add splitting system (3x3) if nothing found
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1aa018f7d5
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549aba963a
33
alpr_api.py
33
alpr_api.py
@ -2,6 +2,7 @@ import json
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import os
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import os
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import sys
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import sys
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import threading
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import threading
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import time
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from time import sleep
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from time import sleep
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import ultimateAlprSdk
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import ultimateAlprSdk
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@ -126,6 +127,7 @@ def create_rest_server_flask():
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def alpr(domain, module):
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def alpr(domain, module):
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# Only care about the ALPR endpoint
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# Only care about the ALPR endpoint
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if domain == 'image' and module == 'alpr':
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if domain == 'image' and module == 'alpr':
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interference = time.time()
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if 'upload' not in request.files:
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if 'upload' not in request.files:
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return jsonify({'error': 'No image found'})
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return jsonify({'error': 'No image found'})
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@ -134,12 +136,13 @@ def create_rest_server_flask():
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return jsonify({'error': 'No selected file'})
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return jsonify({'error': 'No selected file'})
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image = Image.open(image)
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image = Image.open(image)
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result = process_image(image)
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result = convert_to_cpai_compatible(process_image(image))
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result = convert_to_cpai_compatible(result)
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if len(result['predictions']) == 0:
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if len(result['predictions']) == 0:
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print("No plate found in the image, trying to split the image")
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print("No plate found in the image, trying to split the image")
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predictions_found = []
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width, height = image.size
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width, height = image.size
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cell_width = width // 3
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cell_width = width // 3
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cell_height = height // 3
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cell_height = height // 3
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@ -164,10 +167,34 @@ def create_rest_server_flask():
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# Extract the cell as a new image
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# Extract the cell as a new image
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cell_image = image.crop((left, upper, right, lower))
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cell_image = image.crop((left, upper, right, lower))
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result_cell = json.loads(process_image(cell_image))
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if 'plates' in result_cell:
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for plate in result_cell['plates']:
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warpedBox = plate['warpedBox']
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x_coords = warpedBox[0::2]
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y_coords = warpedBox[1::2]
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x_min = min(x_coords) + left
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x_max = max(x_coords) + left
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y_min = min(y_coords) + upper
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y_max = max(y_coords) + upper
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cell_image.show()
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predictions_found.append({
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'confidence': plate['confidences'][0] / 100,
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'label': "Plate: " + plate['text'],
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'plate': plate['text'],
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'x_min': x_min,
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'x_max': x_max,
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'y_min': y_min,
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'y_max': y_max
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})
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if len(predictions_found) > 0:
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# add the prediction with the highest confidence
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result['predictions'].append(max(predictions_found, key=lambda x: x['confidence']))
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result['processMs'] = round((time.time() - interference) * 1000, 2)
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result['inferenceMs'] = result['processMs'] # same as processMs
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return jsonify(result)
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return jsonify(result)
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else:
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else:
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return jsonify({'error': 'Endpoint not implemented'}), 404
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return jsonify({'error': 'Endpoint not implemented'}), 404
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