mirror of
https://github.com/M4TH1EU/easy-local-alpr.git
synced 2025-09-13 15:43:05 +00:00
added support for multiples plates support
This commit is contained in:
parent
10345b4dd9
commit
da0094ec9b
GPG Key ID:
4428608CDA3A98D3
261
alpr_api.py
261
alpr_api.py
@ -41,14 +41,14 @@ else:
|
||||
"assets_folder": os.path.join(bundle_dir, "assets"),
|
||||
"charset": "latin",
|
||||
"car_noplate_detect_enabled": False,
|
||||
"ienv_enabled": False,
|
||||
"ienv_enabled": True, # night vision enhancements
|
||||
"openvino_enabled": True,
|
||||
"openvino_device": "CPU",
|
||||
"npu_enabled": False,
|
||||
"klass_lpci_enabled": False,
|
||||
"klass_vcr_enabled": False,
|
||||
"klass_vmmr_enabled": False,
|
||||
"klass_vbsr_enabled": False,
|
||||
"klass_lpci_enabled": True, # License Plate Country Identification
|
||||
"klass_vcr_enabled": False, # Vehicle Color Recognition (paid)
|
||||
"klass_vmmr_enabled": True, # Vehicle Make and Model Recognition
|
||||
"klass_vbsr_enabled": False, # Vehicle Body Style Recognition (paid)
|
||||
"license_token_file": "",
|
||||
"license_token_data": "",
|
||||
|
||||
@ -150,40 +150,56 @@ def create_rest_server_flask():
|
||||
- upload: The image to be processed
|
||||
- grid_size: The number of cells to split the image into (e.g. 3)
|
||||
- wanted_cells: The cells to process in the grid separated by commas (e.g. 1,2,3,4) (max: grid_size²)
|
||||
- whole_image_fallback: If set to true, the whole image will be processed if no plates are found in the specified cells. (default: true)
|
||||
"""
|
||||
interference = time.time()
|
||||
whole_image_fallback = request.form.get('whole_image_fallback', 'true').lower() == 'true'
|
||||
|
||||
try:
|
||||
if 'upload' not in request.files:
|
||||
return jsonify({'error': 'No image found'}), 400
|
||||
|
||||
grid_size = int(request.form.get('grid_size', 3))
|
||||
wanted_cells = request.form.get('wanted_cells')
|
||||
if wanted_cells:
|
||||
wanted_cells = [int(cell) for cell in wanted_cells.split(',')]
|
||||
else:
|
||||
wanted_cells = list(range(1, grid_size * grid_size + 1))
|
||||
grid_size = int(request.form.get('grid_size', 1))
|
||||
wanted_cells = _get_wanted_cells_from_request(request, grid_size)
|
||||
|
||||
image_file = request.files['upload']
|
||||
if image_file.filename == '':
|
||||
return jsonify({'error': 'No selected file'}), 400
|
||||
|
||||
image = Image.open(image_file)
|
||||
result = process_image(image)
|
||||
result = convert_to_cpai_compatible(result)
|
||||
image = _load_image_from_request(request)
|
||||
|
||||
result = {
|
||||
'predictions': [],
|
||||
'plates': [],
|
||||
'duration': 0
|
||||
}
|
||||
|
||||
if grid_size < 2:
|
||||
logger.debug("Grid size < 2, processing the whole image")
|
||||
response = process_image(image)
|
||||
result.update(_parse_result_from_ultimatealpr(response))
|
||||
else:
|
||||
logger.debug(f"Grid size: {grid_size}, processing specified cells: {wanted_cells}")
|
||||
predictions_found = _find_best_plate_with_grid_split(image, grid_size, wanted_cells)
|
||||
result['predictions'].extend(predictions_found)
|
||||
|
||||
if not result['predictions']:
|
||||
logger.debug("No plate found, attempting grid split")
|
||||
predictions_found = find_best_plate_with_grid_split(image, grid_size, wanted_cells)
|
||||
if predictions_found:
|
||||
result['predictions'].append(max(predictions_found, key=lambda x: x['confidence']))
|
||||
if grid_size >= 2 and whole_image_fallback:
|
||||
logger.debug("No plates found in the specified cells, trying whole image as last resort")
|
||||
response = process_image(image)
|
||||
result.update(_parse_result_from_ultimatealpr(response))
|
||||
|
||||
if result['predictions']:
|
||||
isolated_plate_image = isolate_plate_in_image(image, result['predictions'][0])
|
||||
result['image'] = f"data:image/png;base64,{image_to_base64(isolated_plate_image, compress=True)}"
|
||||
if result['predictions'] and len(result['predictions']) > 0:
|
||||
all_plates = []
|
||||
for plate in result['predictions']:
|
||||
all_plates.append(plate.get('plate'))
|
||||
isolated_plate_image = isolate_plate_in_image(image, plate)
|
||||
plate['image'] = f"data:image/png;base64,{image_to_base64(isolated_plate_image, compress=True)}"
|
||||
|
||||
process_ms = round((time.time() - interference) * 1000, 2)
|
||||
result.update({'processMs': process_ms, 'inferenceMs': process_ms})
|
||||
result['plates'] = all_plates
|
||||
|
||||
duration = round((time.time() - interference) * 1000, 2)
|
||||
result.update({'duration': duration})
|
||||
return jsonify(result)
|
||||
except Exception as e:
|
||||
logger.error(f"Error processing image: {e}")
|
||||
@ -208,17 +224,13 @@ def create_rest_server_flask():
|
||||
return jsonify({'error': 'No image found'}), 400
|
||||
|
||||
grid_size = int(request.form.get('grid_size', 3))
|
||||
wanted_cells = request.form.get('wanted_cells')
|
||||
if wanted_cells:
|
||||
wanted_cells = [int(cell) for cell in wanted_cells.split(',')]
|
||||
else:
|
||||
wanted_cells = list(range(1, grid_size * grid_size + 1))
|
||||
wanted_cells = _get_wanted_cells_from_request(request, grid_size)
|
||||
|
||||
image_file = request.files['upload']
|
||||
if image_file.filename == '':
|
||||
return jsonify({'error': 'No selected file'}), 400
|
||||
|
||||
image = Image.open(image_file)
|
||||
image = _load_image_from_request(request)
|
||||
image = draw_grid_and_cell_numbers_on_image(image, grid_size, wanted_cells)
|
||||
|
||||
image_base64 = image_to_base64(image, compress=True)
|
||||
@ -235,23 +247,46 @@ def create_rest_server_flask():
|
||||
return app
|
||||
|
||||
|
||||
def convert_to_cpai_compatible(result):
|
||||
def _get_wanted_cells_from_request(request, grid_size) -> list:
|
||||
"""
|
||||
Helper function to extract wanted cells from the request.
|
||||
If no cells are specified, it returns all cells in the grid.
|
||||
"""
|
||||
wanted_cells = request.form.get('wanted_cells')
|
||||
if wanted_cells:
|
||||
wanted_cells = [int(cell) for cell in wanted_cells.split(',')]
|
||||
else:
|
||||
wanted_cells = list(range(1, grid_size * grid_size + 1))
|
||||
|
||||
if not all(1 <= cell <= grid_size * grid_size for cell in wanted_cells):
|
||||
raise ValueError("Invalid cell numbers provided.")
|
||||
|
||||
return wanted_cells
|
||||
|
||||
|
||||
def _load_image_from_request(request) -> Image:
|
||||
"""
|
||||
Helper function to load an image from the request.
|
||||
It expects the image to be in the 'upload' field of the request.
|
||||
"""
|
||||
if 'upload' not in request.files:
|
||||
raise ValueError("No image found in request.")
|
||||
|
||||
image_file = request.files['upload']
|
||||
if image_file.filename == '':
|
||||
raise ValueError("No selected file.")
|
||||
|
||||
try:
|
||||
image = Image.open(image_file)
|
||||
return correct_image_orientation(image)
|
||||
except Exception as e:
|
||||
raise ValueError(f"Error loading image: {e}")
|
||||
|
||||
|
||||
def _parse_result_from_ultimatealpr(result) -> dict:
|
||||
result = json.loads(result)
|
||||
response = {
|
||||
'success': "true",
|
||||
'processMs': result['duration'],
|
||||
'inferenceMs': result['duration'],
|
||||
'predictions': [],
|
||||
'message': '',
|
||||
'moduleId': 'ALPR',
|
||||
'moduleName': 'License Plate Reader',
|
||||
'code': 200,
|
||||
'command': 'alpr',
|
||||
'requestId': 'null',
|
||||
'inferenceDevice': 'none',
|
||||
'analysisRoundTripMs': 0,
|
||||
'processedBy': 'none',
|
||||
'timestamp': ''
|
||||
}
|
||||
|
||||
for plate in result.get('plates', []):
|
||||
@ -263,7 +298,6 @@ def convert_to_cpai_compatible(result):
|
||||
|
||||
response['predictions'].append({
|
||||
'confidence': plate['confidences'][0] / 100,
|
||||
'label': f"Plate: {plate['text']}",
|
||||
'plate': plate['text'],
|
||||
'x_min': x_min,
|
||||
'x_max': x_max,
|
||||
@ -273,7 +307,66 @@ def convert_to_cpai_compatible(result):
|
||||
return response
|
||||
|
||||
|
||||
def _find_best_plate_with_grid_split(image: Image, grid_size: int = 3, wanted_cells: list = None,
|
||||
stop_at_first_match: bool = False) -> list:
|
||||
"""
|
||||
Splits the image into a grid and processes each cell to find the best plate.
|
||||
Returns a list of predictions found in the specified cells.
|
||||
"""
|
||||
|
||||
if grid_size < 2:
|
||||
logger.debug("Grid size < 2, skipping split")
|
||||
return []
|
||||
|
||||
predictions_found = []
|
||||
width, height = image.size
|
||||
cell_width = width // grid_size
|
||||
cell_height = height // grid_size
|
||||
|
||||
for cell_index in range(1, grid_size * grid_size + 1):
|
||||
row = (cell_index - 1) // grid_size
|
||||
col = (cell_index - 1) % grid_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 = process_image(cell_image)
|
||||
logger.info(f"Processed image with result (grid): {result}")
|
||||
result_cell = json.loads(result)
|
||||
|
||||
for plate in result_cell.get('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,
|
||||
'plate': plate['text'],
|
||||
'x_min': x_min,
|
||||
'x_max': x_max,
|
||||
'y_min': y_min,
|
||||
'y_max': y_max
|
||||
})
|
||||
|
||||
if stop_at_first_match:
|
||||
logger.debug(f"Found plate in cell {cell_index}: {plate['text']}")
|
||||
return predictions_found
|
||||
|
||||
return predictions_found
|
||||
|
||||
|
||||
def draw_grid_and_cell_numbers_on_image(image: Image, grid_size: int = 3, wanted_cells: list = None) -> Image:
|
||||
"""
|
||||
Draws a grid on the image and numbers the cells.
|
||||
"""
|
||||
|
||||
if grid_size < 1:
|
||||
grid_size = 1
|
||||
|
||||
@ -303,57 +396,13 @@ def draw_grid_and_cell_numbers_on_image(image: Image, grid_size: int = 3, wanted
|
||||
return image
|
||||
|
||||
|
||||
def find_best_plate_with_grid_split(image: Image, grid_size: int = 3, wanted_cells: list = None):
|
||||
if grid_size < 1:
|
||||
logger.debug("Grid size < 1, skipping split")
|
||||
return []
|
||||
def isolate_plate_in_image(image: Image, plate: dict, offset=10) -> Image:
|
||||
"""
|
||||
Isolates the plate area in the image and returns a cropped and resized image.
|
||||
"""
|
||||
|
||||
if wanted_cells is None:
|
||||
wanted_cells = list(range(1, grid_size * grid_size + 1))
|
||||
|
||||
predictions_found = []
|
||||
width, height = image.size
|
||||
cell_width = width // grid_size
|
||||
cell_height = height // grid_size
|
||||
|
||||
for cell_index in range(1, grid_size * grid_size + 1):
|
||||
row = (cell_index - 1) // grid_size
|
||||
col = (cell_index - 1) % grid_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))
|
||||
|
||||
for plate in result_cell.get('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': f"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
|
||||
|
||||
|
||||
def isolate_plate_in_image(image: Image, plate: dict) -> Image:
|
||||
x_min, x_max = plate['x_min'], plate['x_max']
|
||||
y_min, y_max = plate['y_min'], plate['y_max']
|
||||
offset = 10
|
||||
x_min, x_max = plate.get('x_min'), plate.get('x_max')
|
||||
y_min, y_max = plate.get('y_min'), plate.get('y_max')
|
||||
|
||||
cropped_image = image.crop((max(0, x_min - offset), max(0, y_min - offset), min(image.size[0], x_max + offset),
|
||||
min(image.size[1], y_max + offset)))
|
||||
@ -363,7 +412,7 @@ def isolate_plate_in_image(image: Image, plate: dict) -> Image:
|
||||
return resized_image
|
||||
|
||||
|
||||
def image_to_base64(img: Image, compress=False):
|
||||
def image_to_base64(img: Image, compress=False) -> str:
|
||||
"""Convert a Pillow image to a base64-encoded string."""
|
||||
|
||||
buffered = io.BytesIO()
|
||||
@ -376,6 +425,28 @@ def image_to_base64(img: Image, compress=False):
|
||||
return base64.b64encode(buffered.getvalue()).decode('utf-8')
|
||||
|
||||
|
||||
from PIL import Image, ExifTags
|
||||
|
||||
|
||||
def correct_image_orientation(img):
|
||||
try:
|
||||
exif = img._getexif()
|
||||
if exif is not None:
|
||||
orientation_key = next(
|
||||
(k for k, v in ExifTags.TAGS.items() if v == 'Orientation'), None)
|
||||
if orientation_key is not None:
|
||||
orientation = exif.get(orientation_key)
|
||||
if orientation == 3:
|
||||
img = img.rotate(180, expand=True)
|
||||
elif orientation == 6:
|
||||
img = img.rotate(270, expand=True)
|
||||
elif orientation == 8:
|
||||
img = img.rotate(90, expand=True)
|
||||
except Exception as e:
|
||||
print("EXIF orientation correction failed:", e)
|
||||
return img
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
engine_thread = threading.Thread(target=start_backend_loop, daemon=True)
|
||||
engine_thread.start()
|
||||
|
||||
@ -1,14 +1,14 @@
|
||||
#!/bin/bash
|
||||
|
||||
VERSION=1.5.0
|
||||
VERSION=1.6.0
|
||||
|
||||
rm -rf buildenv build dist *.spec
|
||||
python3.10 -m venv buildenv
|
||||
source buildenv/bin/activate
|
||||
python3.10 -m pip install --upgrade pip pyinstaller
|
||||
python3.10 -m pip install ./wheel/ultimateAlprSdk-3.0.0-cp310-cp310-linux_x86_64.whl
|
||||
python3.10 -m pip install ./wheel/ultimateAlprSdk-3.14.1-cp310-cp310-linux_x86_64.whl
|
||||
pip install -r requirements.txt
|
||||
|
||||
pyinstaller --noconfirm --onefile --console --add-data libs:. --add-data assets:assets --add-data static:static --add-data templates:templates --name easy-local-alpr-$VERSION-openvinocpu_linux_x86_64 "alpr_api.py"
|
||||
pyinstaller --noconfirm --console --add-data libs:. --add-data assets:assets --add-data static:static --add-data templates:templates --name easy-local-alpr-$VERSION-openvinocpu_linux_x86_64 "alpr_api.py"
|
||||
deactivate
|
||||
rm -rf buildenv
|
||||
@ -91,18 +91,28 @@
|
||||
<span id="fileName_alpr" class="ml-2 text-sm text-gray-600 dark:text-gray-300"></span>
|
||||
</div>
|
||||
</div>
|
||||
<div>
|
||||
<div class="mt-4">
|
||||
<label for="grid_size_alpr" class="block text-sm font-medium text-gray-700 dark:text-gray-300">Grid
|
||||
Size:</label>
|
||||
<input type="number" id="grid_size_alpr" name="grid_size" value="3"
|
||||
class="mt-1 block w-full px-3 py-2 border border-gray-300 rounded-md shadow-sm focus:outline-none focus:ring-indigo-500 focus:border-indigo-500 sm:text-sm dark:bg-neutral-800 dark:border-neutral-700">
|
||||
</div>
|
||||
<div>
|
||||
<div class="mt-4">
|
||||
<label for="wanted_cells_alpr" class="block text-sm font-medium text-gray-700 dark:text-gray-300">Wanted
|
||||
Cells:</label>
|
||||
<div id="gridContainer_alpr" class="grid-container"></div>
|
||||
<input type="hidden" id="wanted_cells_alpr" name="wanted_cells">
|
||||
</div>
|
||||
<div class="mt-4 flex flex-row space-between">
|
||||
<div>
|
||||
<label for="whole_image_fallback_alpr" class="block text-sm font-medium text-gray-700 dark:text-gray-300">Fallback to whole image if no plate is found in specified cells?</label>
|
||||
<span class="text-sm text-gray-500 dark:text-gray-400">Only applies if grid size >=2</span>
|
||||
</div>
|
||||
|
||||
<div id="gridContainer_alpr" class="grid-container"></div>
|
||||
<input type="checkbox" id="whole_image_fallback_alpr" checked>
|
||||
</div>
|
||||
|
||||
<input id="plate_image_alpr" name="plate_image_alpr" type="hidden" value="true">
|
||||
</div>
|
||||
|
||||
@ -135,10 +145,12 @@
|
||||
|
||||
<div id="imagePreview" class="mt-4 hidden">
|
||||
<label id="imagePreviewLabel"
|
||||
class="block text sm font-medium text-gray-700 dark:text-gray-300">Preview:</label>
|
||||
<img id="previewImage" src="#" alt="Preview" class="max-w-full h-auto rounded-lg">
|
||||
class="block text-sm font-medium text-gray-700 dark:text-gray-300 mb-2">Identified plate images:</label>
|
||||
<div id="previewImageContainer" class="grid grid-cols-1 sm:grid-cols-2 gap-4"></div>
|
||||
<img id="previewImageDebug" src="#" alt="" class="max-w-full h-auto rounded-lg">
|
||||
</div>
|
||||
|
||||
|
||||
<button class="w-full py-2 px-4 bg-black text-white font-semibold rounded-md shadow-sm hover:bg-neutral-900 dark:bg-neutral-900 dark:hover:bg-neutral-950"
|
||||
id="submitButton" type="submit">Submit
|
||||
</button>
|
||||
@ -271,6 +283,7 @@
|
||||
e.preventDefault();
|
||||
const service = $('#service').val();
|
||||
const formData = new FormData(this);
|
||||
formData.append('whole_image_fallback', $("#whole_image_fallback_alpr").is(":checked") ? "true" : "false");
|
||||
var url;
|
||||
if (service === 'alpr') {
|
||||
url = '/v1/image/alpr';
|
||||
@ -296,14 +309,34 @@
|
||||
$('#timer').text(`(${elapsedTime} ms)`);
|
||||
$('#submitButton').prop('disabled', false).text('Submit');
|
||||
|
||||
$('#previewImageDebug').attr('src', '');
|
||||
$('#previewImageContainer').empty();
|
||||
|
||||
if (data.image) {
|
||||
$('#previewImage').attr('src', data.image);
|
||||
$('#previewImageDebug').attr('src', data.image);
|
||||
$('#imagePreview').removeClass('hidden');
|
||||
}
|
||||
|
||||
if (service === 'alpr') $('#imagePreviewLabel').text('Identified plate image:');
|
||||
if (Array.isArray(data.predictions) && data.predictions.length > 0) {
|
||||
data.predictions.forEach((prediction, index) => {
|
||||
if (prediction.image) {
|
||||
const img = $('<img>')
|
||||
.attr('src', prediction.image)
|
||||
.addClass('max-w-full h-auto rounded-lg border border-gray-300 dark:border-gray-700 shadow');
|
||||
|
||||
const wrapper = $('<div>').append(
|
||||
$('<p>').addClass('text-sm mb-1 text-gray-600 dark:text-gray-300').text(`Plate ${index + 1}`),
|
||||
img
|
||||
);
|
||||
|
||||
$('#previewImageContainer').append(wrapper);
|
||||
}
|
||||
});
|
||||
|
||||
$('#imagePreview').removeClass('hidden');
|
||||
$('#imagePreviewLabel').text('Identified plate images:');
|
||||
} else {
|
||||
updateFileName();
|
||||
updateFileName(); // fallback if no images found
|
||||
}
|
||||
},
|
||||
error: function (xhr) {
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user