sorted out some stuff with the motion lib package

README.md

@@ -74,12 +74,10 @@ pip install -r requirements.txt
 ```
 ```bash
 #!/bin/bash
-set -e
-cd "lib"
-# Clone the repo
-#git clone https://gitea.psi.ch/berti_r/Motion_lib.git
 
-pwd
+
+# Install package directly from GitHub
+pip install https://gitea.psi.ch/berti_r/Motion_lib/raw/branch/main/dist/mfl-0.0.3.tar.gz
 
 
 ```

Scripts/Y_Axis_Meas.py

@@ -45,7 +45,8 @@ import numpy as np
 from image_analysis import image_center_of_mass
 from utils import get_datestr, get_timestr
 import ad
-from metrology_functions import init_nr_of_cycles, safe_meas_settings, init_image_processing_yes_no, __process_img
+from metrology_functions import init_nr_of_cycles, safe_meas_settings, init_image_processing_yes_no
+from image_analysis import __process_img
 import myutility as myu
 import cv2
 import json

Scripts/image_analysis.py

@@ -10,6 +10,10 @@ on this image are provided.
 import matplotlib.pyplot as plt
 import scipy.ndimage as ndi
 import skimage.filters
+import numpy as np
+import scipy.ndimage as spnd
+from scipy.optimize import curve_fit
+import cv2
 
 def image_center_of_mass(image=None, binarize=True,
         apply_threshold=True, threshold=None, median_filter=True,
@@ -82,4 +86,41 @@ def image_center_of_mass(image=None, binarize=True,
         plt.legend()
         plt.show()
 
-    return com_x, com_y
+    return com_x, com_y
+def gaussian_2d(coords, amplitude, x0, y0, sigma_x, sigma_y, offset):
+    x, y = coords
+    return amplitude * np.exp(
+        -(((x - x0) ** 2) / (2 * sigma_x ** 2) + ((y - y0) ** 2) / (2 * sigma_y ** 2))
+    ) + offset
+
+def __process_img(img , retimg=0):
+    z = np.float32(img)
+
+    z = spnd.zoom(z, 4, order=3)
+
+    z = cv2.GaussianBlur(z,(11,11),4)
+
+
+
+
+
+    # Create a test image (e.g., 2D Gaussian)
+    x = np.linspace(0, z.shape[0], z.shape[0])
+    y = np.linspace(0, z.shape[1], z.shape[1])
+    x, y = np.meshgrid(x, y)
+
+
+
+    # Flatten and fit
+    x_gues, y_gues =  image_center_of_mass(z, plot=False, verbose=False)
+    max_a = np.max(z)-2
+
+    initial_guess = (max_a, x_gues, y_gues, 10, 10, 0)
+    popt, _ = curve_fit(gaussian_2d, (x.ravel(), y.ravel()), z.ravel(), p0=initial_guess,maxfev=10000)
+
+
+    x = popt[1]
+    y = popt[2]
+    if retimg:
+        return x,y,z
+    return x, y

Scripts/metrology_functions.py

@@ -5,13 +5,14 @@ import matplotlib.pyplot as plt
 from pathlib import Path
 import queue
 import sys
-import scipy.ndimage as spnd
+import json
 import threading as th
-from scipy.optimize import curve_fit
+from PIL import Image, ImageDraw
+import numpy as np
 from utils import get_datestr, get_timestr
 
 config_path = r"C:\Users\berti_r\Python_Projects\StagePerformaceDocu\Config\config.json"  #Path to the config file which has the parameters for the next measurement
-library_path = r"C:\Users\berti_r\Python_Projects\templates\motion_libs" #Path to the motion function lib (ESS based)
+#library_path = r"C:\Users\berti_r\Python_Projects\templates\motion_libs" #Path to the motion function lib (ESS based)
 measurement_mov_path = r"C:\Users\berti_r\Python_Projects\StagePerformaceDocu\Config\measurement.json"
 workdir = \
     os.path.expanduser(rf'C:\Users\berti_r\Python_Projects\StagePerformaceDocu\data\data{get_datestr()}_alignment_tests')
@@ -27,20 +28,16 @@ def check_path(path_str):
         print(f"Error: {e}")
 
 check_path(config_path)
-check_path(library_path)
-sys.path.append(library_path)
+#check_path(library_path)
+#sys.path.append(library_path)
 check_path(measurement_mov_path)
 
 import motionFunctionsLib as mfl # ToDo: comment out when using torque test
-from PIL import Image, ImageDraw
-import numpy as np
-from image_analysis import image_center_of_mass
-
+from image_analysis import image_center_of_mass, __process_img
 import ad
 import myutility as myu
-import cv2
-import json
-import shutil
+
+
 
 
 if not os.path.exists(workdir):
@@ -49,7 +46,7 @@ if not os.path.exists(workdir):
 # connect to PLC using NetId and PLC Port (from AIK in the TwinCat) TODO: <- comment out plc connect when using torque test
 plc = mfl.plc('5.67.222.118.1.1', 852)
 #TODO!!!!!!!!!!!!!!!!!!!!!!!!!!!
-plc.connect()
+#plc.connect()
 axis2 = mfl.axis(plc, 2)
 
 #insert try catch later
@@ -78,11 +75,7 @@ def safe_meas_settings(save_path):
         print(f"dir: {save_path}")
         print(f"Error: {e}")
 
-def gaussian_2d(coords, amplitude, x0, y0, sigma_x, sigma_y, offset):
-    x, y = coords
-    return amplitude * np.exp(
-        -(((x - x0) ** 2) / (2 * sigma_x ** 2) + ((y - y0) ** 2) / (2 * sigma_y ** 2))
-    ) + offset
+
 def compute_gradient(img, pos):
     x, y = pos
     x_int, y_int = int(x), int(y)
@@ -145,37 +138,7 @@ def band_pass_filter(img_float, low_radius, high_radius):
 
     return img_back
 
-def __process_img(img , retimg=0):
-    z = np.float32(img)
 
-    z = spnd.zoom(z, 4, order=3)
-
-    z = cv2.GaussianBlur(z,(11,11),4)
-
-
-
-
-
-    # Create a test image (e.g., 2D Gaussian)
-    x = np.linspace(0, z.shape[0], z.shape[0])
-    y = np.linspace(0, z.shape[1], z.shape[1])
-    x, y = np.meshgrid(x, y)
-
-
-
-    # Flatten and fit
-    x_gues, y_gues =  image_center_of_mass(z, plot=False, verbose=False)
-    max_a = np.max(z)-2
-
-    initial_guess = (max_a, x_gues, y_gues, 10, 10, 0)
-    popt, _ = curve_fit(gaussian_2d, (x.ravel(), y.ravel()), z.ravel(), p0=initial_guess,maxfev=10000)
-
-
-    x = popt[1]
-    y = popt[2]
-    if retimg:
-        return x,y,z
-    return x, y
 
 def aquire_avg(camera_a, nr=10):
     x_array = []