remove max_val param from threshold.mean()

docs/mean_threshold.md

@@ -8,7 +8,7 @@ This local threshold depends on the local average, computed in a squared portion
 
 In the mean adaptive threshold, the local average is the average of the pixel values in the block.
 
-**plantcv.threshold.mean**(*gray_img, block_size, offset, object_type="light", max_value=255*)
+**plantcv.threshold.mean**(*gray_img, block_size, offset, object_type="light"*)
 
 **returns** thresholded/binary image
 
@@ -19,10 +19,9 @@ In the mean adaptive threshold, the local average is the average of the pixel va
  A negative offset sets the local threshold above the local average.
  - object_type - "light" or "dark" (default: "light").
  - "light" for objects brighter than the background, sets the pixels above
- the local threshold to max_value and the pixels below to 0.
+ the local threshold to 255 and the pixels below to 0.
  - "dark" for objects darker than the background, sets the pixels below the
- local threshold to max_value and the pixels above to 0.
- - max_value - Value to apply above threshold (default: 255 = white)
+ local threshold to 255 and the pixels above to 0.
 - **Context:**
  - Useful for unevenly illuminated images
 
@@ -41,13 +40,13 @@ pcv.params.debug = "plot"
 
 # Adaptive threshold with different parameters
 threshold_mean1 = pcv.threshold.mean(gray_img=gray_img, block_size=250, offset=25,
- object_type='dark', max_value=255)
+ object_type='dark')
 
 threshold_mean2 = pcv.threshold.mean(gray_img=gray_img, block_size=15, offset=5,
- object_type='dark', max_value=255)
+ object_type='dark')
 
 threshold_mean3 = pcv.threshold.mean(gray_img=gray_img, block_size=2000, offset=25,
- object_type='dark', max_value=255)
+ object_type='dark')
 ```
 
 **Thresholded image**

docs/updating.md

@@ -1047,7 +1047,7 @@ pages for more details on the input and output variable types.
 
 * pre v3.0dev2: NA
 * post v3.0dev2: bin_img = **plantcv.threshold.mean**(*gray_img, max_value, object_type="light"*)
-* post v4.0: bin_img = **plantcv.threshold.mean**(*gray_img, block_size, offset, object_type="light", max_value=255*)
+* post v4.0: bin_img = **plantcv.threshold.mean**(*gray_img, block_size, offset, object_type="light"*)
 
 #### plantcv.threshold.otsu
 

plantcv/plantcv/threshold/threshold_methods.py

@@ -104,7 +104,7 @@ def gaussian(gray_img, block_size, offset, object_type="light", max_value=255):
 
 
 # Mean adaptive threshold
-def mean(gray_img, block_size, offset, object_type="light", max_value=255):
+def mean(gray_img, block_size, offset, object_type="light"):
  """Creates a binary image from a grayscale image based on the mean adaptive threshold method.
 
  Adaptive thresholds use a threshold value that varies across the image.
@@ -120,10 +120,9 @@ def mean(gray_img, block_size, offset, object_type="light", max_value=255):
  A negative offset sets the local threshold above the local average.
  object_type = "light" or "dark" (default: "light")
  - "light" (for objects brighter than the background) sets the pixels above
- the local threshold to max_value and the pixels below to 0.
+ the local threshold to 255 and the pixels below to 0.
  - "dark" (for objects darker than the background) sets the pixels below the
- local threshold to max_value and the pixels above to 0.
- max_value = Value to apply above threshold (default: 255 = white)
+ local threshold to 255 and the pixels above to 0.
 
  Returns:
  bin_img = Thresholded, binary image
@@ -132,7 +131,6 @@ def mean(gray_img, block_size, offset, object_type="light", max_value=255):
  :param block_size: int
  :param offset: float
  :param object_type: str
- :param max_value: int
  :return bin_img: numpy.ndarray
  """
  # Set the threshold method
@@ -146,7 +144,7 @@ def mean(gray_img, block_size, offset, object_type="light", max_value=255):
 
  params.device += 1
 
- bin_img = _call_adaptive_threshold(gray_img, block_size, offset, max_value, cv2.ADAPTIVE_THRESH_MEAN_C,
+ bin_img = _call_adaptive_threshold(gray_img, block_size, offset, 255, cv2.ADAPTIVE_THRESH_MEAN_C,
  threshold_method, "_mean_threshold_")
 
  return bin_img

tests/plantcv/threshold/test_threshold_methods.py

@@ -47,7 +47,7 @@ def test_mean(objtype, size, threshold_test_data):
  """Test for PlantCV."""
  # Read in test data
  gray_img = cv2.imread(threshold_test_data.small_gray_img, -1)
- binary_img = mean(gray_img=gray_img, block_size=size, offset=2, object_type=objtype, max_value=255)
+ binary_img = mean(gray_img=gray_img, block_size=size, offset=2, object_type=objtype)
  # Assert that the output image has the dimensions of the input image and is binary
  assert gray_img.shape == binary_img.shape and np.array_equal(np.unique(binary_img), np.array([0, 255]))
 
@@ -57,15 +57,15 @@ def test_mean_incorrect_object_type(threshold_test_data):
  # Read in test data
  gray_img = cv2.imread(threshold_test_data.small_gray_img, -1)
  with pytest.raises(RuntimeError):
- _ = mean(gray_img=gray_img, block_size=11, offset=2, object_type="lite", max_value=255)
+ _ = mean(gray_img=gray_img, block_size=11, offset=2, object_type="lite")
 
 
 def test_mean_incorrect_block_size(threshold_test_data):
  """Test for PlantCV."""
  # Read in test data
  gray_img = cv2.imread(threshold_test_data.small_gray_img, -1)
  with pytest.raises(RuntimeError):
- _ = mean(gray_img=gray_img, block_size=1, offset=2, object_type="dark", max_value=255)
+ _ = mean(gray_img=gray_img, block_size=1, offset=2, object_type="dark")
 
 
 @pytest.mark.parametrize("objtype", ["dark", "light"])