Batch Convert Hitachi TIFs to DM4
Published:
If you work with Hitachi SEM/STEM systems (e.g., HF5000), the export format is often a .tif image paired with a .txt file containing acquisition parameters. Opening the TIF directly in Gatan DigitalMicrograph (GMS) yields an uncalibrated image without metadata and often includes a “burned-in” info bar that interferes with FFT or measurements.
This script automates conversion to .dm4, applies calibration, writes metadata tags, and optionally crops the image to a square to remove info bars.
Key features
- Batch processing: convert all
.tiffiles in a folder to.dm4. - Auto-calibration: read
PixelSizefrom the companion.txtand set X/Y scale and units (nm). - Metadata import: parse acquisition parameters (Voltage, Magnification, Stage Position, …) and save under
Private:Hitachi_Metadata. - Smart cropping: optional
CROP_TO_SQUAREremoves rectangular footer/data bar and produces a square image for analysis. - Robust: handles missing metadata gracefully and reports progress.
Usage
- Prepare data: put
.tifimages and their corresponding.txtmetadata files in the same folder. - Open the script in GMS (Python scripting interface) and set
TARGET_FOLDERto your data path. - Configure
CROP_TO_SQUARE = Trueto remove the info bar, orFalseto keep original size. - Run the script — converted
.dm4files will be saved next to the originals.
Configuration notes
- TARGET_FOLDER: full path string to the folder containing
.tiffiles. - CROP_TO_SQUARE: boolean, default
True. - The script writes tags under
Private:Hitachi_Metadata. Open the Tag browser (Ctrl+K) to inspect.
Script Code
# -*- coding: utf-8 -*-
import DigitalMicrograph as DM
import os
import glob
# ==============================================================================
# [CONFIGURATION]
# ==============================================================================
# [IMPORTANT] Please paste your folder path inside the quotes below
TARGET_FOLDER = r"YOUR_DATA_PATH"
# True = Crop extra info bar (keep top-left square).
# False = Keep original size.
CROP_TO_SQUARE = True
# ==============================================================================
def parse_hitachi_metadata(txt_path):
"""Parses the Hitachi txt metadata file."""
metadata = {}
try:
if os.path.exists(txt_path):
with open(txt_path, 'r', encoding='utf-8', errors='ignore') as f:
for line in f:
line = line.strip()
if line and '=' in line:
parts = line.split('=', 1)
if len(parts) == 2:
metadata[parts[0].strip()] = parts[1].strip()
except Exception as e:
print(f"Error reading metadata: {txt_path}, Error: {e}")
return metadata
def crop_image_to_square(img):
"""
Checks if image is rectangular. If so, crops to the largest square.
Returns the new cropped image object. Deletes the old one.
"""
try:
cols = img.GetDimensionSize(0) # Width (X)
rows = img.GetDimensionSize(1) # Height (Y)
# If already square, do nothing
if cols == rows:
return img
# Determine square size (min side)
size = min(cols, rows)
# Get data as Numpy array
data = img.GetNumArray()
# [CRITICAL] Slice AND Copy
# Must use .copy() to create a contiguous array for DM
cropped_data = data[:size, :size].copy()
# Create NEW image from cropped data
new_img = DM.CreateImage(cropped_data)
# [MODIFIED] Use DM.DeleteImage instead of img.Close()
DM.DeleteImage(img)
return new_img
except Exception as e:
print(f" [Warning] Crop failed: {e}. Keeping original size.")
return img
def apply_calibration_and_tags(img, metadata):
"""Applies metadata to DM image."""
# 1. Calibration (Scale)
if 'PixelSize' in metadata:
try:
pixel_size = float(metadata['PixelSize'])
# [MODIFIED] Using SetDimensionUnitInfo as requested
# Y axis (1)
img.SetDimensionScale(1, pixel_size)
img.SetDimensionUnitInfo(1, 'nm', 1)
# X axis (0)
img.SetDimensionScale(0, pixel_size)
img.SetDimensionUnitInfo(0, 'nm', 1)
except ValueError:
pass
except AttributeError:
pass
# 2. Write Tags (Private:Hitachi_Metadata)
try:
tag_group = img.GetTagGroup()
except:
return
base_tag_path = "Private:Hitachi_Metadata"
for key, value in metadata.items():
safe_key = key.replace(" ", "_").replace("/", "_").replace(":", "")
try:
tag_group.SetTagAsString(f"{base_tag_path}:{safe_key}", value)
except:
pass
# 3. Standard Info
if 'Magnification' in metadata:
try:
mag = float(metadata['Magnification'])
tag_group.SetTagAsFloat("Microscope Info:Indicated Magnification", mag)
except: pass
if 'Accelerating voltage' in metadata:
try:
volts = float(metadata['Accelerating voltage'])
tag_group.SetTagAsFloat("Microscope Info:Voltage", volts)
except: pass
def batch_convert_main():
print("Starting batch conversion...")
print(f"Target Folder: {TARGET_FOLDER}")
if not os.path.exists(TARGET_FOLDER):
print("Error: Invalid folder path.")
return
tif_files = glob.glob(os.path.join(TARGET_FOLDER, "*.tif"))
if not tif_files:
print("No .tif files found.")
return
print(f"Found {len(tif_files)} TIF files. Processing...")
count = 0
for tif_path in tif_files:
txt_path = os.path.splitext(tif_path)[0] + ".txt"
metadata = parse_hitachi_metadata(txt_path)
try:
# 1. Open Image
img = DM.OpenImage(tif_path)
if img.IsValid():
# 2. Crop (Optional)
if CROP_TO_SQUARE:
# This function now uses DM.DeleteImage internally for the old image
img = crop_image_to_square(img)
# 3. Apply info
apply_calibration_and_tags(img, metadata)
# 4. Save as .dm4
save_path = os.path.splitext(tif_path)[0] + ".dm4"
img.SaveAsGatan(save_path)
# 5. Close final image
# [MODIFIED] Use global DeleteImage
DM.DeleteImage(img)
count += 1
if count % 10 == 0:
print(f"Processed {count}/{len(tif_files)}...")
else:
print(f"Skipping invalid image: {os.path.basename(tif_path)}")
except Exception as e:
print(f"Error processing {os.path.basename(tif_path)}: {e}")
print("---")
print(f"Success! Converted {count} files.")
try:
DM.OkDialog(f"Success! Converted {count} files.\nFolder: {TARGET_FOLDER}")
except:
pass
if __name__ == "__main__":
batch_convert_main()