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app.py

@@ -11,6 +11,9 @@ from typing import Tuple
 import warnings
 warnings.filterwarnings("ignore", message=".*Trying to detect.*share=True.*")
 
+# constants
+MAX_OFFSET_DISPLAY_LIMIT = 700 # max value for display and slider range for offset
+
 def create_autocorrelation_plot(autocorrelation_curve: np.ndarray, current_slider_offset: int, solver_calculated_offset: int, peak_diffs: np.ndarray, peak_indices: np.ndarray = None) -> plt.Figure:
     """
     Generates a matplotlib plot of the autocorrelation curve with vertical lines
@@ -67,9 +70,6 @@ def create_autocorrelation_plot(autocorrelation_curve: np.ndarray, current_slide
                 colors[selected_peak_idx] = 'r'  # Highlight the selected peak in red
                 sizes[selected_peak_idx] = 60  # Make the selected peak larger
                 
-            # Plot all peaks
-            #ax.scatter(shifted_peak_indices, peak_y_values, c=colors, s=sizes, marker='o', label='Detected Peaks')
-            
             # Specifically label the peak used for the selected offset
             if selected_peak_idx is not None:
                 peak_idx = peak_indices[selected_peak_idx]
@@ -102,7 +102,9 @@ def create_autocorrelation_plot(autocorrelation_curve: np.ndarray, current_slide
 
         # Set appropriate x-axis limits to focus on relevant part of the curve
         # Use this same limit for the slider's range
-        x_limit = min(700, current_slider_offset * 2, solver_calculated_offset * 2)
+        # ensure x_limit is at least a small positive number if offsets are 0 or very small
+        relevant_offset_max = max(10, current_slider_offset * 2, solver_calculated_offset * 2) # ensure a minimum sensible view
+        x_limit = min(MAX_OFFSET_DISPLAY_LIMIT, relevant_offset_max)
         ax.set_xlim([0, x_limit])
         
         # Add title and labels
@@ -163,7 +165,8 @@ def process_image(uploaded_image: np.ndarray):
         solver = InteractiveSolver(uploaded_image)
 
         min_offset = 1
-        max_offset = min(700, solver.default_offset * 2)
+        # ensure max_offset is at least min_offset and considers solver's suggestion
+        max_offset = min(MAX_OFFSET_DISPLAY_LIMIT, max(min_offset +1 , solver.default_offset * 2))
         default_offset = solver.default_offset
         default_offset = max(min_offset, min(default_offset, max_offset))
 

coverage.xml

@@ -1,12 +1,12 @@
 <?xml version="1.0" ?>
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+<coverage version="7.8.2" timestamp="1748050735477" lines-valid="394" lines-covered="367" line-rate="0.9315" branches-covered="0" branches-valid="0" branch-rate="0" complexity="0">
 	<!-- Generated by coverage.py: https://coverage.readthedocs.io/en/7.8.2 -->
 	<!-- Based on https://raw.githubusercontent.com/cobertura/web/master/htdocs/xml/coverage-04.dtd -->
 	<sources>
 		<source>/home/runner/work/magiceye-solver/magiceye-solver</source>
 	</sources>
 	<packages>
-		<package name="magiceye_solve" line-rate="0.8086" branch-rate="0" complexity="0">
+		<package name="magiceye_solve" line-rate="0.8421" branch-rate="0" complexity="0">
 			<classes>
 				<class name="__init__.py" filename="magiceye_solve/__init__.py" complexity="0" line-rate="1" branch-rate="0">
 					<methods/>
@@ -14,7 +14,7 @@
 						<line number="1" hits="1"/>
 					</lines>
 				</class>
-				<class name="solver.py" filename="magiceye_solve/solver.py" complexity="0" line-rate="0.8075" branch-rate="0">
+				<class name="solver.py" filename="magiceye_solve/solver.py" complexity="0" line-rate="0.8411" branch-rate="0">
 					<methods/>
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@@ -117,67 +115,59 @@
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 				</class>
 			</classes>

magiceye_solve/solver.py

@@ -17,65 +17,67 @@ except ImportError:
 
 from scipy.signal import fftconvolve
 
+# constants
+MIN_OFFSET_THRESHOLD = 10  # min offset for images wider than this
+MIN_PEAK_DISTANCE_FROM_CENTER = 20 # min distance of a peak from the center to be considered valid
+AUTOCORRELATION_STD_FACTOR = 3 # factor for threshold calculation in autocorrelation
+
 def offset(img: np.ndarray) -> Tuple[int, np.ndarray, np.ndarray, np.ndarray]:
     """
     Calculates the offset that defines the stereoscopic effect.
-    Now selects the offset corresponding to the highest peak in the autocorrelation curve.
+    Selects the offset corresponding to the highest peak in the autocorrelation curve.
     """
-    # Handle empty or 1D arrays gracefully
     if img.size == 0 or img.ndim < 2:
-        return 0, np.array([]), np.array([]), np.array([])
-    img = img - img.mean()
-    ac: np.ndarray = fftconvolve(img, np.flipud(np.fliplr(img)), mode='same')
-    # check ac shape
+        return 0, np.array([]), np.array([]), np.array([]) # return 0 for invalid input
+
+    img_mean_subtracted = img - img.mean()
+    ac: np.ndarray = fftconvolve(img_mean_subtracted, np.flipud(np.fliplr(img_mean_subtracted)), mode='same')
+
     if ac.ndim < 1 or ac.shape[0] < 1:
-         return img.shape[1], np.array([]), np.array([]), np.array([])
-    ac_center_row = ac[int(ac.shape[0] / 2)]
-    # check center row valid
+        return img.shape[1], np.array([]), np.array([]), np.array([]) # fallback to image width
+
+    ac_center_row = ac[ac.shape[0] // 2]
+
     if ac_center_row.size == 0 or ac_center_row.std() == 0:
-         return img.shape[1], np.array([]), np.array([]), np.array([])
+        return img.shape[1], np.array([]), np.array([]), np.array([]) # fallback if center row is invalid
 
+    idx = np.array([])
     try:
-        threshold = 3 * ac_center_row.std()
-        median_val = np.median(ac_center_row)
-        # threshold sanity check
-        if not np.isfinite(threshold) or threshold <= 0:
-             idx = np.array([])
-        else:
-             idx: np.ndarray = np.where(ac_center_row - median_val > threshold)[0]
+        std_dev = ac_center_row.std()
+        if std_dev > 0: # ensure std_dev is positive
+            threshold = AUTOCORRELATION_STD_FACTOR * std_dev
+            median_val = np.median(ac_center_row)
+            if np.isfinite(threshold): # ensure threshold is a valid number
+                idx = np.where(ac_center_row - median_val > threshold)[0]
     except (ValueError, FloatingPointError):
-        idx = np.array([])
+        pass # idx remains empty if calculation fails
 
-    # peak diffs for viz
     diffs: np.ndarray = np.ediff1d(idx)
-    raw_offset: int = img.shape[1]
+    raw_offset: int = img.shape[1] # default to image width
 
     if idx.size > 0:
-        try:
-            # skip center peak
-            center_idx = len(ac_center_row) // 2
-            valid_peaks_mask = np.abs(idx - center_idx) > 20
-            if np.any(valid_peaks_mask):
-                valid_peaks = idx[valid_peaks_mask]
-                valid_peaks_values = ac_center_row[valid_peaks]
-                if valid_peaks.size > 0:
-                    # highest peak
-                    highest_peak_idx = valid_peaks[np.argmax(valid_peaks_values)]
-                    raw_offset = abs(highest_peak_idx - center_idx)
-            # fallback to max diff if needed
-            if diffs.size > 0:
-                if raw_offset < 10 and np.max(diffs) >= 10:
-                    raw_offset = np.max(diffs)
-                elif raw_offset < 10 and np.max(diffs) < 10:
-                    raw_offset = img.shape[1]
-        except (ValueError, IndexError):
-            raw_offset = img.shape[1]
-
-    # min offset constraint
-    if img.shape[1] < 10:
-        final_offset = max(1, min(raw_offset, img.shape[1]))
-    else:
-        final_offset = max(10, min(raw_offset, img.shape[1]))
+        center_idx = len(ac_center_row) // 2
+        # consider peaks sufficiently far from the center
+        valid_peaks_mask = np.abs(idx - center_idx) > MIN_PEAK_DISTANCE_FROM_CENTER
+        
+        if np.any(valid_peaks_mask):
+            valid_peaks = idx[valid_peaks_mask]
+            valid_peaks_values = ac_center_row[valid_peaks]
+            if valid_peaks.size > 0:
+                # offset from the highest valid peak
+                highest_peak_idx = valid_peaks[np.argmax(valid_peaks_values)]
+                raw_offset = abs(highest_peak_idx - center_idx)
+        
+        # fallback to max difference between peaks if current raw_offset is too small
+        if diffs.size > 0 and raw_offset < MIN_OFFSET_THRESHOLD:
+            max_diff = np.max(diffs)
+            if max_diff >= MIN_OFFSET_THRESHOLD:
+                raw_offset = max_diff
+            # if max_diff is also small, it implies no strong periodic pattern, keep img.shape[1] as fallback
+
+    # apply min offset constraint based on image width
+    min_practical_offset = MIN_OFFSET_THRESHOLD if img.shape[1] >= MIN_OFFSET_THRESHOLD else 1
+    final_offset = max(min_practical_offset, min(raw_offset, img.shape[1]))
 
     return final_offset, ac_center_row, idx, diffs
 
@@ -158,6 +160,32 @@ class InteractiveSolver:
             self.autocorrelation_peak_indices: np.ndarray = np.array([])
             self.autocorrelation_peak_diffs: np.ndarray = np.array([])
 
+    def _process_single_channel(self, channel_data: np.ndarray, gap: int, target_width: int) -> np.ndarray:
+        """Helper to process a single image channel."""
+        if channel_data.size == 0 or channel_data.std() == 0.0:
+            return np.zeros((self.m, target_width), dtype=float)
+
+        shifted = shift_pic(channel_data, gap)
+        if shifted.size == 0 or shifted.shape[1] == 0: # check if shifted result is empty or has zero width
+             return np.zeros((self.m, target_width), dtype=float)
+
+        try:
+            filt_1 = ndimage.prewitt(shifted)
+            filt_2 = ndimage.uniform_filter(filt_1, size=(5, 5))
+            if _SKIMAGE_AVAILABLE:
+                filt_2 = post_process(filt_2)
+            
+            # ensure correct width
+            if filt_2.shape[1] > target_width:
+                filt_2 = filt_2[:, :target_width]
+            elif filt_2.shape[1] < target_width:
+                # pad if narrower, though shift_pic should handle this by design
+                padding = np.zeros((filt_2.shape[0], target_width - filt_2.shape[1]), dtype=filt_2.dtype)
+                filt_2 = np.concatenate((filt_2, padding), axis=1)
+            return filt_2
+        except Exception:
+            return np.zeros((self.m, target_width), dtype=float)
+
     def solve_with_offset(self, user_offset: int, channel_mode: str = 'separate') -> np.ndarray:
         """
         Solves the autostereogram using a specified offset and channel handling mode.
@@ -172,60 +200,45 @@ class InteractiveSolver:
             The solved image as a NumPy array.
         """
         if user_offset <= 0:
-            if channel_mode == 'average':
-                return np.zeros((self.m, self.n), dtype=float)
-            else:
-                return np.zeros((self.m, self.n * self.c), dtype=float)
-
-        int_gap = int(user_offset)
-        effective_gap = min(int_gap, self.n)
+            # determine expected shape for zero offset based on mode
+            # for average mode, it's single channel, original width
+            # for separate mode, it's multi-channel, original width per channel
+            # however, shift_pic with 0 gap returns original image, so width isn't reduced
+            # thus, n or n*c is appropriate.
+            return np.zeros((self.m, self.n if channel_mode == 'average' else self.n * self.c), dtype=float)
+
+        effective_gap = min(int(user_offset), self.n)
         final_width_per_channel = max(0, self.n - effective_gap)
 
         if channel_mode == 'average':
             img_to_process = np.mean(self.image, axis=2) if self.color_image else self.image
-            if img_to_process.size == 0 or img_to_process.std() == 0.0:
-                return np.zeros((self.m, final_width_per_channel), dtype=float)
-            shifted: np.ndarray = shift_pic(img_to_process, effective_gap)
-            if shifted.size == 0:
-                return np.zeros((self.m, 0), dtype=float)
-            try:
-                filt_1: np.ndarray = ndimage.prewitt(shifted)
-                filt_2: np.ndarray = ndimage.uniform_filter(filt_1, size=(5, 5))
-                if _SKIMAGE_AVAILABLE:
-                    filt_2 = post_process(filt_2)
-                return filt_2
-            except Exception:
-                return np.zeros((self.m, final_width_per_channel), dtype=float)
+            return self._process_single_channel(img_to_process, effective_gap, final_width_per_channel)
 
         elif channel_mode == 'separate':
-            solution: np.ndarray = np.zeros((self.m, final_width_per_channel * self.c), dtype=float)
+            # prepare an empty array for the full solution
+            # if final_width_per_channel is 0, the solution will also have 0 width for all channels
+            solution_shape = (self.m, final_width_per_channel * self.c)
+            solution: np.ndarray = np.zeros(solution_shape, dtype=float)
+            
+            if final_width_per_channel == 0: # if no width, return empty solution early
+                return solution
+
             for i in range(self.c):
-                color: np.ndarray = self.image[:, :, i] if self.color_image else self.image
-                if not self.color_image and i > 0:
-                    break
-                if color.size == 0 or color.std() == 0.0:
-                    continue
-                shifted: np.ndarray = shift_pic(color, effective_gap)
-                if shifted.size == 0:
-                    continue
-                try:
-                    filt_1: np.ndarray = ndimage.prewitt(shifted)
-                    filt_2: np.ndarray = ndimage.uniform_filter(filt_1, size=(5, 5))
-                    if _SKIMAGE_AVAILABLE:
-                        filt_2 = post_process(filt_2)
-                    filt_m, filt_n = filt_2.shape
-                    if filt_n != final_width_per_channel:
-                        if filt_n < final_width_per_channel:
-                            continue
-                        filt_2 = filt_2[:, :final_width_per_channel]
-                        filt_n = final_width_per_channel
+                channel_data: np.ndarray = self.image[:, :, i] if self.color_image else self.image
+                if not self.color_image and i > 0: # only process once for grayscale
+                    break 
+                
+                processed_channel = self._process_single_channel(channel_data, effective_gap, final_width_per_channel)
+                
+                # ensure processed_channel has the correct dimensions before assignment
+                if processed_channel.shape[0] == self.m and processed_channel.shape[1] == final_width_per_channel:
                     start_col = i * final_width_per_channel
                     end_col = start_col + final_width_per_channel
-                    rows_to_copy = min(self.m, filt_m)
-                    solution[:rows_to_copy, start_col:end_col] = filt_2[:rows_to_copy, :]
-                except Exception:
-                    continue
+                    solution[:, start_col:end_col] = processed_channel
+                # if dimensions mismatch (e.g. due to an error in _process_single_channel returning wrong shape),
+                # that part of the solution remains zeros, which is a graceful fallback.
             return solution
 
-        else:
+        else: # unknown channel_mode
+            # fallback to a zero array with expected dimensions if mode is invalid
             return np.zeros((self.m, final_width_per_channel * self.c), dtype=float)