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process_ocr.py
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process_ocr.py
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from enum import Enum
from PIL import Image, ImageDraw
import io
import json
from multiprocessing.pool import Pool
from google.cloud import storage, vision
from google.protobuf.json_format import MessageToDict
class FeatureType(Enum):
PAGE = 1
BLOCK = 2
PARAGRAPH = 3
WORD = 4
SYMBOL = 5
COLORS = {
FeatureType.PAGE: "yellow",
FeatureType.BLOCK: "blue",
FeatureType.PARAGRAPH: "red",
FeatureType.WORD: "green",
FeatureType.SYMBOL: "purple",
}
EPS = 20
COLORS_OF_WORDS = {
"red": (255, 0, 0),
"green": (0, 255, 0),
"blue": (0, 0, 255),
"white": (255, 255, 255),
}
def draw_boxes(image, bounds, color):
"""
:param image: a PIL image
:param bounds: Bounding box vertices, not necessarily axis aligned
[{'x': 1608, 'y': 243},
{'x': 2853, 'y': 243},
{'x': 2853, 'y': 356},
{'x': 1608, 'y': 356}]
:param color: color to draw the box
:return:
"""
draw = ImageDraw.Draw(image)
for bound in bounds:
draw.polygon(
[
bound.vertices[0].x,
bound.vertices[0].y,
bound.vertices[1].x,
bound.vertices[1].y,
bound.vertices[2].x,
bound.vertices[2].y,
bound.vertices[3].x,
bound.vertices[3].y,
],
None,
color,
)
return image
def get_document_text(uri):
"""
Run OCR on a GCS file
:param uri: Storage URI
"""
vision_client = vision.ImageAnnotatorClient()
image = vision.types.Image()
image.source.image_uri = uri
response = vision_client.document_text_detection(image=image)
document = response.full_text_annotation
message = MessageToDict(response)
bounds = {i: [] for i in FeatureType}
for page in document.pages:
for block in page.blocks:
for paragraph in block.paragraphs:
for word in paragraph.words:
for symbol in word.symbols:
bounds[FeatureType.SYMBOL].append(symbol.bounding_box)
bounds[FeatureType.WORD].append(word.bounding_box)
bounds[FeatureType.PARAGRAPH].append(paragraph.bounding_box)
bounds[FeatureType.BLOCK].append(block.bounding_box)
return bounds, message
def _is_color_similar(a, b, eps=EPS):
return all(abs(a[i] - b[i]) < eps for i in range(len(a)))
def get_main_color(image, box):
"""
:param image: An image
:param box: Bounding box vertices, not necessarily axis aligned
[{'x': 1608, 'y': 243},
{'x': 2853, 'y': 243},
{'x': 2853, 'y': 356},
{'x': 1608, 'y': 356}]
:return: main color
"""
left = min(v["x"] for v in box)
top = min(v["y"] for v in box)
right = max(v["x"] for v in box)
bottom = max(v["y"] for v in box)
cropped = image.copy().crop((left, top, right, bottom))
colors = cropped.getcolors(cropped.size[0] * cropped.size[1])
ordered = sorted(colors, key=lambda x: x[0], reverse=True)
EPS = 16
for _, col in ordered:
if _is_color_similar(col, COLORS_OF_WORDS["white"], EPS):
continue
return col
def render_doc_text(uri, *levels):
"""
Top level handler for process_ocr
:param uri: storage URI
:param levels: list of levels to draw boxes from FeatureType
:return:
"""
split_uri = uri[5:].split("/")
bucket = split_uri[0]
path = "/".join(split_uri[1:])
filename = path.split("/")[-1].split(".")[0]
filename = f"{filename}_{'_'.join(levels)}"
storage_client = storage.Client()
bucket = storage_client.get_bucket(bucket)
blob = bucket.get_blob(path)
buffer = io.BytesIO()
blob.download_to_file(buffer)
buffer.seek(0)
image = Image.open(buffer)
bounds, message = get_document_text(uri)
for level in levels:
if hasattr(FeatureType, level):
param = getattr(FeatureType, level)
draw_boxes(image, bounds[param], COLORS[param])
for page in message["fullTextAnnotation"]["pages"]:
for block in page["blocks"]:
for paragraphs in block["paragraphs"]:
for i in range(len(paragraphs["words"])):
main_color = get_main_color(
image, paragraphs["words"][i]["boundingBox"]["vertices"]
)
paragraphs["words"][i]["color"] = main_color
image.save(f"files/annotated/{filename}.jpg")
with open(f"files/ocr/{filename}.json", "w+") as fd:
json.dump(message, fd)
return image
def _render_doc_text_parallel(args):
return render_doc_text(*args)
def process_all(bucket_name, folder):
client = storage.Client()
bucket = client.get_bucket(bucket_name)
params = []
for i, blob in enumerate(bucket.list_blobs(prefix=folder)):
if blob.name.endswith("/"):
continue
uri = f"gs://{bucket_name}/{blob.name}"
params.append((uri, "WORD", "BLOCK"))
pool = Pool(processes=8, maxtasksperchild=1000)
i = 0
for _ in pool.imap_unordered(_render_doc_text_parallel, params, chunksize=16):
i += 1
if i % 10 == 0:
print(f"{i}/{len(params)}")
pool.close()
pool.join()
if __name__ == "__main__":
process_all("daisy-data", "cleaned_images")