Jsumner

littlecv/littlecv/__init__.py

@@ -10,7 +10,7 @@
 from littlecv.littlecv.plot_image import plot_image
 
 __all__ = [
-    "analyze_size",
+    "size",
     "Outputs",
     "readimage",
     "rgb2gray_lab",

littlecv/littlecv/plot_image.py

@@ -25,13 +25,3 @@ def plot_image(img, cmap=None, plot=True, **kwargs):
             plt.figure()
             plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
             plt.show()
-
-        elif cmap is None and len(dimensions) == 2:
-            plt.figure()
-            plt.imshow(img, cmap="gray")
-            plt.show()
-
-        elif cmap is not None and len(dimensions) == 2:
-            plt.figure()
-            plt.imshow(img, cmap=cmap)
-            plt.show()

littlecv/littlecv/readimage.py

@@ -21,13 +21,6 @@ def readimage(filename, plot=True):
     """
 
     img = cv2.imread(filename, -1)
-    # Default to drop alpha channel if user doesn't specify 'rgba'
-    if len(np.shape(img)) == 3 and np.shape(img)[2] == 4:
-        img = cv2.imread(filename)
-
-    if img is None:
-        raise RuntimeError("Failed to open" + filename)
-
     # Split path from filename
     path, img_name = os.path.split(filename)
     # we are always going to debug in this dummy version

littlecv/littlecv/threshold_binary.py

@@ -2,31 +2,19 @@
 from littlecv.littlecv.plot_image import plot_image
 
 
-def binary(gray_img, threshold, object_type="light", plot=True):
+def binary(gray_img, threshold, plot=True):
     """Creates a binary image from a grayscale image based on the threshold value.
 
     Inputs:
     gray_img     = numpy.ndarray, Grayscale image data
     threshold    = int, Threshold value (0-255)
-    object_type  = str, "light" or "dark" (default: "light")
-                   - If object is lighter than the background then standard thresholding is done
-                   - If object is darker than the background then inverse thresholding is done
     plot         = bool, lazy version of pcv.params.debug
 
     Returns:
     bin_img      = numpy.ndarray, Thresholded binary image
     """
-    # Set the threshold method
-    threshold_method = ""
-    if object_type.upper() == "LIGHT":
-        threshold_method = cv2.THRESH_BINARY
-    elif object_type.upper() == "DARK":
-        threshold_method = cv2.THRESH_BINARY_INV
-    else:
-        RuntimeError('Object type ' + str(object_type) + ' is not "light" or "dark"!')
-
     # Threshold the image
-    _, bin_img = cv2.threshold(gray_img, threshold, 255, threshold_method)
+    _, bin_img = cv2.threshold(gray_img, threshold, 255, cv2.THRESH_BINARY)
 
     plot_image(bin_img, plot=plot)
 

readme.md

@@ -30,7 +30,7 @@ The final piece of the `workflow.ipynb` checks the area phenotype, when that che
 #### Steps
 
 * 1: Open a branch with your name (`git checkout -b first_last`).
-* 2: Check current behavior either in jupyter (run `jupyter-lab` and use GUI) or with the tests (run `py.test --cov littlecv`)
+* 2: Check current behavior either in jupyter (run `jupyter-lab` and use GUI) or with the tests (run `py.test --cov littlecv --cov-report="term-missing"`)
 * 3: Edit `littlecv` code.
 * 4: Restart Jupyter Kernel and run all cells again or rerun tests. Make edits and test them until tests pass.
 * 5: Commit your changes (`git commit -m "your commit message here"`). This is like saving the files on your branch. How often you do this and how granular commits are is mostly up to you.

tests/test_littlecv.py

@@ -1,14 +1,69 @@
 import os
+import cv2
+import matplotlib
+import numpy as np
 from littlecv import littlecv as lcv
 
 
+# disable plotting
+matplotlib.use("Template")
+
+
+def test_readimage():
+    """Test littlecv readimage"""
+    data = os.path.join(os.path.dirname(os.path.abspath(__file__)), "testdata/plant_cropped.png")
+    img, _, _ = lcv.readimage(filename=data, plot=False)
+    assert img.shape == (2000, 1600, 3)
+
+
+def test_plot_image():
+    """Test littlecv plot_image"""
+    data = os.path.join(os.path.dirname(os.path.abspath(__file__)), "testdata/plant_cropped.png")
+    img, _, _ = lcv.readimage(filename=data, plot=False)
+    lcv.plot_image(img)
+    assert True
+
+
+def test_rgb2gray():
+    """Test littlecv rgb to grayscale conversion"""
+    data = os.path.join(os.path.dirname(os.path.abspath(__file__)), "testdata/plant_cropped.png")
+    img, _, _ = lcv.readimage(filename=data, plot=False)
+    b_img = lcv.rgb2gray_lab(rgb_img=img, channel='b', plot=False)
+    assert b_img.shape == (2000, 1600)
+
+
+def test_binary():
+    """Test littlecv binary threshold"""
+    data = os.path.join(os.path.dirname(os.path.abspath(__file__)), "testdata/plant_cropped.png")
+    img, _, _ = lcv.readimage(filename=data, plot=False)
+    b_img = lcv.rgb2gray_lab(rgb_img=img, channel='b', plot=False)
+    thresh_mask = lcv.binary(gray_img=b_img, threshold=130, plot=False)
+    assert all(np.unique(thresh_mask) == [0, 255])
+
+
+def test_fill():
+    """Test littlecv fill"""
+    data = os.path.join(os.path.dirname(os.path.abspath(__file__)), "testdata/plant_cropped.png")
+    img, _, _ = lcv.readimage(filename=data, plot=False)
+    b_img = lcv.rgb2gray_lab(rgb_img=img, channel='b', plot=False)
+    thresh_mask = lcv.binary(gray_img=b_img, threshold=130, plot=False)
+    fill_mask = lcv.fill(thresh_mask, size=1000, plot=False)
+    assert np.sum(fill_mask) < np.sum(thresh_mask)
+
+
+def test_area():
+    """Test littlecv size"""
+    d = np.array([[0, 0, 1, 0, 0], [0, 1, 1, 1, 0], [0,1,1,1,0], [0, 0, 1, 0, 0]]).astype("uint8")
+    _, out = lcv.size(img=d, mask=d)
+    assert out.observations["littlecv_test"]["area"]["value"] == np.sum(d)
+
+
 def test_littlecv_workflow():
     """Test for your edit"""
     data = os.path.join(os.path.dirname(os.path.abspath(__file__)), "testdata/plant_cropped.png")
     img, _, _ = lcv.readimage(filename=data, plot=False)
     b_img = lcv.rgb2gray_lab(rgb_img=img, channel='b', plot=False)
-    thresh_mask = lcv.binary(gray_img=b_img, threshold=130, object_type='light', plot=False)
+    thresh_mask = lcv.binary(gray_img=b_img, threshold=130, plot=False)
     fill_mask = lcv.fill(thresh_mask, size=1000, plot=False)
     _, outputs = lcv.size(img=img, mask=fill_mask, plot=False)
-
     assert outputs.observations["littlecv_test"]["area"]["value"] == 198568.0