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import os |
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from PIL import Image, ImageDraw, ImageFont |
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from .utils import image_label_2_color |
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def get_flattened_output(docs): |
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print("Running Flattened Output") |
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flattened_output = [] |
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annotation_key = 'output' |
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for doc in docs: |
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flattened_output_item = {annotation_key: []} |
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doc_annotation = doc[annotation_key] |
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for i, span in enumerate(doc_annotation): |
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if len(span['words']) > 1: |
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for span_chunk in span['words']: |
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flattened_output_item[annotation_key].append( |
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{ |
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'label': span['label'], |
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'text': span_chunk['text'], |
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'words': [span_chunk] |
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} |
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) |
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else: |
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flattened_output_item[annotation_key].append(span) |
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flattened_output.append(flattened_output_item) |
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return flattened_output |
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def annotate_image(image_path, annotation_object): |
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print("Annotating Images") |
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img = None |
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image = Image.open(image_path).convert('RGBA') |
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tmp = image.copy() |
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label2color = image_label_2_color(annotation_object) |
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overlay = Image.new('RGBA', tmp.size, (0, 0, 0)+(0,)) |
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draw = ImageDraw.Draw(overlay) |
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font = ImageFont.load_default() |
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predictions = [span['label'] for span in annotation_object['output']] |
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boxes = [span['words'][0]['box'] for span in annotation_object['output']] |
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for prediction, box in zip(predictions, boxes): |
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draw.rectangle(box, outline=label2color[prediction], |
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width=3, fill=label2color[prediction]+(int(255*0.33),)) |
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draw.text((box[0] + 10, box[1] - 10), text=prediction, |
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fill=label2color[prediction], font=font) |
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img = Image.alpha_composite(tmp, overlay) |
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img = img.convert("RGB") |
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image_name = os.path.basename(image_path) |
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image_name = image_name[:image_name.find('.')] |
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output_folder = 'static/temp/img_display/' |
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os.makedirs(output_folder, exist_ok=True) |
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img.save(os.path.join(output_folder, f'{image_name}.jpg_inference.jpg')) |
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