numerical

Signed-off-by: Saurabh Misra <[email protected]>

Author: misrasaurabh1

codeflash/after_numerical.py

@@ -0,0 +1,17 @@
+def _equalize_cv(img: np.ndarray, mask: np.ndarray | None = None) -> np.ndarray:
+    histogram = cv2.calcHist([img], [0], mask, [256], (0, 256)).ravel()
+
+    # Find the first non-zero index with a numpy operation
+    i = np.flatnonzero(histogram)[0] if np.any(histogram) else 255
+
+    total = np.sum(histogram)
+    if histogram[i] == total:
+        return np.full_like(img, i)
+
+    scale = 255.0 / (total - histogram[i])
+
+    # Optimize cumulative sum and scale to generate LUT
+    cumsum_histogram = np.cumsum(histogram)
+    lut = np.clip(((cumsum_histogram - cumsum_histogram[i]) * scale)
+                  .round(), 0, 255).astype(np.uint8)
+    return sz_lut(img, lut, inplace=True)

codeflash/before_numerical.py

@@ -0,0 +1,22 @@
+def _equalize_cv(img: np.ndarray, mask: np.ndarray | None = None) -> np.ndarray:
+    histogram = cv2.calcHist([img], [0], mask, [256], (0, 256)).ravel()
+    i = 0
+    for val in histogram:
+        if val > 0:
+            break
+        i += 1
+    i = min(i, 255)
+
+    total = np.sum(histogram)
+    if histogram[i] == total:
+        return np.full_like(img, i)
+
+    scale = 255.0 / (total - histogram[i])
+    _sum = 0
+
+    lut = np.zeros(256, dtype=np.uint8)
+
+    for idx in range(i + 1, len(histogram)):
+        _sum += histogram[idx]
+        lut[idx] = clip(round(_sum * scale), np.uint8)
+    return sz_lut(img, lut, inplace=True)