mathieu/easy-local-alpr
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add splitting system (3x3) if nothing found

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Mathieu Broillet 2024-07-17 22:24:32 +02:00
parent 1aa018f7d5
commit 549aba963a
Signed by: mathieu
GPG Key ID: A08E484FE95074C1
1 changed files with 30 additions and 3 deletions
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alpr_api.py
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@ -2,6 +2,7 @@ import json
import os import os
import sys import sys
import threading import threading
import time
from time import sleep from time import sleep
import ultimateAlprSdk import ultimateAlprSdk
@ -126,6 +127,7 @@ def create_rest_server_flask():
def alpr(domain, module): def alpr(domain, module):
# Only care about the ALPR endpoint # Only care about the ALPR endpoint
if domain == 'image' and module == 'alpr': if domain == 'image' and module == 'alpr':
interference = time.time()
if 'upload' not in request.files: if 'upload' not in request.files:
return jsonify({'error': 'No image found'}) return jsonify({'error': 'No image found'})
@ -134,12 +136,13 @@ def create_rest_server_flask():
return jsonify({'error': 'No selected file'}) return jsonify({'error': 'No selected file'})
image = Image.open(image) image = Image.open(image)
result = process_image(image) result = convert_to_cpai_compatible(process_image(image))
result = convert_to_cpai_compatible(result)
if len(result['predictions']) == 0: if len(result['predictions']) == 0:
print("No plate found in the image, trying to split the image") print("No plate found in the image, trying to split the image")
predictions_found = []
width, height = image.size width, height = image.size
cell_width = width // 3 cell_width = width // 3
cell_height = height // 3 cell_height = height // 3
@ -164,10 +167,34 @@ def create_rest_server_flask():
# Extract the cell as a new image # Extract the cell as a new image
cell_image = image.crop((left, upper, right, lower)) cell_image = image.crop((left, upper, right, lower))
result_cell = json.loads(process_image(cell_image))
if 'plates' in result_cell:
for plate in result_cell['plates']:
warpedBox = plate['warpedBox']
x_coords = warpedBox[0::2]
y_coords = warpedBox[1::2]
x_min = min(x_coords) + left
x_max = max(x_coords) + left
y_min = min(y_coords) + upper
y_max = max(y_coords) + upper
cell_image.show() predictions_found.append({
'confidence': plate['confidences'][0] / 100,
'label': "Plate: " + plate['text'],
'plate': plate['text'],
'x_min': x_min,
'x_max': x_max,
'y_min': y_min,
'y_max': y_max
})
if len(predictions_found) > 0:
# add the prediction with the highest confidence
result['predictions'].append(max(predictions_found, key=lambda x: x['confidence']))
result['processMs'] = round((time.time() - interference) * 1000, 2)
result['inferenceMs'] = result['processMs'] # same as processMs
return jsonify(result) return jsonify(result)
else: else:
return jsonify({'error': 'Endpoint not implemented'}), 404 return jsonify({'error': 'Endpoint not implemented'}), 404