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from PIL import Image # (pip install Pillow)
import numpy as np # (pip install numpy)
from skimage import measure # (pip install scikit-image)
#from shapely.geometry import Polygon, MultiPolygon # (pip install Shapely)
import os
import json
def create_sub_masks(mask_image, width, height):
# Initialize a dictionary of sub-masks indexed by RGB colors
sub_masks = {}
for x in range(width):
for y in range(height):
# Get the RGB values of the pixel
pixel = mask_image.getpixel((x,y))[:3]
# Check to see if we have created a sub-mask...
pixel_str = str(pixel)
sub_mask = sub_masks.get(pixel_str)
if sub_mask is None:
# Create a sub-mask (one bit per pixel) and add to the dictionary
# Note: we add 1 pixel of padding in each direction
# because the contours module doesn"t handle cases
# where pixels bleed to the edge of the image
sub_masks[pixel_str] = Image.new("1", (width+2, height+2))
# Set the pixel value to 1 (default is 0), accounting for padding
sub_masks[pixel_str].putpixel((x+1, y+1), 1)
return sub_masks
# def create_sub_mask_annotation(sub_mask):
# # Find contours (boundary lines) around each sub-mask
# # Note: there could be multiple contours if the object
# # is partially occluded. (E.g. an elephant behind a tree)
# contours = measure.find_contours(np.array(sub_mask), 0.5, positive_orientation="low")
# polygons = []
# segmentations = []
# for contour in contours:
# # Flip from (row, col) representation to (x, y)
# # and subtract the padding pixel
# for i in range(len(contour)):
# row, col = contour[i]
# contour[i] = (col - 1, row - 1)
# # Make a polygon and simplify it
# poly = Polygon(contour)
# if poly.length > 100:
# poly = poly.simplify(0.5, preserve_topology=True)
# if(poly.is_empty):
# # Go to next iteration, dont save empty values in list
# continue
# polygons.append(poly)
# segmentation = np.array(poly.exterior.coords).ravel().tolist()
# segmentations.append(segmentation)
# return polygons, segmentations
def create_category_annotation(category_dict):
category_list = []
for key, value in category_dict.items():
category = {
"supercategory": key,
"id": value,
"name": key
}
category_list.append(category)
return category_list
def create_image_annotation(file_name, width, height, image_id):
images = {
"file_name": file_name,
"height": height,
"width": width,
"id": image_id
}
return images
def create_annotation_format(polygon, segmentation, image_id, category_id, annotation_id):
min_x, min_y, max_x, max_y = polygon.bounds
width = max_x - min_x
height = max_y - min_y
bbox = (min_x, min_y, width, height)
area = polygon.area
annotation = {
"segmentation": segmentation,
"area": area,
"iscrowd": 0,
"image_id": image_id,
"bbox": bbox,
"category_id": category_id,
"id": annotation_id
}
return annotation
def get_coco_json_format():
# Standard COCO format
coco_format = {
"info": {},
"licenses": [],
"images": [{}],
"categories": [{}],
"annotations": [{}]
}
return coco_format
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