Tony Lian
Improve error handling in parsing
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import ast
from matplotlib.patches import Polygon
from matplotlib.collections import PatchCollection
import matplotlib.pyplot as plt
import numpy as np
import warnings
import inflect
import gradio as gr
p = inflect.engine()
# user_error = ValueError
user_error = gr.Error
img_dir = "imgs"
objects_text = "Objects: "
bg_prompt_text = "Background prompt: "
bg_prompt_text_no_trailing_space = bg_prompt_text.rstrip()
neg_prompt_text = "Negative prompt: "
neg_prompt_text_no_trailing_space = neg_prompt_text.rstrip()
# h, w
box_scale = (512, 512)
size = box_scale
size_h, size_w = size
print(f"Using box scale: {box_scale}")
def parse_input(text=None, no_input=False):
warnings.warn("Parsing input without negative prompt is deprecated.")
if not text:
if no_input:
raise user_error(f"No input parsed in \"{text}\".")
text = input("Enter the response: ")
if objects_text in text:
text = text.split(objects_text)[1]
text_split = text.split(bg_prompt_text_no_trailing_space)
if len(text_split) == 2:
gen_boxes, bg_prompt = text_split
elif len(text_split) == 1:
if no_input:
raise user_error(f"Invalid input (no background prompt): {text}")
gen_boxes = text
bg_prompt = ""
while not bg_prompt:
# Ignore the empty lines in the response
bg_prompt = input("Enter the background prompt: ").strip()
if bg_prompt_text_no_trailing_space in bg_prompt:
bg_prompt = bg_prompt.split(bg_prompt_text_no_trailing_space)[1]
else:
raise user_error(f"Invalid input (possibly multiple background prompts): {text}")
try:
gen_boxes = ast.literal_eval(gen_boxes)
except SyntaxError as e:
# Sometimes the response is in plain text
if "No objects" in gen_boxes:
gen_boxes = []
else:
raise e
bg_prompt = bg_prompt.strip()
return gen_boxes, bg_prompt
def parse_input_with_negative(text=None, no_input=False):
# no_input: should not request interactive input
if not text:
if no_input:
raise user_error(f"No input parsed in \"{text}\".")
text = input("Enter the response: ")
if objects_text in text:
text = text.split(objects_text)[1]
text_split = text.split(bg_prompt_text_no_trailing_space)
if len(text_split) == 2:
gen_boxes, text_rem = text_split
elif len(text_split) == 1:
if no_input:
raise user_error(f"Invalid input (no background prompt): {text}")
gen_boxes = text
text_rem = ""
while not text_rem:
# Ignore the empty lines in the response
text_rem = input("Enter the background prompt: ").strip()
if bg_prompt_text_no_trailing_space in text_rem:
text_rem = text_rem.split(bg_prompt_text_no_trailing_space)[1]
else:
raise user_error(f"Invalid input (possibly multiple background prompts): {text}")
text_split = text_rem.split(neg_prompt_text_no_trailing_space)
if len(text_split) == 2:
bg_prompt, neg_prompt = text_split
elif len(text_split) == 1:
bg_prompt = text_rem
# Negative prompt is optional: if it's not provided, we default to empty string
neg_prompt = ""
if not no_input:
# Ignore the empty lines in the response
neg_prompt = input("Enter the negative prompt: ").strip()
if neg_prompt_text_no_trailing_space in neg_prompt:
neg_prompt = neg_prompt.split(neg_prompt_text_no_trailing_space)[1]
else:
raise user_error(f"Invalid input (possibly multiple negative prompts): {text}")
try:
gen_boxes = ast.literal_eval(gen_boxes)
except SyntaxError as e:
# Sometimes the response is in plain text
if "No objects" in gen_boxes or gen_boxes.strip() == "":
gen_boxes = []
else:
raise e
bg_prompt = bg_prompt.strip()
neg_prompt = neg_prompt.strip()
# LLM may return "None" to mean no negative prompt provided.
if neg_prompt == "None":
neg_prompt = ""
return gen_boxes, bg_prompt, neg_prompt
def filter_boxes(gen_boxes, scale_boxes=True, ignore_background=True, max_scale=3):
if gen_boxes is None:
return []
if len(gen_boxes) == 0:
return []
box_dict_format = False
gen_boxes_new = []
for gen_box in gen_boxes:
if isinstance(gen_box, dict):
if not gen_box['bounding_box']:
continue
name, [bbox_x, bbox_y, bbox_w, bbox_h] = gen_box['name'], gen_box['bounding_box']
box_dict_format = True
else:
if not gen_box[1]:
continue
name, [bbox_x, bbox_y, bbox_w, bbox_h] = gen_box
if bbox_w <= 0 or bbox_h <= 0:
# Empty boxes
continue
if ignore_background:
if (bbox_w >= size[1] and bbox_h >= size[0]) or bbox_x > size[1] or bbox_y > size[0]:
# Ignore the background boxes
continue
if bbox_x < 0 or bbox_y < 0 or bbox_x + bbox_w > size[1] or bbox_y + bbox_h > size[0]:
# Out of bounds boxes exist: we need to scale and shift all the boxes
print(f"**Some boxes are out of bounds: {gen_box}, scaling all the boxes to fit**")
scale_boxes = True
gen_boxes_new.append(gen_box)
gen_boxes = gen_boxes_new
if len(gen_boxes) == 0:
return []
filtered_gen_boxes = []
if box_dict_format:
# For compatibility
bbox_left_x_min = min([gen_box['bounding_box'][0] for gen_box in gen_boxes])
bbox_right_x_max = max([gen_box['bounding_box'][0] + gen_box['bounding_box'][2] for gen_box in gen_boxes])
bbox_top_y_min = min([gen_box['bounding_box'][1] for gen_box in gen_boxes])
bbox_bottom_y_max = max([gen_box['bounding_box'][1] + gen_box['bounding_box'][3] for gen_box in gen_boxes])
else:
bbox_left_x_min = min([gen_box[1][0] for gen_box in gen_boxes])
bbox_right_x_max = max([gen_box[1][0] + gen_box[1][2] for gen_box in gen_boxes])
bbox_top_y_min = min([gen_box[1][1] for gen_box in gen_boxes])
bbox_bottom_y_max = max([gen_box[1][1] + gen_box[1][3] for gen_box in gen_boxes])
# All boxes are empty
if (bbox_right_x_max - bbox_left_x_min) == 0:
return []
# Used if scale_boxes is True
shift = -bbox_left_x_min
# Make sure the boxes fit horizontally and vertically
scale_w = size_w / (bbox_right_x_max - bbox_left_x_min)
scale_h = size_h / (bbox_bottom_y_max - bbox_top_y_min)
scale = min(scale_w, scale_h, max_scale)
for gen_box in gen_boxes:
if box_dict_format:
name, [bbox_x, bbox_y, bbox_w, bbox_h] = gen_box['name'], gen_box['bounding_box']
else:
name, [bbox_x, bbox_y, bbox_w, bbox_h] = gen_box
if scale_boxes:
# Vertical: move the boxes if out of bound
# Horizontal: move and scale the boxes so it spans the horizontal line
bbox_x = (bbox_x + shift) * scale
bbox_y = bbox_y * scale
bbox_w, bbox_h = bbox_w * scale, bbox_h * scale
# TODO: verify this makes the y center not moving
bbox_y_offset = 0
if bbox_top_y_min * scale + bbox_y_offset < 0:
bbox_y_offset -= bbox_top_y_min * scale
if bbox_bottom_y_max * scale + bbox_y_offset >= size_h:
bbox_y_offset -= bbox_bottom_y_max * scale - size_h
bbox_y += bbox_y_offset
if bbox_y < 0:
bbox_y, bbox_h = 0, bbox_h - bbox_y
name = name.rstrip(".")
bounding_box = (int(np.round(bbox_x)), int(np.round(bbox_y)), int(np.round(bbox_w)), int(np.round(bbox_h)))
if box_dict_format:
gen_box = {
'name': name,
'bounding_box': bounding_box
}
else:
gen_box = (name, bounding_box)
filtered_gen_boxes.append(gen_box)
return filtered_gen_boxes
def draw_boxes(anns):
ax = plt.gca()
ax.set_autoscale_on(False)
polygons = []
color = []
for ann in anns:
c = (np.random.random((1, 3))*0.6+0.4)
[bbox_x, bbox_y, bbox_w, bbox_h] = ann['bbox']
poly = [[bbox_x, bbox_y], [bbox_x, bbox_y+bbox_h],
[bbox_x+bbox_w, bbox_y+bbox_h], [bbox_x+bbox_w, bbox_y]]
np_poly = np.array(poly).reshape((4, 2))
polygons.append(Polygon(np_poly))
color.append(c)
# print(ann)
name = ann['name'] if 'name' in ann else str(ann['category_id'])
ax.text(bbox_x, bbox_y, name, style='italic',
bbox={'facecolor': 'white', 'alpha': 0.7, 'pad': 5})
p = PatchCollection(polygons, facecolor='none',
edgecolors=color, linewidths=2)
ax.add_collection(p)
def show_boxes(gen_boxes, bg_prompt=None, neg_prompt=None, ind=None, show=False):
if len(gen_boxes) == 0:
return
if isinstance(gen_boxes[0], dict):
anns = [{'name': gen_box['name'], 'bbox': gen_box['bounding_box']}
for gen_box in gen_boxes]
else:
anns = [{'name': gen_box[0], 'bbox': gen_box[1]} for gen_box in gen_boxes]
# White background (to allow line to show on the edge)
I = np.ones((size[0]+4, size[1]+4, 3), dtype=np.uint8) * 255
plt.imshow(I)
plt.axis('off')
if bg_prompt is not None:
ax = plt.gca()
ax.text(0, 0, bg_prompt + f"(Neg: {neg_prompt})" if neg_prompt else bg_prompt, style='italic',
bbox={'facecolor': 'white', 'alpha': 0.7, 'pad': 5})
c = (np.zeros((1, 3)))
[bbox_x, bbox_y, bbox_w, bbox_h] = (0, 0, size[1], size[0])
poly = [[bbox_x, bbox_y], [bbox_x, bbox_y+bbox_h],
[bbox_x+bbox_w, bbox_y+bbox_h], [bbox_x+bbox_w, bbox_y]]
np_poly = np.array(poly).reshape((4, 2))
polygons = [Polygon(np_poly)]
color = [c]
p = PatchCollection(polygons, facecolor='none',
edgecolors=color, linewidths=2)
ax.add_collection(p)
draw_boxes(anns)
if show:
plt.show()
def show_masks(masks):
masks_to_show = np.zeros((*size, 3), dtype=np.float32)
for mask in masks:
c = (np.random.random((3,))*0.6+0.4)
masks_to_show += mask[..., None] * c[None, None, :]
plt.imshow(masks_to_show)
plt.savefig(f"{img_dir}/masks.png")
plt.show()
plt.clf()
def convert_box(box, height, width):
# box: x, y, w, h (in 512 format) -> x_min, y_min, x_max, y_max
x_min, y_min = box[0] / width, box[1] / height
w_box, h_box = box[2] / width, box[3] / height
x_max, y_max = x_min + w_box, y_min + h_box
return x_min, y_min, x_max, y_max
def convert_spec(spec, height, width, include_counts=True, verbose=False):
# Infer from spec
prompt, gen_boxes, bg_prompt = spec['prompt'], spec['gen_boxes'], spec['bg_prompt']
# This ensures the same objects appear together because flattened `overall_phrases_bboxes` should EXACTLY correspond to `so_prompt_phrase_box_list`.
gen_boxes = sorted(gen_boxes, key=lambda gen_box: gen_box[0])
gen_boxes = [(name, convert_box(box, height=height, width=width)) for name, box in gen_boxes]
# NOTE: so phrase should include all the words associated to the object (otherwise "an orange dog" may be recognized as "an orange" by the model generating the background).
# so word should have one token that includes the word to transfer cross attention (the object name).
# Currently using the last word of the object name as word.
if bg_prompt:
so_prompt_phrase_word_box_list = [(f"{bg_prompt} with {name}", name, name.split(" ")[-1], box) for name, box in gen_boxes]
else:
so_prompt_phrase_word_box_list = [(f"{name}", name, name.split(" ")[-1], box) for name, box in gen_boxes]
objects = [gen_box[0] for gen_box in gen_boxes]
objects_unique, objects_count = np.unique(objects, return_counts=True)
num_total_matched_boxes = 0
overall_phrases_words_bboxes = []
for ind, object_name in enumerate(objects_unique):
bboxes = [box for name, box in gen_boxes if name == object_name]
if objects_count[ind] > 1:
phrase = p.plural_noun(object_name.replace("an ", "").replace("a ", ""))
if include_counts:
phrase = p.number_to_words(objects_count[ind]) + " " + phrase
else:
phrase = object_name
# Currently using the last word of the phrase as word.
word = phrase.split(' ')[-1]
num_total_matched_boxes += len(bboxes)
overall_phrases_words_bboxes.append((phrase, word, bboxes))
assert num_total_matched_boxes == len(gen_boxes), f"{num_total_matched_boxes} != {len(gen_boxes)}"
objects_str = ", ".join([phrase for phrase, _, _ in overall_phrases_words_bboxes])
if objects_str:
if bg_prompt:
overall_prompt = f"{bg_prompt} with {objects_str}"
else:
overall_prompt = objects_str
else:
overall_prompt = bg_prompt
if verbose:
print("so_prompt_phrase_word_box_list:", so_prompt_phrase_word_box_list)
print("overall_prompt:", overall_prompt)
print("overall_phrases_words_bboxes:", overall_phrases_words_bboxes)
return so_prompt_phrase_word_box_list, overall_prompt, overall_phrases_words_bboxes