Merge pull request #1578 from danforthcenter/1575-image-tiling

1575 image tiling

Author: nfahlgren

docs/img/documentation_images/visualize_tile/Tile_1.jpg

@@ -1344,6 +1344,11 @@ pages for more details on the input and output variable types.
 * pre v4.0: NA
 * post v4.0: fig, ax = **pcv.visualize.pixel_scatter_plot**(*paths_to_imgs, x_channel, y_channel*)
 
+#### plantcv.visualize.tile
+
+* pre v4.4: NA
+* post v4.4: tile_img = **pcv.visualize.tile**(*img_list, ncol*)
+
 #### plantcv.visualize.time_lapse_video
 
 * pre v4.0: NA

docs/img/documentation_images/visualize_tile/Tile_2.jpg

@@ -0,0 +1,46 @@
+## Visualize composite of image tiles
+
+This is a plotting method used to examine several output versions, such as from different model fits with varying parameters, all at once.
+
+**plantcv.visualize.tile**(*images, ncol*)
+
+**returns** comp_img
+
+- **Parameters:**
+    - images - A list of numpy arrays to tile into a composite.
+    - ncol - Number of columns in composite output. Number of rows is calculated from the number of input images.
+
+- **Example use:**
+    - Below
+
+
+```python
+
+from plantcv import plantcv as pcv
+import os
+
+# Read in a list of images
+images = []
+for i in os.listdir("./test_images/"):
+    images.append(pcv.readimage("./test_images/"+i)[0])
+
+# Examine all images at once
+composite = pcv.visualize.tile(images=images, ncol=2)
+
+```
+
+**Input images**
+
+![Screenshot](img/documentation_images/visualize_tile/Tile_1.jpg)
+
+![Screenshot](img/documentation_images/visualize_tile/Tile_2.jpg)
+
+![Screenshot](img/documentation_images/visualize_tile/Tile_3.jpg)
+
+![Screenshot](img/documentation_images/visualize_tile/Tile_4.jpg)
+
+**Output**
+
+![Screenshot](img/documentation_images/visualize_tile/Tile_output.jpg)
+
+**Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/main/plantcv/plantcv/visualize/tile.py)

docs/img/documentation_images/visualize_tile/Tile_3.jpg

@@ -209,6 +209,7 @@ nav:
           - 'Object Sizes': visualize_obj_sizes.md
           - 'Pixel Scatter Plot': 'visualize_pixel_scatter_vis.md'
           - 'Pseudocolor': visualize_pseudocolor.md
+          - 'Tile': visualize_tile.md
           - 'Time Lapse Video': visualize_time_lapse_video.md
       - 'Watershed Segmentation': watershed.md
       - 'White balance': white_balance.md

docs/img/documentation_images/visualize_tile/Tile_4.jpg

@@ -11,7 +11,8 @@
 from plantcv.plantcv.visualize.hyper_histogram import hyper_histogram
 from plantcv.plantcv.visualize.pixel_scatter_vis import pixel_scatter_plot
 from plantcv.plantcv.visualize.chlorophyll_fluorescence import chlorophyll_fluorescence
+from plantcv.plantcv.visualize.tile import tile
 
 __all__ = ["pseudocolor", "colorize_masks", "histogram", "colorspaces", "auto_threshold_methods",
            "overlay_two_imgs", "colorize_label_img", "obj_size_ecdf", "obj_sizes", "hyper_histogram",
-           "pixel_scatter_plot", "time_lapse_video", "chlorophyll_fluorescence"]
+           "pixel_scatter_plot", "time_lapse_video", "chlorophyll_fluorescence", "tile"]

docs/img/documentation_images/visualize_tile/Tile_output.jpg

@@ -0,0 +1,88 @@
+# Tile output images in a plot to visualize all at once
+
+import cv2
+import numpy as np
+import os
+from plantcv.plantcv import params
+from plantcv.plantcv._debug import _debug
+
+
+def _row_resize(row, ncol):
+    """Resizes and concatenates objects in a row.
+
+    Parameters
+    ----------
+    row : list of numpy.ndarray
+        List of images to concatenate.
+    ncol : int
+        Number of columns in desired composite image.
+
+    Returns
+    -------
+    numpy.ndarray
+        Image concatenated horizontally.
+    """
+    h_min = min(img.shape[0] for img in row)
+    # Resizing each image so they're the same
+    row_resize = [cv2.resize(img, (int(img.shape[1] * h_min / img.shape[0]), h_min),
+                             interpolation=cv2.INTER_CUBIC) for img in row]
+    # Add empty images to the end of the row so things still stay the same size
+    while len(row_resize) < ncol:
+        row_resize.append(np.zeros(row_resize[0].shape, dtype=np.uint8))
+    # Concatenate horizontally
+    return cv2.hconcat(row_resize)
+
+
+# Same as _row_resize but for columns
+def _col_resize(col):
+    """Resized and concatenates objects in a column.
+
+    Parameters
+    ----------
+    col : list of numpy.ndarray
+        List of images to concatenate vertically.
+
+    Returns
+    -------
+    numpy.ndarray
+        Image concatenated vertically.
+    """
+    w_min = min(img.shape[1] for img in col)
+    col_resize = [cv2.resize(img, (w_min, int(img.shape[0] * w_min / img.shape[1])),
+                             interpolation=cv2.INTER_CUBIC) for img in col]
+    return cv2.vconcat(col_resize)
+
+
+# The function that does the tiling
+def tile(images, ncol):
+    """Tile a list of images into a composite with given dimensions.
+
+    Parameters
+    ----------
+    images : list of numpy.ndarray
+        List of images to tile.
+    ncol : int
+        Number of columns in desired composite image.
+
+    Returns
+    -------
+    numpy.ndarray
+        Tiled composite image.
+    """
+    # Increment the device counter
+    params.device += 1
+
+    # Calculate number of rows - always rounds up
+    nrow = int(len(images) / ncol) + (len(images) % ncol > 0)
+    tracker = 0
+    mat = []
+    for _ in range(nrow):
+        row = []
+        for _ in range(ncol):
+            if tracker <= (len(images) - 1):
+                row.append(images[tracker])
+            tracker += 1
+        mat.append(_row_resize(row, ncol))
+    comp_img = _col_resize(mat)
+    _debug(visual=comp_img, filename=os.path.join(params.debug_outdir, f"{params.device}_tile.png"))
+    return comp_img

docs/updating.md

@@ -22,6 +22,10 @@ def __init__(self):
         self.small_composed_contours_file = os.path.join(self.datadir, "setaria_small_plant_composed_contours.npz")
         # PlantCV hyperspectral image object
         self.hsi_file = os.path.join(self.datadir, "hsi.pkl")
+        # Tile image directory
+        self.tile_dir = os.path.join(self.datadir, "visualize_tile/")
+        # Tile image output
+        self.tile_out = os.path.join(self.datadir, "Tile_output.jpg")
 
     @staticmethod
     def load_hsi(pkl_file):

docs/visualize_tile.md

@@ -0,0 +1,12 @@
+import cv2
+from plantcv.plantcv.visualize import tile
+
+
+def test_tile(visualize_test_data):
+    """Test for PlantCV."""
+    # Read in image list
+    images = []
+    for _ in range(4):
+        images.append(cv2.imread(visualize_test_data.small_rgb_img))
+    composite = tile(images=images, ncol=3)
+    assert composite.shape == (670, 1200, 3)