save conf

2025-07-18 08:52:59 +02:00
parent b3c4969a2f
commit 987d0eda50
4 changed files with 226 additions and 6 deletions
--- a/Config/config.json
+++ b/Config/config.json
@@ -3,7 +3,7 @@
    "Amplitude_mm": 1,
    "Time_in_beam_s": 5,
    "Time_out_of_beam_s": 10,
-    "Exposure_time": 0.0004,
+    "Exposure_time": 0.0003,
    "Img_Processing": 1,
    "pixel_size_mu": 0.275
 }
--- a/Scripts/pycache/metrology_functions.cpython-313.pyc
+++ b/Scripts/pycache/metrology_functions.cpython-313.pyc
--- a/Scripts/metrology_functions.py
+++ b/Scripts/metrology_functions.py
@@ -82,13 +82,42 @@ def gaussian_2d(coords, amplitude, x0, y0, sigma_x, sigma_y, offset):
    return amplitude * np.exp(
        -(((x - x0) ** 2) / (2 * sigma_x ** 2) + ((y - y0) ** 2) / (2 * sigma_y ** 2))
    ) + offset
+def band_pass_filter(image, low_radius, high_radius):
+    # Convert to float32 for FFT
+    img_float = np.float32(image)
+
+    # Get shape
+    rows, cols = img_float.shape
+    crow, ccol = rows // 2 , cols // 2  # center
+
+    # Perform FFT and shift zero frequency to center
+    f = np.fft.fft2(img_float)
+    fshift = np.fft.fftshift(f)
+
+    # Create a band-pass mask
+    mask = np.zeros((rows, cols), np.uint8)
+    for u in range(rows):
+        for v in range(cols):
+            dist = np.sqrt((u - crow)**2 + (v - ccol)**2)
+            if low_radius < dist < high_radius:
+                mask[u, v] = 1
+
+    # Apply mask and inverse FFT
+    fshift_filtered = fshift * mask
+    f_ishift = np.fft.ifftshift(fshift_filtered)
+    img_back = np.fft.ifft2(f_ishift)
+    img_back = np.abs(img_back)
+
+    return img_back

 def __process_img(img , retimg=0):
+    img_np = np.array(img)
+    img = band_pass_filter(img_np,15,25)
    img = cv2.GaussianBlur(img,(5,5),0)
    img = spnd.zoom(img, 4,order=3)

    z = np.array(img)
-    # Create a test image (e.g., 2D Gaussian)
+    """# 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)
@@ -101,8 +130,8 @@ def __process_img(img , retimg=0):
    popt, _ = curve_fit(gaussian_2d, (x.ravel(), y.ravel()), z.ravel(), p0=initial_guess)

    x = popt[1]
-    y = popt[2]
-    x,y = np.unravel_index(np.argmax(z), z.shape)
+    y = popt[2]"""
+    y,x = np.unravel_index(np.argmax(z), z.shape)
    #ret1, th1 = cv2.threshold(img, 196, 255, cv2.THRESH_BINARY)
    #x, y = image_center_of_mass(img, plot=False, verbose=False)
    if retimg:
--- a/notebooks/sample.ipynb
+++ b/notebooks/sample.ipynb
@@ -87,6 +87,10 @@
   "metadata": {
    "jupyter": {
     "source_hidden": true
+    },
+    "ExecuteTime": {
+     "end_time": "2025-07-18T06:52:44.899219Z",
+     "start_time": "2025-07-18T06:52:44.675052Z"
    }
   },
   "source": [
@@ -295,8 +299,195 @@
    "\n",
    "\n"
   ],
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Path exists: C:\\Users\\berti_r\\Python_Projects\\StagePerformaceDocu\\Scripts\n",
+      "Path exists: C:\\Users\\berti_r\\Python_Projects\\StagePerformaceDocu\\Config\\config.json\n",
+      "Path exists: C:\\Users\\berti_r\\Python_Projects\\StagePerformaceDocu\\Config\\config.json\n",
+      "Path exists: C:\\Users\\berti_r\\Python_Projects\\templates\\motion_libs\n",
+      "Path exists: C:\\Users\\berti_r\\Python_Projects\\StagePerformaceDocu\\Config\\measurement.json\n",
+      "Constructor for PLC\n",
+      "Connect to PLC\n",
+      "is_open()=True\n",
+      "get_local_address()=None\n",
+      "read_device_info()=('Plc30 App', <pyads.structs.AdsVersion object at 0x00000214B0829550>)\n",
+      "GVL_APP.nAXIS_NUM=3\n",
+      "Constructor for axis\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Dropdown(description='Test Type:', options=('Image Test', 'Repeatability Test', 'Static Test'), value='Image T…"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "32821ca26005425a9c8732deb7dc87ed"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Output()"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "a17b77f1308a40a08fdf90784756fe86"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "BoundedIntText(value=10, description='Nr of cycles:', max=1000, min=1)"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "ca9b34140a1b49ac976feaf9eef11b92"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Output()"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "8f31b3e0d1254f7fae2a3f0df4e5d375"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(value='0.0003', description='Exposure [s]:')"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "6da3d9bc3be34c88a6b19957d0d58f35"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Output()"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "4f986990fc59495dbdb5934387d40120"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "ToggleButton(value=True, description='Processing', tooltip='Toggle processing on/off')"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "d3e8ee7c113940a487f265c1c0b20b08"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Output()"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "eb679b026bb140d1bd0f3c862cfc9b00"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Button(description='Set exposure time', style=ButtonStyle())"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "cc949c4a1bad410da8289166146a509e"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Output()"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "583de4908de9490ca46e8f642ab39691"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Button(description='Start Measurement', style=ButtonStyle())"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "97bbe99cd5a843d485f61bc9ea5ecad7"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Output()"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "6277546bd4a94b41a3dd142175b3b13f"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 1
  },
  {
   "cell_type": "markdown",