fix(v5): default fft spectrum to log magnitude

README.md

@@ -86,7 +86,7 @@ The app is organized as a small MATLAB-like image workspace. Each operation crea
 
 ### Chapter 4: Frequency Domain
 
-- **FFT/DFT spectrum view** shows magnitude, log magnitude, or phase of the image in the frequency domain. Formula: `F(u,v) = DFT{f(x,y)}`. Apply it to a periodic-noisy state with `log_magnitude` to see the noise peaks.
+- **FFT/DFT spectrum view** shows the log magnitude of the image in the frequency domain. Formula: `log(1 + |fftshift(fft2(f))|)`. Apply it to a periodic-noisy state to see the noise peaks.
 - **Inverse FFT reconstruction** reconstructs from a previously saved FFT state. Formula: `f = real(ifft2(ifftshift(F)))`.
 
 ### Chapter 6: RGB Color Processing

backend/processing/registry.py

@@ -354,19 +354,14 @@ def rgb_channel(image, params):
 
 
 def fft_spectrum(image, params):
-    """Display the DFT magnitude, log magnitude, or phase spectrum of an image.
+    """Display the DFT log-magnitude spectrum of an image.
 
     Use it to understand whether image information is concentrated in low or high frequencies.
     """
 
     gray = to_gray(image).astype(np.float32)
     spectrum = np.fft.fftshift(np.fft.fft2(gray))
-    mode = params.get("mode", "log_magnitude")
+    magnitude = np.log1p(np.abs(spectrum))
-    if mode == "phase":
-        return gray_to_rgb(normalize_to_uint8(np.angle(spectrum)))
-    magnitude = np.abs(spectrum)
-    if mode == "log_magnitude":
-        magnitude = np.log1p(magnitude)
     return gray_to_rgb(normalize_to_uint8(magnitude))
 
 
@@ -436,7 +431,7 @@ OPERATIONS = [
         kernel_pair_preview("Sobel", [[-1, -2, -1], [0, 0, 0], [1, 2, 1]], [[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]),
     ], formula="Gradient image = abs(imfilter(f,Gx)) + abs(imfilter(f,Gy))."),
     operation("high_boost", "High-Boost / Edge Emphasis", CH3, "High-Boost Filtering", high_boost_slide, {"A": float_param(1.5, 1, 6, 0.1, description="Boost factor A, where A >= 1."), "K": odd_param(3, 35, description="Odd averaging mask size used for the blurred image.")}, formula="f_hb = A f - blurred(f)."),
-    operation("fft_spectrum", "FFT/DFT Spectrum View", CH4, "DFT and FFT", fft_spectrum, {"mode": select_param("log_magnitude", ["magnitude", "log_magnitude", "phase"], description="Choose magnitude, log magnitude, or phase display.")}, formula="F(u,v) = DFT{f(x,y)}", repeatable=False),
+    operation("fft_spectrum", "FFT/DFT Spectrum View", CH4, "DFT and FFT", fft_spectrum, formula="Display log(1 + |fftshift(fft2(f))|).", repeatable=False),
     operation("inverse_fft_reconstruction", "Inverse FFT Reconstruction", CH4, "DFT and FFT", inverse_fft_reconstruction, formula="f = real(ifft2(ifftshift(F))). Apply this to an FFT/DFT Spectrum View state.", repeatable=False),
     operation("rgb_to_gray", "Convert to Grayscale", CH6, "Color Conversion", rgb_to_gray_matlab, {"red_weight": float_param(0.299, 0, 1, 0.001, description="R coefficient in gray = aR + bG + cB."), "green_weight": float_param(0.587, 0, 1, 0.001, description="G coefficient in gray = aR + bG + cB."), "blue_weight": float_param(0.114, 0, 1, 0.001, description="B coefficient in gray = aR + bG + cB.")}, formula="gray = 0.299R + 0.587G + 0.114B by default.", repeatable=False),
     operation("rgb_channel", "RGB Channel View", CH6, "RGB color model", rgb_channel, {"channel": select_param("r", ["r", "g", "b"], description="Select the RGB channel to view.")}, formula="Show one RGB channel as grayscale.", repeatable=False),

backend/processing/services.py

@@ -223,15 +223,12 @@ def image_state_fft_spectrum_create(*, state, params):
     """
 
     source = load_image_array(state.image)
-    mode = params.get("mode", "log_magnitude")
-    if mode not in {"magnitude", "log_magnitude", "phase"}:
-        raise ProcessingError("FFT mode must be magnitude, log_magnitude, or phase.")
     spectrum = image_fft(source)
-    preview = fft_preview_image(spectrum, mode)
+    preview = fft_preview_image(spectrum)
     fft_data_path = save_fft_array(spectrum, "state-fft-data")
     operation_params = {
-        **params,
+        **(params or {}),
-        "mode": mode,
+        "mode": "log_magnitude",
         "fft_data_path": fft_data_path,
         "source_state_id": str(state.id),
         "source_color_mode": "RGB" if source.ndim == 3 else "L",
@@ -276,15 +273,10 @@ def image_fft(image):
     return np.stack(channels, axis=2)
 
 
-def fft_preview_image(spectrum, mode):
+def fft_preview_image(spectrum):
-    """Convert complex FFT data to a display-only uint8 preview image."""
+    """Convert complex FFT data to a display-only log-magnitude uint8 preview image."""
 
-    if mode == "phase":
+    preview = np.log1p(np.abs(spectrum))
-        preview = np.angle(spectrum)
-    else:
-        preview = np.abs(spectrum)
-        if mode == "log_magnitude":
-            preview = np.log1p(preview)
     return normalize_to_uint8(preview)
 
 

backend/processing/tests/test_api.py

@@ -83,6 +83,7 @@ class ApiTests(TestCase):
         self.assertEqual(operations["periodic_noise"]["params"]["T"]["default"], 100)
         self.assertEqual(operations["inverse_fft_reconstruction"]["label"], "Inverse FFT Reconstruction")
         self.assertFalse(operations["inverse_fft_reconstruction"]["repeatable"])
+        self.assertEqual(operations["fft_spectrum"]["params"], {})
         self.assertEqual(operations["box_filter"]["label"], "Average / Box Filter")
         self.assertEqual(operations["gaussian_filter"]["label"], "Gaussian Filter")
         self.assertFalse(operations["negative"]["repeatable"])
@@ -312,7 +313,7 @@ class ApiTests(TestCase):
         s0_id = upload.data["states"][0]["state_id"]
         spectrum = self.client.post(
             f"/api/states/{s0_id}/operations/",
-            {"operation": "fft_spectrum", "params": {"mode": "log_magnitude"}},
+            {"operation": "fft_spectrum", "params": {}},
             format="json",
         )
 
@@ -327,7 +328,7 @@ class ApiTests(TestCase):
         s0_id = upload.data["states"][0]["state_id"]
         spectrum = self.client.post(
             f"/api/states/{s0_id}/operations/",
-            {"operation": "fft_spectrum", "params": {"mode": "log_magnitude"}},
+            {"operation": "fft_spectrum", "params": {}},
             format="json",
         )
         response = self.client.post(
@@ -347,7 +348,7 @@ class ApiTests(TestCase):
         s0_id = upload.data["states"][0]["state_id"]
         spectrum = self.client.post(
             f"/api/states/{s0_id}/operations/",
-            {"operation": "fft_spectrum", "params": {"mode": "log_magnitude"}},
+            {"operation": "fft_spectrum", "params": {}},
             format="json",
         )
         response = self.client.post(