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import h5py
import nd2reader
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider
import random
import nrrd
def update_frame(val):
"""
Update the displayed frame based on the slider's position.
"""
frame_index = int(slider.val)
frame = dataset[frame_index, :, :]
img.set_data(frame)
fig.canvas.draw_idle()
def visualize_frames_with_slider(file_path, dataset_name):
"""
Visualizes frames from a specified dataset in an HDF5 file with a slider to browse through them.
"""
global dataset, img, slider, fig
with h5py.File(file_path, 'r') as hdf:
if dataset_name in hdf:
dataset = hdf[dataset_name]
# Initial frame
frame_index = 0
initial_frame = dataset[frame_index, :, :]
# Setting up the plot
fig, ax = plt.subplots()
plt.subplots_adjust(left=0.1, bottom=0.25)
img = ax.imshow(initial_frame, cmap='gray')
ax.set_title(f'Frame {frame_index} from {dataset_name}')
# Slider
ax_slider = plt.axes([0.1, 0.1, 0.65, 0.03])
slider = Slider(ax_slider, 'Frame', 0, dataset.shape[0] - 1, valinit=frame_index, valfmt='%0.0f')
# Update the frame when the slider is moved
slider.on_changed(update_frame)
plt.show()
else:
print(f"Dataset '{dataset_name}' not found in the file.")
def extract_metadata(nd2_file_path):
"""
Extracts metadata from a .nd2 file.
"""
with nd2reader.ND2Reader(nd2_file_path) as images:
metadata = images.metadata
return metadata
def print_metadata(obj, prefix=''):
"""
Recursively prints the metadata of the HDF5 object (group/dataset).
"""
print(f"{prefix}{obj.name}")
for key, val in obj.attrs.items():
print(f"{prefix} Metadata: {key} = {val}")
if isinstance(obj, h5py.Group):
for key in obj:
print_metadata(obj[key], prefix + ' ')
def nd2_test(nd2_file_path):
print(nd2reader.ND2Reader(nd2_file_path))
print(nd2reader.ND2Reader(nd2_file_path).metadata['num_frames'])
with nd2reader.ND2Reader(nd2_file_path) as images:
x_size = images.sizes['x']
y_size = images.sizes['y']
z_size = nd2reader.ND2Reader(nd2_file_path).metadata['z_coordinates'][0]
num_channels = images.sizes['c']
for channel in range(num_channels):
# Accessing each channel
channel_image = images.get_frame_2D(c=channel, t=500)
plt.imshow(channel_image, cmap='gray')
plt.title(f'Channel {channel}')
plt.show()
def h5_test(file_path):
with h5py.File(file_path, 'r') as hdf:
for dataset in hdf:
print(dataset)
dataset = 'pos_feature'
if dataset in hdf:
data = hdf[dataset][:]
print(data.shape)
print(data[5])
print(data[25])
print(data[55])
print(data[75])
print(data[-115])
else:
print(f"Dataset {dataset} not found in the file.")
# Visualize frames with a slider
visualize_frames_with_slider(file_path, 'img_nir')
def extract_second_channel(nd2_path, frame_index):
"""
Extract the second channel from a specified frame in an .nd2 file.
"""
with nd2reader.ND2Reader(nd2_path) as images:
return images.get_frame_2D(c=1, t=frame_index)
def create_rgb_image(paths, frame_index):
"""
Create an RGB image from the second channels of a specified frame from three .nd2 files.
"""
channels = [extract_second_channel(path, frame_index) for path in paths]
rgb_image = np.stack(channels, axis=-1)
rgb_image = (255 * (rgb_image / rgb_image.max())).astype(np.uint8)
return rgb_image
def update(val):
"""
Update the image based on the slider's position.
"""
frame_index = int(slider.val)
rgb_image = create_rgb_image(nd2_paths, frame_index)
ax.imshow(rgb_image)
fig.canvas.draw_idle()
def test_nrrd(file_path):
"""
Reads a .nrrd file and prints its metadata.
Parameters:
file_path (str): The path to the .nrrd file.
"""
try:
file, header = nrrd.read(file_path, index_order='C')
print(header['sizes'])
print(header['space directions'])
print(np.shape(np.array(file)))
except Exception as e:
print(f"An error occurred: {e}")
"""
nd2_paths = ['C:\\Users\\sep27\\Documents\\[X] Data\\flavell\\2022-06-14-03.nd2', 'C:\\Users\\sep27\\Documents\\[X] Data\\flavell\\2022-06-14-04.nd2', 'C:\\Users\\sep27\\Documents\\[X] Data\\flavell\\2022-06-14-05.nd2']
# Determine the number of frames in the smallest file
min_frames = min(nd2reader.ND2Reader(f).sizes['t'] for f in nd2_paths)
# Create the initial RGB image
frame_index = 0
rgb_image = create_rgb_image(nd2_paths, frame_index)
# Create the plot
fig, ax = plt.subplots()
plt.subplots_adjust(left=0.1, bottom=0.25)
ax.imshow(rgb_image)
# Create the slider
ax_slider = plt.axes([0.1, 0.1, 0.8, 0.03])
slider = Slider(ax_slider, 'Frame', 0, min_frames-1, valinit=frame_index, valfmt='%0.0f')
# Update the image when the slider value changes
slider.on_changed(update)
plt.show()
"""
file_path = 'C:\\Users\\sep27\\Documents\\[X] Data\\flavell\\2022-06-14-01.h5'
h5_test(file_path)
## Replace 'path_to_file.nrrd' with the actual file path
#test_nrrd('C:\\Users\\sep27\\Documents\\[X] Data\\flavell\\proj_neuropal\\2022-06-14-01\\neuron_rois.nrrd')