app.py simplify encode_image()

app.py

@@ -73,31 +73,12 @@ def encode_image(data: np.ndarray, quality: int = 85) -> dict:
     if np.any(np.isnan(data)) or np.any(np.isinf(data)):
         raise ValueError("Input array contains NaN or Inf")
 
-    # Data Type Handling and Scaling
-    if np.issubdtype(data.dtype, np.floating):
-        # Floating-point input: Use cv2.normalize (as before)
-        if np.min(data) == np.max(data):
-            scaled_data = np.full(data.shape, 0, dtype=np.uint8) if np.min(data) <=0 else np.full(data.shape, 255, dtype=np.uint8)
-        else:
-            scaled_data = cv2.normalize(data, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)
-
-    elif np.issubdtype(data.dtype, np.integer):
-        # Integer input
-        if data.dtype == np.uint8:
-            # uint8: Skip normalization *unless* all values are the same
-            if np.min(data) == np.max(data):
-                scaled_data = np.full(data.shape, 0, dtype=np.uint8) if np.min(data) == 0 else np.full(data.shape, 255, dtype=np.uint8)
-            else:
-                scaled_data = np.ascontiguousarray(data) # Already uint8, just ensure contiguity
-        else:
-            # Other integer types: Convert to float64 for safe normalization
-            scaled_data = cv2.normalize(data.astype(np.float64), None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)
+    # Normalize and convert to uint8
+    if np.issubdtype(data.dtype, np.floating) or np.issubdtype(data.dtype, np.integer):
+        scaled_data = cv2.normalize(data, None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8)
     else:
         raise TypeError("Input array must have a floating-point or integer data type.")
 
-    scaled_data = np.ascontiguousarray(scaled_data)
-
-
     # JPEG Encoding (with quality control and error handling)
     try:
         retval, buf = cv2.imencode(".jpg", scaled_data, [int(cv2.IMWRITE_JPEG_QUALITY), quality])