, 1, (img.shape[1], img.shape[0]))
# cv2.VideoWriter(outputFile, fourcc, frame, size) : fourcc는 코덱 정보, frame은 초당 저장될 프레임, size는 저장될 사이즈를 뜻합니다 cv2.VideoWriter_fourcc('D','I','V','X') 이런식으로 사용
# 현재 테스트 동영상의 프레임은 25
number = 0 # 마스크 안 쓴 사람 사진 저장할 때 사용
def get_max_temperature(thermal_np, x1, y1, x2, y2):
# 온도 데이터에서 얼굴 영역만 잘라서 검사함
import sys
import os.path
import cv2
from tifffile import imsave
import numpy as np
import h5py
import time
import psutil
#from uvctypesParabilis_v2 import *
from multiprocessing import Queue
import threading
from subprocess import call
# define a video capture object
colorMapType = 0
vid = Lepton()
def generate_colour_map():
"""
Conversion of the colour map from GetThermal to a numpy LUT:
https://github.com/groupgets/GetThermal/blob/bb467924750a686cc3930f7e3a253818b755a2c0/src/dataformatter.cpp#L6
"""
lut = np.zeros((256, 1, 3), dtype=np.uint8)
#colorMaps
colormap_grayscale = [
0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7,
8, 8, 8, 9, 9, 9, 10, 10, 10, 11, 11, 11, 12, 12, 12, 13, 13, 13, 14,
14, 14, 15, 15, 15, 16, 16, 16, 17, 17, 17, 18, 18, 18, 19, 19, 19, 20,
import numpy as np from flirpy.camera.lepton import Lepton camera = Lepton() image = camera.grab(0) print(image) camera.close()
frameCount = 0
globalCount = 0
duration = 10
totalFrame = 250
# se;f.spo2Flag=0
final_sig = []
spo2_set = []
vid = Lepton()
def generate_colour_map():
"""
Conversion of the colour map from GetThermal to a numpy LUT:
https://github.com/groupgets/GetThermal/blob/bb467924750a686cc3930f7e3a253818b755a2c0/src/dataformatter.cpp#L6
"""
global maxVal
global minVal
lut = np.zeros((256, 1, 3), dtype=np.uint8)
#colorMaps
colormap_grayscale = [
0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7,
from flirpy.camera.lepton import Lepton cam = Lepton() image = cam.grab() print(image) print(cam.frame_count) print(cam.frame_mean) print(cam.ffc_temp_k) print(cam.fpa_temp_k) cam.close()
def test_open_lepton():
camera = Lepton()
camera.close()
def test_capture_setup_first():
with Lepton() as camera:
camera.setup_video()
image = camera.grab()
assert image is not None
def test_capture_multiple():
with Lepton() as camera:
for _ in range(10):
image = camera.grab()
print(image.mean)
def test_find_lepton():
camera = Lepton()
camera.find_video_device()
camera.close()
import time
import pytest
from flirpy.camera.lepton import Lepton
if Lepton.find_video_device() is None:
pytest.skip("Lepton not connected, skipping tests", allow_module_level=True)
def test_open_lepton():
camera = Lepton()
camera.close()
def test_find_lepton():
camera = Lepton()
camera.find_video_device()
camera.close()
def test_find_and_capture_lepton():
camera = Lepton()
image = camera.grab()
assert image is not None
assert len(image.shape) == 2
assert image.dtype == 'uint16'
camera.close()
def test_capture_multiple():
with Lepton() as camera:
for _ in range(10):
image = camera.grab()
from collections import deque
# telegram API bot = co_vision_bot
token = '1130712531:AAE3W0J9Y3s2opGvE_c8My8e96-vhqlLAGE'
mc = '1314303321'
bot = telepot.Bot(token)
# 구글 API 설정
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = r'ServiceAccountToken.json'
client = vision.ImageAnnotatorClient()
facenet = cv2.dnn.readNet('../training custom dataset/face_detector/deploy.prototxt', '../training custom dataset/face_detector/res10_300x300_ssd_iter_140000.caffemodel')
# FaceDetector 모델 > OpenCv의 DNN
model = load_model('../training custom dataset/mask_detector.model')
# MaskDetector 모델 > Keras 모델
thermal_camera = Lepton()
fir = flir_image_extractor.FlirImageExtractor()
class MainWindow(QWidget):
# class constructor
def __init__(self):
# call QWidget constructor
super().__init__()
self.ui = Ui_Form()
self.ui.setupUi(self)
# create a timer
self.timer = QTimer()
# set timer timeout callback function
self.timer.timeout.connect(self.viewCam)
def main():
tDetector = TensoflowFaceDector(PATH_TO_CKPT)
dlib_landmarks = dlib.shape_predictor(DLIB_LANDMARKS_MODEL)
fov_rgb = 60
fov_thermal = 71
fov_offset = 0 #0.055
fov_correction = math.tan(math.radians(fov_rgb / 2)) / math.tan(
math.radians(fov_thermal / 2))
print(fov_correction)
# fov_correction = fov_rgb/fov_thermal + fov_offset
rgb_camera = cv2.VideoCapture(2)
rgb_camera.set(cv2.CAP_PROP_FPS, 8)
rgb_fps = rgb_camera.get(cv2.CAP_PROP_FPS)
# Check if camera opened successfully
if (rgb_camera.isOpened() == False):
print("Unable to read camera feed")
# Default resolutions of the frame are obtained.The default resolutions are system dependent.
# We convert the resolutions from float to integer.
frame_width = int(rgb_camera.get(3))
frame_height = int(rgb_camera.get(4))
output_vid = cv2.VideoWriter('thermal7.avi',
cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'),
10, (frame_width * 2, frame_height))
# print("RGB FPS: {:.2f}".format(rgb_fps))
thermal_camera = Lepton()
thermal_width, thermal_height = 160, 120
corrected_thermal_width = thermal_width * fov_correction
corrected_thermal_height = thermal_height * fov_correction
interval, count = 0, 0
cx_thermal, cy_thermal = int(thermal_width / 2), int(thermal_height / 2)
radius = 1
roi = 5
x1 = cx_thermal - roi
x2 = cx_thermal + roi
y1 = cy_thermal - roi
y2 = cy_thermal + roi
x1_crop = cx_thermal - int(corrected_thermal_width / 2)
y1_crop = cy_thermal - int(corrected_thermal_height / 2)
x2_crop = cx_thermal + int(corrected_thermal_width / 2)
y2_crop = cy_thermal + int(corrected_thermal_height / 2)
cx_corrected = int((x1_crop + x2_crop) / 2)
cy_corrected = int((y1_crop + y2_crop) / 2)
left_offset = 25 #+20
right_offset = 25 #-35
top_offset = 12 #20
bottom_offset = 12 #-20
cx_rgb, cy_rgb = int(frame_width / 2), int(frame_height / 2)
pts_src = np.array([[179, 229], [346, 224], [513, 215], [556, 172],
[78, 198]])
pts_dst = np.array([[889 - 640, 203], [1018 - 640, 204], [1145 - 640, 201],
[1175 - 640, 174], [818 - 640, 179]])
# print('Calculating homographic matrix')
# H, status = cv2.findHomography(pts_src, pts_dst)
while True:
t1 = time.time()
# ********** THERMAL CAMERA *************************************
raw_image = thermal_camera.grab(device_id=0)
# raw_image_corrected = raw_image[y1_crop:y2_crop, x1_crop:x2_crop]
# thermal_image = ((np.subtract(raw_image_corrected, norm_mat_new.transpose() * 7000)/1500)*255).astype(np.uint8)
thermal_image = ((np.subtract(raw_image, 7000) / 1500) * 255).astype(
np.uint8)
#*********** RGB CAMERA PROCESSING *******************************
ret, rgb_image = rgb_camera.read()
gray_image = cv2.cvtColor(rgb_image, cv2.COLOR_BGR2GRAY)
if ret == False:
break
rgb_height, rgb_width, rgb_channel = rgb_image.shape
boxes, scores, classes, num_detections = tDetector.run(rgb_image)
boxes = np.squeeze(boxes)
scores = np.squeeze(scores)
for score, box in zip(scores, boxes):
if score > 0.5:
# print('Detecting face ...')
# ymin, xmin, ymax, xmax = box
left = int(box[1] * rgb_width)
top = int(box[0] * rgb_height)
right = int(box[3] * rgb_width)
bottom = int(box[2] * rgb_height)
box_width = right - left
box_height = bottom - top
cv2.rectangle(rgb_image, (left, top), (right, bottom),
(255, 255, 255), int(round(rgb_height / 150)), 8)
# cv2.imwrite(filename, image)
scaled_left = int((left / rgb_width *
corrected_thermal_width) + left_offset)
scaled_top = int((top / rgb_height *
corrected_thermal_height) + top_offset)
scaled_right = int((right / rgb_width *
corrected_thermal_width) + right_offset)
scaled_bottom = int((bottom / rgb_height *
corrected_thermal_height) + bottom_offset)
# if top > 70 and right:
# scaled_left = int((left+left_offset)/rgb_width * corrected_thermal_width)
# scaled_top = int((top+top_offset)/rgb_height * corrected_thermal_height)
# scaled_right = int((right+right_offset)/rgb_width * corrected_thermal_width)
# scaled_bottom = int((bottom+bottom_offset)/rgb_height * corrected_thermal_height)
# else:
# scaled_left = int((left+left_offset)/rgb_width * corrected_thermal_width)
# scaled_top = int((top)/rgb_height * corrected_thermal_height)
# scaled_right = int((right+right_offset)/rgb_width * corrected_thermal_width)
# scaled_bottom = int((bottom)/rgb_height * corrected_thermal_height)
# DLIB FACIAL LANDMARKS
dlibRect = dlib.rectangle(left, top, right, bottom)
shape = dlib_landmarks(gray_image, dlibRect)
# shape = face_utils.shape_to_np(shape)
# Temperature RoI
# Case 0: Eye regions
# leye_x_rgb = shape.part(38).x
# leye_y_rgb = shape.part(38).y
# reye_x_rgb = shape.part(40).x
# reye_y_rgb = shape.part(40).y
# Case 1: Tear Glands regions
# RGB Image
ltear_x1_rgb = shape.part(38).x
ltear_y1_rgb = shape.part(38).y
ltear_x2_rgb = shape.part(39).x
ltear_y2_rgb = shape.part(40).y
cv2.rectangle(rgb_image, (ltear_x1_rgb, ltear_y1_rgb),
(ltear_x2_rgb, ltear_y2_rgb), (0, 255, 0), 2, 2)
rtear_x1_rgb = shape.part(42).x
rtear_y1_rgb = shape.part(43).y
rtear_x2_rgb = shape.part(47).x
rtear_y2_rgb = shape.part(47).y
cv2.rectangle(rgb_image, (rtear_x1_rgb, rtear_y1_rgb),
(rtear_x2_rgb, rtear_y2_rgb), (0, 255, 0), 2, 2)
# Thermal Image
ltear_x1_thermal = int(
shape.part(38).x / rgb_width * corrected_thermal_width +
left_offset)
ltear_y1_thermal = int(
shape.part(38).y / rgb_height * corrected_thermal_height +
top_offset)
ltear_x2_thermal = int(
shape.part(39).x / rgb_width * corrected_thermal_width +
right_offset)
ltear_y2_thermal = int(
shape.part(40).y / rgb_height * corrected_thermal_height +
bottom_offset)
cv2.rectangle(thermal_image,
(ltear_x1_thermal, ltear_y1_thermal),
(ltear_x2_thermal, ltear_y2_thermal),
(0, 255, 0), 1, 1)
rtear_x1_thermal = int(
shape.part(42).x / rgb_width * corrected_thermal_width +
left_offset)
rtear_y1_thermal = int(
shape.part(43).y / rgb_height * corrected_thermal_height +
top_offset)
rtear_x2_thermal = int(
shape.part(47).x / rgb_width * corrected_thermal_width +
right_offset)
rtear_y2_thermal = int(
shape.part(47).y / rgb_height * corrected_thermal_height +
bottom_offset)
cv2.rectangle(thermal_image,
(rtear_x1_thermal, rtear_y1_thermal),
(rtear_x2_thermal, rtear_y2_thermal),
(0, 255, 0), 1, 1)
# Case 2: Inside mouth region
mouth_x1_rgb = shape.part(48).x
mouth_y1_rgb = shape.part(50).y
mouth_x2_rgb = shape.part(54).x
mouth_y2_rgb = shape.part(57).y
cv2.rectangle(rgb_image, (mouth_x1_rgb, mouth_y1_rgb),
(mouth_x2_rgb, mouth_y2_rgb), (0, 255, 0), 2, 2)
# Thermal Image
mouth_x1_thermal = int(
shape.part(48).x / rgb_width * corrected_thermal_width +
left_offset)
mouth_y1_thermal = int(
shape.part(50).y / rgb_height * corrected_thermal_height +
top_offset)
mouth_x2_thermal = int(
shape.part(54).x / rgb_width * corrected_thermal_width +
right_offset)
mouth_y2_thermal = int(
shape.part(57).y / rgb_height * corrected_thermal_height +
bottom_offset)
cv2.rectangle(thermal_image,
(mouth_x1_thermal, mouth_y1_thermal),
(mouth_x2_thermal, mouth_y2_thermal),
(0, 255, 0), 1, 1)
# Case 3: Forehead regions
# forehead_height = shape.part(19).y - top
# RGB IMAGE
forehead_x1_rgb = shape.part(19).x
forehead_y1_rgb = shape.part(19).y
forehead_x2_rgb = shape.part(24).x
forehead_y2_rgb = shape.part(24).y
cv2.rectangle(rgb_image, (forehead_x1_rgb, top),
(forehead_x2_rgb, forehead_y2_rgb), (0, 255, 0),
2, 2)
# Thermal Image
forehead_x1_thermal = int(
shape.part(19).x / rgb_width * corrected_thermal_width +
left_offset)
forehead_y1_thermal = int(
shape.part(19).y / rgb_height * corrected_thermal_height +
top_offset)
forehead_x2_thermal = int(
shape.part(24).x / rgb_width * corrected_thermal_width +
right_offset)
forehead_y2_thermal = int(
shape.part(24).y / rgb_height * corrected_thermal_height +
bottom_offset)
cv2.rectangle(thermal_image, (forehead_x1_thermal, scaled_top),
(forehead_x2_thermal, forehead_y2_thermal),
(0, 255, 0), 1, 1)
# cv2.circle(rgb_image, (leye_x_rgb, leye_y_rgb), 1, (0,255,0), 2)
# cv2.circle(rgb_image, (reye_x_rgb, reye_y_rgb), 1, (0,255,0), 2)
# leye_x_thermal = int(leye_x_rgb/rgb_width * corrected_thermal_width)
# leye_y_thermal = int(leye_y_rgb/rgb_height * corrected_thermal_height)
# reye_x_thermal = int(reye_x_rgb/rgb_width * corrected_thermal_width)
# reye_y_thermal = int(reye_y_rgb/rgb_height * corrected_thermal_height)
# cv2.circle(thermal_image, (leye_x_thermal, leye_y_thermal), 1, (0,255,0), -1)
# cv2.circle(thermal_image, (reye_x_thermal, reye_y_thermal), 1, (0,255,0), -1)
# for i in range(67):
# # landmarks in rgb image
# cv2.circle(rgb_image, (shape.part(i).x, shape.part(i).y), 1, (0,0,255), 2)
# # landmarks in thermal image
# cv2.circle(thermal_image, (int((shape.part(i).x)/rgb_width*corrected_thermal_width+left_offset),
# int((shape.part(i).y)/rgb_height*corrected_thermal_height+top_offset)), 1, (0,0,255), 1)
# cv2.imshow('test_image', rgb_image)
# left_top = H.dot(np.array([[left], [top], [1]]))
# right_bottom = H.dot(np.array([[right], [bottom], [1]]))
# x1, y1 = int(left_top[0][0]), int(left_top[1][0])
# x2, y2 = int(right_bottom[0][0]), int(right_bottom[1][0])
#************* TEMPERTURE PROCESSING **************************
# calculate temperature only at RoI
# Case 0: Entire Face
# roi_crop = raw_image[scaled_top:scaled_bottom,scaled_left:scaled_right]
# Case 1: Mouth inside
roi_crop = raw_image[mouth_y1_thermal:mouth_y2_thermal,
mouth_x1_thermal:mouth_x2_thermal]
# Case 2: Forehead
# roi_crop = raw_image[scaled_top:forehead_y2_thermal,forehead_x1_thermal:forehead_x2_thermal]
avg_read = np.mean(roi_crop.flatten())
min_read = np.min(roi_crop.flatten())
max_read = np.max(roi_crop.flatten())
# Formula with average reading - (taking all data)-->R2=99.55
# temp_degF = 0.096156 * avg_read - 675.106714
# Formula with average reading - (taking selected data)-->R2=99.74%
temp_degF = 0.09745 * avg_read - 685.80252
# Formula with maximum reading - (taking selected data)-->R2=99.54%
# temp_degF = 0.0931129 * max_read - 652.367
temp_degC = (temp_degF - 32) * 5 / 9 - 3
# cv2.rectangle(thermal_image, (x1, y1),(x2, y2),(0, 0, 255), int(round(thermal_height/150)), 1)
cv2.rectangle(thermal_image, (scaled_left, scaled_top),
(scaled_right, scaled_bottom), (255, 255, 255),
int(round(thermal_height / 150)), 1)
cv2.putText(thermal_image,
r'Temp: {:.2f} deg C'.format(temp_degC),
(scaled_left + 2, scaled_top - 5),
cv2.FONT_HERSHEY_SIMPLEX, 0.2, (0, 0, 255), 1,
cv2.LINE_AA)
# else:
# print('Not detecting face...')
t2 = time.time()
interval += (t2 - t1)
# print('Average temperature: {:.2f}'.format(temp_degF))
# ************** Usuage: CAMERA CALIBRATION *****************
# if count==0 or interval>= 120:
# os.system('spd-say "Next Reading"')
# roi_crop = image[y1:y2, x1:x2]
# avg_read = np.mean(roi_crop.flatten())
# min_read = np.min(roi_crop.flatten())
# max_read = np.max(roi_crop.flatten())
# # data = roi_crop.flatten().tolist()
# # mode_read = max(set(data), key=data.count)
# print("Iteration: {}".format(count))
# print('Average value: {:.2f}'.format(avg_read))
# print('Minimum value: {}'.format(min_read))
# print('Maximum value: {}'.format(max_read))
# print('---------------------------------')
# # print('Mode value: {}'.format(mode_read))
# interval = 0
# count += 1
fps = (1 / (t2 - t1))
# cv2.circle(rgb_image, (cx_rgb, cy_rgb), 5, (255,0,0), -1)
# cv2.circle(thermal_image, (cx_corrected, cy_corrected), 1, (0,255,0), -1)
# cv2.circle(thermal_image, (int(cx_rgb/rgb_width*corrected_thermal_width),
# int(cy_rgb/rgb_height*corrected_thermal_height)), 1, (255,0,0), -1)
# print("FPS: {:.2f}".format(fps))
# cv2.circle(thermal_image, (cx, cy), 1, (0,0,0), -1)
# cv2.rectangle(thermal_image, (x1,y1), (x2,y2), (255,255,255), 1)
# cv2.putText(thermal_image, 'FPS: {:.2f}'.format(fps), (3,10),
# cv2.FONT_HERSHEY_SIMPLEX, 0.2, (0,0,255), 1, cv2.LINE_AA)
thermal_image = cv2.applyColorMap(thermal_image, cv2.COLORMAP_HOT)
# print(heatmap.shape)
disp_window = np.hstack([
cv2.resize(rgb_image, (640, 480)),
cv2.resize(thermal_image, (640, 480))
])
# display rgb image
cv2.imshow('Temperature Detector', disp_window)
# cv2.imshow('Temperature Detector1', thermal_image)
# cv2.imshow('Temperature Detector2', cv2.resize(rgb_image, (640, 480)))
output_vid.write(disp_window)
k = cv2.waitKey(1) & 0xff
if k == ord('q') or k == 27:
break
thermal_camera.close()
# close the camera
thermal_camera.close()
output_vid.release()
rgb_camera.release()
cv2.destroyAllWindows()
# import the opencv library
import cv2
from flirpy.camera.lepton import Lepton
import numpy
import sys
import pyperclip
numpy.set_printoptions(threshold=sys.maxsize)
# define a video capture object
vid = Lepton()
def getFrame():
global tiff_frame
global camState
global maxVal
global minVal
data = q.get(True, 500)
if data is None:
print('No Data')
if camState == 'recording':
startRec.hdf5_file.create_dataset(('image'+str(tiff_frame)), data=data)
tiff_frame += 1
#Cannot you cv2.resize on raspberry pi 3b+. Not enough processing power.
#data = cv2.resize(data[:,:], (640, 480))
minVal, maxVal, minLoc, maxLoc = cv2.minMaxLoc(data)
img = cv2.LUT(raw_to_8bit(data), generate_colour_map())
#display_temperature only works if cv2.resize is used
#display_temperatureK(img, minVal, minLoc, (255, 0, 0)) #displays min temp at min temp location on image
#display_temperatureK(img, maxVal, maxLoc, (0, 0, 255)) #displays max temp at max temp location on image
#display_temperatureK(img, minVal, (10,55), (255, 0, 0)) #display in top left corner the min temp
#display_temperatureK(img, maxVal, (10,25), (0, 0, 255)) #display in top left corner the max temp
return img
