# default_exp datasets.generators_legacy
%load_ext autoreload
%autoreload 2
from nbdev import *
from typing import List, Tuple
Helpers for Dataset Generation¶
Use tmp dir¶
import tempfile
tmp_dir = tempfile.TemporaryDirectory()
tmp_dir.name
'/tmp/tmpdtgtmebx'
from pathlib import Path
f_path = Path(tmp_dir.name) / 'test.csv'
f_path
Path('/tmp/tmpdtgtmebx/test.csv')
import pandas as pd
from pandas import DataFrame
DataFrame({'a': [4], 'b': 5}).to_csv(f_path, index=False)
pd.read_csv(f_path)
| a | b | |
|---|---|---|
| 0 | 4 | 5 |
Create images¶
im = Image.new('RGB', (500, 300), (128, 128, 128))
draw = ImageDraw.Draw(im)
draw.ellipse((100, 100, 150, 200), fill=(255, 0, 0), outline=(0, 0, 0))
draw.rectangle((200, 100, 300, 200), fill=(4, 4, 200), outline=(255, 255, 255))
draw.line((350, 200, 450, 100), fill=(255, 255, 0), width=10)
im
def draw_grid(im=None, size=(100, 100), n_hlines=10, n_vlines=10, black=True):
"""
size: (width, hight)
black: bool
draw grid and numbers in black or white
"""
color = (0, 0, 0)
if not black:
color = (255, 255, 255)
if im is None:
im = Image.new('RGB', size, color=(211, 211, 211))
width, hight = im.size
draw = ImageDraw.Draw(im)
ln_width = int((max(size) * 0.03) / max(n_hlines, n_vlines))
for h in range(n_hlines):
y = hight * h * (1 / n_hlines)
draw.line((0, y, width, y), fill=color, width=ln_width)
draw.text((width * 0.05, y), text=str((int(y))) + 'y',
fill=color)
draw.text((width * 0.9, y), text=str((int(y))) + 'y', fill=color)
for h in range(n_vlines):
x = width * h * (1 / n_vlines)
draw.line((x, 0, x, hight), fill=color, width=ln_width)
draw.text((x, hight * 0.05), text=str((int(x))) + 'x', fill=color)
draw.text((x, hight * 0.9), text=str((int(x))) + 'x', fill=color)
return im
draw_grid(size=(200, 200), n_hlines=4, n_vlines=4)
img = Image.new('RGB', (300, 300), color='grey')
# draw_bbox((35, 50, 200, 250), im=img, black=False)
draw_bbox((200, 250, 35, 50), im=img, black=False, width=4)
draw_grid(img, n_hlines=5, n_vlines=5)
xyxy_to_xywh([50, 50, 150, 150])
[50, 50, 100, 100]
xywh_to_xyxy([50, 50, 100, 100])
[50, 50, 150, 150]
Overlap & Intersection over Union (IOU)¶
r1 = (10, 10, 110, 110)
r2 = (50, 50, 90, 90)
assert overlap(r1, r2) == 1
assert overlap(r2, r1) == 1
r1 = (0, 0, 100, 100)
r2 = (50, 50, 150, 150)
assert overlap(r1, r2) == 0.25
assert overlap(r2, r1) == 0.25
# export
def bb_intersection_over_union(boxA, boxB, verbose=False):
interArea, boxAArea, boxBArea, _ = bbox_intersection(boxA, boxB)
iou = interArea / float(boxAArea + boxBArea - interArea)
if verbose:
print(f"iou: {iou: .2f}, interArea: {interArea: .2f}"
f", boxAArea {boxAArea: .2f}, box1Area {boxBArea: .2f}")
return iou
r1 = (10, 10, 110, 110)
r2 = (80, 80, 180, 180)
img = Image.new('RGB', (300, 200), color='grey')
draw_bbox(r1, im=img, black=False, values=True)
draw_bbox(r2, im=img, black=False, values=True)
iou = bb_intersection_over_union(r1, r2, verbose=True)
# iou = bb_intersection_over_union(r1, r2, verbose=True)
_, _, _, union = bbox_intersection(r1, r2)
draw = ImageDraw.Draw(img)
draw.rectangle(union, fill='blue')
img
iou: 0.05, interArea: 900.00, boxAArea 10000.00, box1Area 10000.00
r1 = (10, 10, 110, 110)
r2 = (20, 20, 120, 120)
img = Image.new('RGB', (200, 150), color='grey')
draw_bbox(r1, im=img, black=False, values=True)
draw_bbox(r2, im=img, black=False, values=True)
iou = bb_intersection_over_union(r1, r2, verbose=True)
_, _, _, union = bbox_intersection(r1, r2)
draw = ImageDraw.Draw(img)
draw.rectangle(union, fill='blue')
img
iou: 0.68, interArea: 8100.00, boxAArea 10000.00, box1Area 10000.00
r1 = (10, 10, 110, 110)
img = Image.new('RGB', (200, 150), color='grey')
draw_bbox(r1, im=img, black=False, values=True)
draw_bbox(r1, im=img, black=False, values=True)
iou = bb_intersection_over_union(r1, r1, verbose=True)
_, _, _, union = bbox_intersection(r1, r2)
draw = ImageDraw.Draw(img)
draw.rectangle(union, fill='blue')
img
iou: 1.00, interArea: 10000.00, boxAArea 10000.00, box1Area 10000.00
r1 = (10, 10, 110, 110)
r2 = (20, 20, 90, 90)
img = Image.new('RGB', (200, 150), color='grey')
draw_bbox(r1, im=img, black=False, values=True)
draw_bbox(r2, im=img, black=False, values=True)
iou = bb_intersection_over_union(r1, r2, verbose=True)
_, _, _, union = bbox_intersection(r1, r2)
draw = ImageDraw.Draw(img)
draw.rectangle(union, fill='blue')
img
iou: 0.49, interArea: 4900.00, boxAArea 10000.00, box1Area 4900.00
Sample Random bbox¶
canvas_size = (300, 300)
img = Image.new('RGB', canvas_size, color='grey')
bboxs: List[Tuple[int, int, int, int]] = []
for i in range(10):
bbox = sample_bbox(bboxs=bboxs, canvas_size=canvas_size, diag=(0.1, 0.3),
max_iou=0.3, max_overlap=0.5)
bboxs.append(bbox)
draw_bbox(bbox, im=img, black=False, values=False, width=3)
img
Draw Objects inside bbox¶
img = Image.new('RGB', (200, 100), color='grey')
bbox1 = (25, 25, 90, 75)
bbox2 = (125, 25, 190, 75)
draw_ellipse(img, bbox1, "blue")
draw_rectangle(img, bbox2, "red")
draw_bbox(bbox1, im=img, black=False, values=False)
draw_bbox(bbox2, im=img, black=False, values=False)
img
Create Object Detection Dataset¶
Generic Dataset¶
import tempfile
tmp_dir = tempfile.TemporaryDirectory()
path = Path(tmp_dir.name)
create_simple_object_detection_dataset(path=path, n_samples=5)
print(pd.read_json(path / 'annotations.json').T)
Image.open(list(path.glob('**/images/*'))[2])
labels \
img_0.jpg [[blue, rectangle], [yellow, rectangle]]
img_1.jpg [[red, rectangle]]
img_2.jpg [[blue, ellipse], [blue, rectangle], [blue, re...
img_3.jpg [[yellow, rectangle]]
img_4.jpg [[yellow, rectangle], [blue, rectangle]]
bboxs
img_0.jpg [[56, 51, 87, 90], [69, 5, 108, 34]]
img_1.jpg [[56, 8, 121, 57]]
img_2.jpg [[30, 19, 75, 44], [4, 62, 47, 133], [65, 87, ...
img_3.jpg [[49, 4, 138, 59]]
img_4.jpg [[61, 3, 134, 50], [75, 87, 108, 146]]
Specific Tasks¶
#export
def create_color_classification(path, n_samples=10, n_colors=3, size=(150, 150)):
create_simple_object_detection_dataset(path=path, n_objects=(1, 1), n_samples=n_samples,
n_colors=n_colors, size=size)
with open(path / 'annotations.json', 'r') as f:
annotations = json.load(f)
# simplify by dropping mutli-label and bbox
annotations = {k: {'labels': v['labels'][0][0]} for k, v in annotations.items()}
with open(path / 'annotations.json', 'w') as f:
json.dump(annotations, f)
import tempfile
tmp_dir = tempfile.TemporaryDirectory()
path = Path(tmp_dir.name)
create_color_classification(path, size=(100, 100))
print(pd.read_json(path / 'annotations.json').T)
Image.open(list(path.glob('**/images/*'))[2])
labels
img_0.jpg yellow
img_1.jpg blue
img_2.jpg yellow
img_3.jpg red
img_4.jpg yellow
img_5.jpg blue
img_6.jpg yellow
img_7.jpg blue
img_8.jpg red
img_9.jpg blue
#export
def create_shape_color_classification(path, n_samples=10, n_colors=3, size=(150, 150)):
create_simple_object_detection_dataset(path=path, n_objects=(1, 1), n_samples=n_samples,
n_colors=n_colors, size=size)
with open(path / 'annotations.json', 'r') as f:
annotations = json.load(f)
# simplify by dropping mutli-label and bbox
annotations = {k: {'labels': v['labels'][0]} for k, v in annotations.items()}
with open(path / 'annotations.json', 'w') as f:
json.dump(annotations, f)
import tempfile
tmp_dir = tempfile.TemporaryDirectory()
path = Path(tmp_dir.name)
create_shape_color_classification(path, size=(100, 100))
print(pd.read_json(path / 'annotations.json').T)
Image.open(list(path.glob('**/images/*'))[2])
labels
img_0.jpg [blue, rectangle]
img_1.jpg [yellow, rectangle]
img_2.jpg [blue, rectangle]
img_3.jpg [yellow, ellipse]
img_4.jpg [blue, ellipse]
img_5.jpg [yellow, rectangle]
img_6.jpg [blue, ellipse]
img_7.jpg [red, ellipse]
img_8.jpg [yellow, rectangle]
img_9.jpg [blue, ellipse]
#export
def create_object_detection(path, n_samples=10, n_objects=(1, 1), n_colors=3,
size=(150, 150), multilabel=False):
create_simple_object_detection_dataset(path=path, n_objects=n_objects, n_samples=n_samples,
n_colors=n_colors, size=size)
with open(path / 'annotations.json', 'r') as f:
annotations = json.load(f)
# simplify by dropping mutli-label and bbox
if max(n_objects) == 1:
annotations = {k: {
'labels': v['labels'][0], 'bbox': v['bboxs'][0]} for k, v in annotations.items()}
if not multilabel:
for k, v in annotations.items():
v['labels'] = v['labels'][0]
else:
if not multilabel:
for k, v in annotations.items():
v['labels'] = v['labels'] = [label[0] for label in v['labels']]
with open(path / 'annotations.json', 'w') as f:
json.dump(annotations, f)
import tempfile
tmp_dir = tempfile.TemporaryDirectory()
path = Path(tmp_dir.name)
create_object_detection(path, size=(100, 100), n_objects=(1, 5), multilabel=True)
print(pd.read_json(path / 'annotations.json').T)
Image.open(list(path.glob('**/images/*'))[2])
labels \
img_0.jpg [[yellow, rectangle], [blue, rectangle]]
img_1.jpg [[red, ellipse], [blue, ellipse], [red, rectan...
img_2.jpg [[red, ellipse], [red, rectangle]]
img_3.jpg [[blue, ellipse], [red, ellipse], [blue, recta...
img_4.jpg [[yellow, rectangle], [red, ellipse], [blue, r...
img_5.jpg [[blue, rectangle], [red, ellipse], [red, elli...
img_6.jpg [[yellow, rectangle], [red, rectangle], [yello...
img_7.jpg [[blue, rectangle], [red, rectangle]]
img_8.jpg [[red, rectangle], [red, rectangle], [yellow, ...
img_9.jpg [[red, rectangle]]
bboxs
img_0.jpg [[9, 22, 44, 77], [58, 30, 93, 47]]
img_1.jpg [[22, 20, 39, 45], [43, 34, 78, 81], [2, 52, 2...
img_2.jpg [[77, 20, 98, 43], [53, 34, 76, 65]]
img_3.jpg [[11, 21, 46, 40], [52, 29, 85, 52], [4, 66, 1...
img_4.jpg [[3, 23, 34, 56], [35, 22, 72, 51], [68, 76, 9...
img_5.jpg [[68, 57, 93, 76], [23, 0, 82, 38], [22, 52, 5...
img_6.jpg [[3, 10, 46, 63], [63, 3, 82, 30], [49, 70, 88...
img_7.jpg [[18, 47, 47, 64], [21, 6, 62, 29]]
img_8.jpg [[1, 4, 34, 29], [43, 47, 70, 70], [38, 8, 63,...
img_9.jpg [[58, 14, 91, 37]]
