def test_add_text_cv2(): """ Smokescreen """ img = 255 * np.ones((512,512,3), np.uint8) text = 'Hello World!' add_text_cv2(img, text) import matplotlib.pyplot as plt plt.imshow(img) #plt.show() plt.close()
def save_classnames_in_image_sufficientpx(
rgb_img: np.ndarray,
label_img: np.ndarray,
id_to_class_name_map: Mapping[int,str],
font_color = (0,0,0),
save_to_disk: bool = False,
save_fpath: str = '',
min_conncomp_px: int = 4000,
font_scale: int = 1
):
"""
Write a classname over each connected component of a label
map as long as the connected component has a sufficiently
large number of pixels (specified as argument).
Args:
- rgb_img: Numpy array (H,W,3) representing RGB image
- label_img: Numpy array (H,W) representing label map
- id_to_class_name_map: mapping from class ID to classname
- font_color: 3-tuple representing RGB font color
- save_to_disk: whether to save image to disk
- save_fpath: absolute file path
- min_conncomp_px: minimum number of pixels to justify
placing a text label over connected component
- font_scale: scale of font text
Returns:
- rgb_img: Numpy array (H,W,3) with embedded classanmes
"""
H, W, C = rgb_img.shape
class_to_conncomps_dict = scipy_conn_comp(label_img)
for class_idx, conncomps_list in class_to_conncomps_dict.items():
for conncomp in conncomps_list:
if conncomp.sum() < min_conncomp_px:
continue
text = id_to_class_name_map[class_idx]
y,x = get_mean_mask_location(conncomp)
x -= 55 # move the text so approx. centered over mask.
x = max(0,x)
x = min(W-1,x)
# jitter location if nonconvex object mean not within its mask
if conncomp[y,x] != 1:
x,y = search_jittered_location_in_mask(x,y,conncomp)
# print(f'Class idx: {class_idx}: (x,y)=({x},{y})')
rgb_img = add_text_cv2(
rgb_img,
text,
coords_to_plot_at=(x,y),
font_color=font_color,
font_scale=font_scale,
thickness=2)
if save_to_disk:
cv2_write_rgb(save_fpath, rgb_img)
return rgb_img
def form_contained_classes_color_guide(
label_img: np.ndarray,
id_to_class_name_map: Mapping[int,str],
fname_stem: str,
save_dir: str,
save_to_disk: bool = True,
max_colors_per_col: int = 10
) -> np.ndarray:
"""
Write out an image explaining the classes inside an image.
Args:
label_img
id_to_class_name_map
fname_stem
save_dir
Returns:
palette_img: Array with cells colored with class color from palette
"""
ids_present = np.unique(label_img)
num_cols = math.ceil(len(ids_present) / max_colors_per_col)
num_rows = max_colors_per_col
palette_img = np.zeros(
(NUM_PX_PER_ROW*num_rows,NUM_PX_PER_COL * num_cols,3),
dtype=np.uint8
)
for i, labelid in enumerate(ids_present):
col_idx = i // max_colors_per_col
row_idx = i % max_colors_per_col
class_name = id_to_class_name_map[labelid]
id_img = np.ones((NUM_PX_PER_ROW,NUM_PX_PER_COL), dtype=np.uint16) * labelid
blank_img = 255 * np.ones((NUM_PX_PER_ROW,NUM_PX_PER_COL,3), dtype=np.uint8)
blank_img = add_text_cv2(blank_img, str(class_name))
color_img = convert_instance_img_to_mask_img(id_img, blank_img)
# vertical pixel start
v_start = row_idx * NUM_PX_PER_ROW
v_end = (row_idx+1) * NUM_PX_PER_ROW
# horizontal pixel start
h_start = NUM_PX_PER_COL * col_idx
h_end = h_start + NUM_PX_PER_COL
palette_img[v_start:v_end,h_start:h_end,:] = color_img
palette_save_fpath = f'{save_dir}/{fname_stem}_colors.png'
if save_to_disk:
cv2.imwrite(palette_save_fpath, palette_img[:,:,::-1])
return palette_img
def save_classnames_in_image_sufficientpx(rgb_img,
label_img,
id_to_class_name_map,
font_color=(0, 0, 0),
save_to_disk: bool = False,
save_fpath: str = '',
min_conncomp_px: int = 4000):
"""
Args:
- rgb_img
- label_img
- id_to_class_name_map: Mapping[int,str]
Returns:
-
"""
H, W, C = rgb_img.shape
class_to_conncomps_dict = scipy_conn_comp(label_img)
for class_idx, conncomps_list in class_to_conncomps_dict.items():
for conncomp in conncomps_list:
if conncomp.sum() < min_conncomp_px:
continue
text = id_to_class_name_map[class_idx]
y, x = get_mean_mask_location(conncomp)
x -= 55 # move the text so approx. centered over mask.
x = max(0, x)
x = min(W - 1, x)
# jitter location if nonconvex object mean not within its mask
if conncomp[y, x] != 1:
x, y = search_jittered_location_in_mask(x, y, conncomp)
# print(f'Class idx: {class_idx}: (x,y)=({x},{y})')
rgb_img = add_text_cv2(rgb_img,
text,
coords_to_plot_at=(x, y),
font_color=font_color,
font_scale=1,
thickness=2)
if save_to_disk:
cv2_write_rgb(save_fpath, rgb_img)
return rgb_img
def save_classnames_in_image_maxcardinality(
rgb_img,
label_img,
id_to_class_name_map,
font_color = (0,0,0),
save_to_disk: bool = False,
save_fpath: str = ''
) -> np.ndarray:
"""
Args:
rgb_img
label_img
id_to_class_name_map: Mapping[int,str]
Returns:
rgb_img
"""
H, W, C = rgb_img.shape
class_to_conncomps_dict = scipy_conn_comp(label_img)
for class_idx, conncomps_list in class_to_conncomps_dict.items():
mask_idx = find_max_cardinality_mask(conncomps_list)
maxsz_conncomp = conncomps_list[mask_idx]
text = id_to_class_name_map[class_idx]
y,x = get_mean_mask_location(maxsz_conncomp)
x -= 55
x = max(0,x)
x = min(W-1,x)
# print(f'Class idx: {class_idx}: (x,y)=({x},{y})')
rgb_img = add_text_cv2(
rgb_img,
text,
coords_to_plot_at=(x,y),
font_color=font_color,
font_scale = 1,
thickness=2)
if save_to_disk:
cv2_write_rgb(save_fpath, rgb_img)
return rgb_img
