Timelapse list of arrays

docs/updating.md

@@ -1631,7 +1631,7 @@ pages for more details on the input and output variable types.
 
 * pre v4.0: NA
 * post v4.0: frame_size = **pcv.visualize.time_lapse_video**(*img_list, out_filename='./time_lapse_video.mp4', fps=29.97, display=True*)
-* post v5.0: frame_size = **pcv.visualize.time_lapse_video**(*source, out_filename='./time_lapse_video.mp4', fps=29.97*)
+* post v5.0: **pcv.visualize.time_lapse_video**(*source, out_filename='./time_lapse_video.mp4', fps=29.97*)
 
 #### plantcv.watershed_segmentation
 

docs/visualize_time_lapse_video.md

@@ -4,10 +4,10 @@ This function generates and saves the time-lapse video based on a list of paths
 
 **plantcv.visualize.time_lapse_video**(*source, out_filename='./time_lapse_video.mp4', fps=29.97*)
 
-**returns** frame_size
+**returns** None
 
 - **Parameters:**
-    - source         - List of paths to the images to create the video or file path to a directory of images to use.
+    - source         - File path to a directory of images to use, list of paths to the images, or list of `numpy.ndarray` objects to create the video
     - out_filename   - Name of file to save the generated video to.
     - fps            - Frame rate (frames per second) By default fps=29.97. Commonly used values: 23.98, 24, 25, 29.97, 30, 50, 59.94, 60
 
@@ -23,8 +23,8 @@ from plantcv import plantcv as pcv
 # Note you will have to change this part on your own path
 img_directory = './path_to_images_directory/'
 
-frame_size = pcv.visualize.time_lapse_video(source=img_directory,
-                                            out_filename='./eg_time_lapse.mp4')
+pcv.visualize.time_lapse_video(source=img_directory,
+                               out_filename='./eg_time_lapse.mp4')
 ```
 
 **Video generated**

plantcv/plantcv/visualize/time_lapse_video.py

@@ -15,7 +15,8 @@ def time_lapse_video(source, out_filename='./time_lapse_video.mp4', fps=29.97):
     Parameters:
     -----------
     source         = string or list,
-        Path to a folder of images to use or list of paths to the images to create the video
+        Path to a folder of images to use, list of paths to the images,
+        or list of numpy.ndarray objects to create the video
     out_filename   = string,
         name of file to save the generated video to
     fps            = float,
@@ -30,15 +31,21 @@ def time_lapse_video(source, out_filename='./time_lapse_video.mp4', fps=29.97):
     img_list = source
     if isinstance(source, str):
         img_list = read_dataset(source, sort=True)
+    # for file paths read the images
+    if isinstance(img_list[0], str):
+        imgs = []
+        list_r = []
+        list_c = []
+        for file in img_list:
+            img = iio.imread(file)
+            list_r.append(img.shape[0])
+            list_c.append(img.shape[1])
+            imgs.append(img)
+    else:
+        list_c = [img.shape[0] for img in img_list]
+        list_r = [img.shape[1] for img in img_list]
+        imgs = img_list
 
-    imgs = []
-    list_r = []
-    list_c = []
-    for file in img_list:
-        img = iio.imread(file)
-        list_r.append(img.shape[0])
-        list_c.append(img.shape[1])
-        imgs.append(img)
     max_c, max_r = np.max(list_c), np.max(list_r)
 
     # use the largest size of the images as the frame size
@@ -54,5 +61,3 @@ def time_lapse_video(source, out_filename='./time_lapse_video.mp4', fps=29.97):
 
     frames = [resize(img, frame_size, interpolation=None) for img in imgs]
     iio.imwrite(out_filename, frames, plugin="FFMPEG", fps=fps, codec="libx264")
-
-    return frame_size

tests/plantcv/visualize/test_time_lapse_video.py

@@ -4,7 +4,7 @@
 from plantcv.plantcv.visualize.time_lapse_video import time_lapse_video
 
 
-def test_plantcv_visualize_time_lapse_video_list_input(tmpdir):
+def test_plantcv_visualize_time_lapse_video_path_list_input(tmpdir):
     """Test for PlantCV."""
     # Generate 3 test images and saved in tmpdir
     list_im = []
@@ -17,7 +17,21 @@ def test_plantcv_visualize_time_lapse_video_list_input(tmpdir):
         list_im.append(img_i_path)
 
     vid_name = os.path.join(tmpdir, 'test_time_lapse_video.mp4')
-    _ = time_lapse_video(source=list_im, out_filename=vid_name, fps=29.97)
+    time_lapse_video(source=list_im, out_filename=vid_name, fps=29.97)
+    assert os.path.exists(vid_name) and os.path.getsize(vid_name) > 100
+
+
+def test_plantcv_visualize_time_lapse_video_array_list_input(tmpdir):
+    """Test for PlantCV."""
+    # Generate 3 test images and saved in tmpdir
+    list_im = []
+    for _ in range(3):
+        temp_img = np.random.rand(3, 3)
+        min_, max_ = np.nanmin(temp_img), np.nanmax(temp_img)
+        temp_img = np.interp(temp_img, (min_, max_), (0, 255)).astype('uint8')
+        list_im.append(temp_img)
+    vid_name = os.path.join(tmpdir, 'test_time_lapse_video.mp4')
+    time_lapse_video(source=list_im, out_filename=vid_name, fps=29.97)
     assert os.path.exists(vid_name) and os.path.getsize(vid_name) > 100
 
 
@@ -32,7 +46,7 @@ def test_plantcv_visualize_time_lapse_video_str_input(tmpdir):
         cv2.imwrite(img_i_path, temp_img)
 
     vid_name = os.path.join(tmpdir, 'test_time_lapse_video.mp4')
-    _ = time_lapse_video(source=str(tmpdir), out_filename=vid_name, fps=29.97)
+    time_lapse_video(source=str(tmpdir), out_filename=vid_name, fps=29.97)
     assert os.path.exists(vid_name) and os.path.getsize(vid_name) > 100
 
 
@@ -50,7 +64,7 @@ def test_plantcv_visualize_time_lapse_video_different_img_sizes_warns(tmpdir, ca
         list_im.append(img_i_path)
 
     vid_name = os.path.join(tmpdir, 'test_time_lapse_video.mp4')
-    _ = time_lapse_video(source=list_im, out_filename=vid_name, fps=29.97)
+    time_lapse_video(source=list_im, out_filename=vid_name, fps=29.97)
     _, err = capsys.readouterr()
 
     assert "Warning" in err and os.path.exists(vid_name)