os.chdir(os.path.dirname(
os.path.realpath(__file__))) # change cwd to file location
THEORY_DF = plot.import_theory(
) # import the theory steady state dataframe
DATA_LOC = ['190405_2']
# DATA_LOC = ['190405']# ,'190329','190405','190405_2', '190405_3']
for data in DATA_LOC:
rel_imgs_dir = './Data/' + data + '/' # File path relative to the script
file_ext = '.ARW'
# load in camera matrix
with open(f'{rel_imgs_dir[:7]}cam_mtx.pickle', 'rb') as pickle_in:
camera_params = pickle.load(pickle_in)
# Get list of file names
file_ids = raw_img.get_image_fid(rel_imgs_dir, file_ext)
filenames = file_ids[file_ext[1:]]
# Get background images
BG_ids = raw_img.get_image_fid(rel_imgs_dir + 'BG/', file_ext)
BG_filenames = BG_ids[file_ext[1:]]
#crop background imgs and get crop_pos
(BG, crop_pos, box_dims) = raw_img.prep_background_imgs([
raw_img.raw_img(rel_imgs_dir + 'BG/', f, file_ext)
for f in BG_filenames
], camera_params)
#----------------------------------------------------------------
plume_absorbance_thres = (0.0, 0.15)
for COUNT, f in enumerate(filenames):
# Image preprocessing ========================
# import image
IMG = raw_img.raw_img(rel_imgs_dir, f, file_ext)
os.chdir(os.path.dirname(os.path.realpath(__file__))) # change cwd to file location
video_loc = './Data/190619_3/'
file_ext = '.jpg'
fps = 50
time_between_img = 0.5 # seconds
plume_absorbance_thres = (0.0, 0.15)
# Not needed but saves changing the prep_background imgs func
with open(f'{video_loc[:7]}cam_mtx.pickle', 'rb') as pickle_in:
camera_params = pickle.load(pickle_in)
if CAPTURE_FRAMES == 1:
# create jpgs frames
video_to_frames(video_loc, '00000.MTS', image_ext=file_ext,
video_fps=fps, spacing=time_between_img, start_time=10)
# Get list of file names
file_ids = raw_img.get_image_fid(video_loc, file_ext)
FNAMES = file_ids[file_ext[1:]]
# Get background images
BG_IDS = raw_img.get_image_fid(video_loc + 'BG/', file_ext)
BG_FNAMES = BG_IDS[file_ext[1:]]
(BG, CROP_POS, box_dims) = raw_img.prep_background_imgs(
[raw_img.raw_img(video_loc + 'BG/',
f, file_ext) for f in BG_FNAMES], camera_params)
for count, f in enumerate(FNAMES):
# Preprocess the image
#########################################
img = raw_img.raw_img(video_loc, f, file_ext)
img.get_experiment_conditions(get_g_ss = True)
if count == 0:
plume_q = (img.plume_q*1e-06) / 60 # m^3s^-1
conc_data = conc_data[['image', 'dye conc (ppm)']]
channels = ['red', 'green', 'blue']
stats = [
'max', 'h max', 'v max', 'min', 'h min', 'v min', 'mean',
'h mean', 'v mean', 'std', 'h std', 'v std'
]
multi_col = pd.MultiIndex.from_product([channels, stats], )
conc = pd.DataFrame(columns=multi_col)
conc[conc_data.columns] = conc_data
# load in camera matrix
with open(f'{rel_imgs_dir[:7]}cam_mtx.pickle', 'rb') as pickle_in:
camera_params = pickle.load(pickle_in)
# Get list of file names
file_ids = raw_img.get_image_fid(rel_imgs_dir, FILE_EXT)
filenames = file_ids[FILE_EXT[1:]]
# Get background images
BG_ids = raw_img.get_image_fid(rel_imgs_dir + 'BG/', FILE_EXT)
BG_filenames = BG_ids[FILE_EXT[1:]]
#crop background imgs and get crop_pos
(BG, crop_pos, box_dims) = raw_img.prep_background_imgs([
raw_img.raw_img(rel_imgs_dir + 'BG/', f, FILE_EXT)
for f in BG_filenames
], camera_params)
#----------------------------------------------------------------
# Analyse in reverse so that the crop images are of the steady state
for count, f in enumerate(filenames):
try:
FILE_EXT = '.ARW'
GET_CAM_MTX = 1
if GET_CAM_MTX == 1:
# termination CRITERIA
CRITERIA = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(12,5,0)
OBJP = np.zeros((10*15, 3), np.float32)
OBJP[:, :2] = np.mgrid[0:15, 0:10].T.reshape(-1, 2)
# Arrays to store object points and image points from all the images.
OBJPOINTS = [] # 3d point in real world space
IMGPOINTS = [] # 2d points in image plane.
# Get list of file names
FILE_IDS = raw_img.get_image_fid(REL_IMGS_DIR, FILE_EXT)
FILENAMES = FILE_IDS[FILE_EXT]
for fname in FILENAMES:
print(f'Processing {fname}')
obj = raw_img.raw_img(REL_IMGS_DIR, fname, FILE_EXT)
IMG = obj.raw_image
gray = cv2.cvtColor(IMG, cv2.COLOR_RGB2GRAY)
# gray = img
# Find the chess board corners
RET, corners = cv2.findChessboardCorners(gray, (15, 10), None)
print(RET)
# If found, add object points, image points (after refining them)