Convert JSON

1. Download JSON

Option 1: Download JSON file directly from the UI using the Export tab and drag JSON file into working director
Option 2: Use mdai library to download JSON

Create Client

import mdai
# Get variables from project info tab and user settings
DOMAIN = 'public.md.ai'
YOUR_PERSONAL_TOKEN = 'a1s2d3f4g4h5h59797kllh8vk'
PROJECT_ID = 'MwBe19Br' # project info
mdai_client = mdai.Client(domain=DOMAIN, access_token=YOUR_PERSONAL_TOKEN)

Example output

Successfully authenticated to public.md.ai.

Download annotations

# Download only the annotation data
p = mdai_client.project(PROJECT_ID, path='.',  annotations_only=True)

Example output

Using working directory for data.
Preparing annotations export for project MwBe19Br...
Success: annotations data for project MwBe19Br ready.
Downloading file: mdai_public_project_MwBe19Br_annotations_labelgroup_all_2020-09-23-214038.json
No project created. Downloaded annotations only.

2. JSON to Dataframe

Copy the downloaded file name from the output above or from your downloaded JSON file. (if errors occur, try downloading annotations outside of your firewall)

# Replace with your filename
JSON = 'mdai_public_project_MwBe19Br_annotations_labelgroup_all_2020-09-23-214038.json'
results = mdai.common_utils.json_to_dataframe(JSON)
# Annotations dataframe
annots_df = results['annotations']

3. Conversions

Bounding box

Extract box data dictionary items

# Simplify table
columns_brief = ['id', 'StudyInstanceUID', 'SeriesInstanceUID', 'SOPInstanceUID', 'labelName', 'data', 'annotationMode']
annots_df = annots_df[columns_brief]
 
# Box annotations
boxes = annots_df[annots_df.annotationMode == 'bbox']
 
# Extract box data
def extract_box_data(df):
    j = df.copy()
    j = j[(j.annotationMode == 'bbox') & (~j.data.isnull())]
    try:
        j['data'] = j['data'].apply(lambda x:json.loads(x.replace("'", "\"")))
    except:
        j['data']
 
    j['x'] = [d['x'] for _,d in j.data.iteritems()]
    j['y'] = [d['y'] for _,d in j.data.iteritems()]
    j['w'] = [d['width'] for _,d in j.data.iteritems()]
    j['h'] = [d['height'] for _,d in j.data.iteritems()]
    j = j.drop('data', axis=1)
    return j
 
boxes = extract_box_data(boxes)
boxes.head()

Example output

	id	StudyInstanceUID	SeriesInstanceUID	SOPInstanceUID	labelName	annotationMode	x	y	w	h
0	A_J07mWn	1.3.6.1.4.1.14519.5.2.1.1079.4008.312038908377...	1.3.6.1.4.1.14519.5.2.1.1079.4008.228089024512...	1.3.6.1.4.1.14519.5.2.1.1079.4008.515564548072...	Stomach	bbox	220.822845	124.504723	121.008514	82.380470
1	A_qxVdNk	1.3.6.1.4.1.14519.5.2.1.1079.4008.312038908377...	1.3.6.1.4.1.14519.5.2.1.1079.4008.228089024512...	1.3.6.1.4.1.14519.5.2.1.1079.4008.306261432947...	Stomach	bbox	183.308075	148.138794	99.586609	68.920502
2	A_49KLWk	1.3.6.1.4.1.14519.5.2.1.1079.4008.312038908377...	1.3.6.1.4.1.14519.5.2.1.1079.4008.228089024512...	1.3.6.1.4.1.14519.5.2.1.1079.4008.132057312174...	Stomach	bbox	251.314133	125.590210	84.764328	103.895081

Derive additional box data

Now lets get the box center, area, and bottom left corner

boxes['area'] = boxes.x * boxes.y
boxes['center_x'] = boxes.x + boxes.w/2
boxes['center_y'] = boxes.y + boxes.h/2
boxes['bottom_x'] = boxes.x + boxes.w
boxes['bottom_y'] = boxes.y + boxes.h
# Convert values to integers for simplicity
boxes[boxes.columns[6:]] = boxes[boxes.columns[6:]].astype('int')
boxes.head()

Example output

	id	StudyInstanceUID	SeriesInstanceUID	SOPInstanceUID	labelName	annotationMode	x	y	w	h	area	center_x	center_y	bottom_x	bottom_y
0	A_J07mWn	1.3.6.1.4.1.14519.5.2.1.1079.4008.312038908377...	1.3.6.1.4.1.14519.5.2.1.1079.4008.228089024512...	1.3.6.1.4.1.14519.5.2.1.1079.4008.515564548072...	Stomach	bbox	220	124	121	82	27391	280	165	341	206
1	A_qxVdNk	1.3.6.1.4.1.14519.5.2.1.1079.4008.312038908377...	1.3.6.1.4.1.14519.5.2.1.1079.4008.228089024512...	1.3.6.1.4.1.14519.5.2.1.1079.4008.306261432947...	Stomach	bbox	183	148	99	68	27109	232	182	282	217
2	A_49KLWk	1.3.6.1.4.1.14519.5.2.1.1079.4008.312038908377...	1.3.6.1.4.1.14519.5.2.1.1079.4008.228089024512...	1.3.6.1.4.1.14519.5.2.1.1079.4008.132057312174...	Stomach	bbox	251	125	84	103	31523	293	177	335	229

Formatting examples

Detectron 2

Detectron 2 expects bbox coordinates to be in the format of [x_upper_left, y_upper_left, x_lower_right, y_lower_right]. Be mindful of the need to scale the annotation data if image is scaled

scale = 1.
boxes['detectron_bbox'] =  [[row.x * scale, row.y * scale, row.bottom_x * scale, row.bottom_y * scale] for _,row in boxes.iterrows()]

Fastai expects bbox coordinates to be in the format of (y_upper_left, x_upper_left, y_lower_right, x_lower_right) with the origin being in the upper left hand corner of the image. Remember to scale annotations if images are scaled

scale = 1.
boxes['fastai_bbox'] =  [[row.y * scale, row.x * scale, row.bottom_y * scale, row.bottom_x * scale] for _,row in boxes.iterrows()]

Freeform and polygon

Filter for freeform and polygon

# Simplify table
columns_brief = ['id', 'StudyInstanceUID', 'SeriesInstanceUID', 'SOPInstanceUID', 'labelName', 'data', 'annotationMode']
annots_df = annots_df[columns_brief]
 
# Shape annotations
shapes = annots_df[(annots_df.annotationMode == 'freeform') | (annots_df.annotationMode == 'polygon')]

Get box bounding the vertices

# Extract box data from vertices
import numpy as np
 
def vertices_to_boxes(data):
    vertices = data['vertices']
    px=[v[0] for v in vertices]
    py=[v[1] for v in vertices]
    x = int(np.min(px))
    y = int(np.min(py))
    x2 = int(np.max(px))
    y2 = int(np.max(py))
    w = x2 - x
    h = y2 - y
    return (x, y, w, h, x2, y2)
 
shapes['x'],shapes['y'],shapes['w'],shapes['h'],shapes['bottom_x'],shapes['bottom_y'] = zip(*shapes['data'].map(vertices_to_boxes))
shapes.head()

Example output

id	StudyInstanceUID	SeriesInstanceUID	SOPInstanceUID	labelName	data	annotationMode	x	y	w	h	bottom_x	bottom_y
0	A_JddPQE	1.3.46.670589.11.20182.5.0.8336.20130724083604...	1.3.46.670589.11.20182.5.0.7188.20130724085044...	1.3.6.1.4.1.9590.100.1.2.260616927513309992437...	Liver	{'vertices': [[64, 130], [64, 129], [64, 128],...	freeform	54	115	10	20	64	135
1	A_qG28ZY	1.3.46.670589.11.20182.5.0.8336.20130724083604...	1.3.46.670589.11.20182.5.0.7188.20130724085044...	1.3.6.1.4.1.9590.100.1.2.388757691711551811514...	Liver	{'vertices': [[66, 129], [66, 128], [66, 127],...	freeform	53	112	13	27	66	139

Longest diameter

Get longest diameter of a shape in mm. Pixel spacing is obtained using the pydicom library and the PixelSpacing tag

import cv2
 
def longest_diameter(data, pixel_spacing):
    try:
        row_spacing, col_spacing = pixel_spacing
    except:
        return -1
 
    max_points = most_distant_points(np.array(data['vertices']))
    x1 = max_points[0][0]
    y1 = max_points[0][1]
    x2 = max_points[1][0]
    y2 = max_points[1][1]
 
    dx = col_spacing * (x2 - x1)
    dy = row_spacing * (y2 - y1)
    distance = np.sqrt(dx ** 2 + dy ** 2)
    # returns longest diameter in mm
    return round(distance, 2)
 
def most_distant_points(points):
    hull = cv2.convexHull(points, returnPoints=True)
 
    max_points = [points[0], points[1]]
    max_distance = 0
    for i in range(0,len(hull)):
        for j in range(i+1,len(hull)):
            p1 = hull[i][0]
            p2 = hull[j][0]
            distance = calc_distance(p1, p2)
        if (distance > max_distance):
            max_points = [p1, p2]
            max_distance = distance
    return max_points;
 
def calc_distance(p1, p2):
    return np.sqrt((p1[0] - p2[0]) ** 2 + (p1[1] - p2[1]) ** 2)

Example

import pydicom as py
 
ds = py.dcmread('000.dcm')
ld = longest_diameter(data, ds.PixelSpacing)

Mask

Convert MD.ai annotation to mask

Function to load a single mask instance from one row of annotation data. This will turn one box, free form, polygon, etc into a binary mask sized to the corresponding image.

def load_mask_instance(row):
    """Load instance masks for the given annotation row. Masks can be different types,
    mask is a binary true/false map of the same size as the image.
    """
 
    mask = np.zeros((row.height, row.width), dtype=np.uint8)
 
    annotation_mode = row.annotationMode
    # print(annotation_mode)
 
    if annotation_mode == "bbox":
        # Bounding Box
        x = int(row["data"]["x"])
        y = int(row["data"]["y"])
        w = int(row["data"]["width"])
        h = int(row["data"]["height"])
        mask_instance = mask[:,:].copy()
        cv2.rectangle(mask_instance, (x, y), (x + w, y + h), 255, -1)
        mask[:,:] = mask_instance
 
    # FreeForm or Polygon
    elif annotation_mode == "freeform" or annotation_mode == "polygon":
        vertices = np.array(row["data"]["vertices"])
        vertices = vertices.reshape((-1, 2))
        mask_instance = mask[:,:].copy()
        cv2.fillPoly(mask_instance, np.int32([vertices]), (255, 255, 255))
        mask[:,:] = mask_instance
 
    # Line
    elif annotation_mode == "line":
        vertices = np.array(row["data"]["vertices"])
        vertices = vertices.reshape((-1, 2))
        mask_instance = mask[:,:].copy()
        cv2.polylines(mask_instance, np.int32([vertices]), False, (255, 255, 255), 12)
        mask[:,:] = mask_instance
 
    elif annotation_mode == "location":
        # Bounding Box
        x = int(row["data"]["x"])
        y = int(row["data"]["y"])
        mask_instance = mask[:,:].copy()
        cv2.circle(mask_instance, (x, y), 7, (255, 255, 255), -1)
        mask[:,:] = mask_instance
 
    elif annotation_mode is None:
        print("Not a local instance")
 
 
    return mask.astype(np.bool)

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