mathieu/easy-local-alpr
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improve splitting

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This commit is contained in:
Mathieu Broillet 2024-07-17 22:46:00 +02:00
parent b1b762ce9c
commit 42b1571ee2
Signed by: mathieu
GPG Key ID: A08E484FE95074C1
1 changed files with 46 additions and 39 deletions
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85
alpr_api.py
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@ -136,44 +136,7 @@ def create_rest_server_flask():
if not result['predictions']: if not result['predictions']:
print("No plate found in the image, attempting to split the image") print("No plate found in the image, attempting to split the image")
predictions_found = [] predictions_found = find_best_plate_with_split(image)
width, height = image.size
cell_width = width // 3
cell_height = height // 3
cells_to_process = [2, 4, 5, 6, 8, 9]
for cell_index in range(1, 10):
row = (cell_index - 1) // 3
col = (cell_index - 1) % 3
left = col * cell_width
upper = row * cell_height
right = left + cell_width
lower = upper + cell_height
if cell_index in cells_to_process:
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
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 predictions_found: if predictions_found:
result['predictions'].append(max(predictions_found, key=lambda x: x['confidence'])) result['predictions'].append(max(predictions_found, key=lambda x: x['confidence']))
@ -211,7 +174,6 @@ def convert_to_cpai_compatible(result):
if 'plates' in result: if 'plates' in result:
plates = result['plates'] plates = result['plates']
for plate in plates: for plate in plates:
warpedBox = plate['warpedBox'] warpedBox = plate['warpedBox']
x_coords = warpedBox[0::2] x_coords = warpedBox[0::2]
@ -234,6 +196,51 @@ def convert_to_cpai_compatible(result):
return response return response
def find_best_plate_with_split(image, split_size=4, wanted_cells=None):
if wanted_cells is None:
wanted_cells = [5, 6, 7, 9, 10, 11, 14, 15] # TODO: use params not specifc to my use case
predictions_found = []
width, height = image.size
cell_width = width // split_size
cell_height = height // split_size
for cell_index in range(1, split_size * split_size + 1):
row = (cell_index - 1) // split_size
col = (cell_index - 1) % split_size
left = col * cell_width
upper = row * cell_height
right = left + cell_width
lower = upper + cell_height
if cell_index in wanted_cells:
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
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
})
return predictions_found
if __name__ == '__main__': if __name__ == '__main__':
engine = threading.Thread(target=load_engine, daemon=True) engine = threading.Thread(target=load_engine, daemon=True)
engine.start() engine.start()