mathieu/easy-local-alpr
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some refactor

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This commit is contained in:
Mathieu Broillet 2024-08-04 12:58:59 +02:00
parent 0d7351d651
commit 9cf457511e
Signed by: mathieu
GPG Key ID: C4A6176ABC6B2DFC
1 changed files with 60 additions and 91 deletions
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95
alpr_api.py
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@ -30,9 +30,9 @@ information. The server is created using Flask and the ultimateALPR SDK is used
See the README.md file for more information on how to run this script. See the README.md file for more information on how to run this script.
""" """
# Load configuration from a JSON file or environment variables # Load configuration
CONFIG_PATH = os.path.join(bundle_dir, CONFIG_PATH = os.path.join(bundle_dir,
'config.json') # TODO: store config file outside of bundle (to avoid compilation by users) 'config.json') # TODO: store config file outside of bundle (to remove need for compilation by users)
if os.path.exists(CONFIG_PATH): if os.path.exists(CONFIG_PATH):
with open(CONFIG_PATH, 'r') as config_file: with open(CONFIG_PATH, 'r') as config_file:
JSON_CONFIG = json.load(config_file) JSON_CONFIG = json.load(config_file)
@ -87,11 +87,11 @@ def start_backend_loop():
load_engine() load_engine()
# loop for about an hour or 3000 requests then reload the engine (fix for trial license) # loop for about an hour or 3000 requests then reload the engine (fix for trial license)
while counter < 3000 and time.time() - boot_time < 60 * 60: while counter < 3000 and time.time() - boot_time < 3600:
# every 120 sec # every 120 sec
if int(time.time()) % 120 == 0: if int(time.time()) % 120 == 0:
if not is_engine_loaded(): if not is_engine_loaded():
unload_engine() # just in case unload_engine()
load_engine() load_engine()
time.sleep(1) time.sleep(1)
@ -126,23 +126,15 @@ def process_image(image: Image) -> str:
counter += 1 counter += 1
width, height = image.size width, height = image.size
image_type = IMAGE_TYPES_MAPPING.get(image.mode, None)
if image.mode in IMAGE_TYPES_MAPPING: if image_type is None:
image_type = IMAGE_TYPES_MAPPING[image.mode] raise ValueError(f"Invalid mode: {image.mode}")
else:
raise ValueError("Invalid mode: %s" % image.mode)
result = ultimateAlprSdk.UltAlprSdkEngine_process( result = ultimateAlprSdk.UltAlprSdkEngine_process(
image_type, image_type, image.tobytes(), width, height, 0, 1
image.tobytes(),
width,
height,
0, # stride
1 # exifOrientation
) )
if not result.isOK(): if not result.isOK():
raise RuntimeError("Process failed: %s" % result.phrase()) raise RuntimeError(f"Process failed: {result.phrase()}")
else:
return result.json() return result.json()
@ -172,34 +164,30 @@ def create_rest_server_flask():
else: else:
wanted_cells = list(range(1, grid_size * grid_size + 1)) wanted_cells = list(range(1, grid_size * grid_size + 1))
image = request.files['upload'] image_file = request.files['upload']
if image.filename == '': if image_file.filename == '':
return jsonify({'error': 'No selected file'}), 400 return jsonify({'error': 'No selected file'}), 400
image = Image.open(image) image = Image.open(image_file)
result = process_image(image) result = process_image(image)
result = convert_to_cpai_compatible(result) result = convert_to_cpai_compatible(result)
if not result['predictions']: if not result['predictions']:
logger.debug("No plate found in the image, attempting to split the image") logger.debug("No plate found, attempting grid split")
predictions_found = find_best_plate_with_grid_split(image, grid_size, wanted_cells) predictions_found = find_best_plate_with_grid_split(image, grid_size, wanted_cells)
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']))
# Add the isolated plate image to the result
if result['predictions']: if result['predictions']:
isolated_plate_image = isolate_plate_in_image(image, result['predictions'][0]) 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)}" result['image'] = f"data:image/png;base64,{image_to_base64(isolated_plate_image, compress=True)}"
result['processMs'] = round((time.time() - interference) * 1000, 2) process_ms = round((time.time() - interference) * 1000, 2)
result['inferenceMs'] = result['processMs'] result.update({'processMs': process_ms, 'inferenceMs': process_ms})
return jsonify(result) return jsonify(result)
except Exception as e: except Exception as e:
logger.error(f"Error processing image: {e}") logger.error(f"Error processing image: {e}")
logger.error(traceback.format_exc()) logger.error(traceback.format_exc())
return jsonify({'error': 'Error processing image'}), 500 return jsonify({'error': 'Error processing image'}), 500
@app.route('/v1/image/alpr_grid_debug', methods=['POST']) @app.route('/v1/image/alpr_grid_debug', methods=['POST'])
@ -216,35 +204,28 @@ def create_rest_server_flask():
- The image with the grid overlayed on it - The image with the grid overlayed on it
""" """
try: try:
if 'upload' not in request.files: if 'upload' not in request.files:
return jsonify({'error': 'No image found'}), 400 return jsonify({'error': 'No image found'}), 400
grid_size = int(request.form.get('grid_size', 3)) grid_size = int(request.form.get('grid_size', 3))
wanted_cells = request.form.get('wanted_cells') wanted_cells = request.form.get('wanted_cells')
if wanted_cells: if wanted_cells:
wanted_cells = [int(cell) for cell in wanted_cells.split(',')] wanted_cells = [int(cell) for cell in wanted_cells.split(',')]
else: else:
wanted_cells = list(range(1, grid_size * grid_size + 1)) wanted_cells = list(range(1, grid_size * grid_size + 1))
image = request.files['upload'] image_file = request.files['upload']
if image.filename == '': if image_file.filename == '':
return jsonify({'error': 'No selected file'}), 400 return jsonify({'error': 'No selected file'}), 400
image = Image.open(image) image = Image.open(image_file)
image = draw_grid_and_cell_numbers_on_image(image, grid_size, wanted_cells) image = draw_grid_and_cell_numbers_on_image(image, grid_size, wanted_cells)
image_base64 = image_to_base64(image, compress=True) image_base64 = image_to_base64(image, compress=True)
result = { return jsonify({"image": f"data:image/png;base64,{image_base64}"})
"image": f"data:image/png;base64,{image_base64}"
}
return jsonify(result)
except Exception as e: except Exception as e:
logger.error(f"Error processing image: {e}") logger.error(f"Error processing image: {e}")
logger.error(traceback.format_exc()) logger.error(traceback.format_exc())
return jsonify({'error': 'Error processing image'}), 500 return jsonify({'error': 'Error processing image'}), 500
@app.route('/') @app.route('/')
@ -256,7 +237,6 @@ def create_rest_server_flask():
def convert_to_cpai_compatible(result): def convert_to_cpai_compatible(result):
result = json.loads(result) result = json.loads(result)
response = { response = {
'success': "true", 'success': "true",
'processMs': result['duration'], 'processMs': result['duration'],
@ -274,27 +254,22 @@ def convert_to_cpai_compatible(result):
'timestamp': '' 'timestamp': ''
} }
if 'plates' in result: for plate in result.get('plates', []):
plates = result['plates']
for plate in plates:
warpedBox = plate['warpedBox'] warpedBox = plate['warpedBox']
x_coords = warpedBox[0::2] x_coords = warpedBox[0::2]
y_coords = warpedBox[1::2] y_coords = warpedBox[1::2]
x_min = min(x_coords) x_min, x_max = min(x_coords), max(x_coords)
x_max = max(x_coords) y_min, y_max = min(y_coords), max(y_coords)
y_min = min(y_coords)
y_max = max(y_coords)
response['predictions'].append({ response['predictions'].append({
'confidence': plate['confidences'][0] / 100, 'confidence': plate['confidences'][0] / 100,
'label': "Plate: " + plate['text'], 'label': f"Plate: {plate['text']}",
'plate': plate['text'], 'plate': plate['text'],
'x_min': x_min, 'x_min': x_min,
'x_max': x_max, 'x_max': x_max,
'y_min': y_min, 'y_min': y_min,
'y_max': y_max 'y_max': y_max
}) })
return response return response
@ -337,7 +312,6 @@ def find_best_plate_with_grid_split(image: Image, grid_size: int = 3, wanted_cel
wanted_cells = list(range(1, grid_size * grid_size + 1)) wanted_cells = list(range(1, grid_size * grid_size + 1))
predictions_found = [] predictions_found = []
width, height = image.size width, height = image.size
cell_width = width // grid_size cell_width = width // grid_size
cell_height = height // grid_size cell_height = height // grid_size
@ -354,8 +328,7 @@ def find_best_plate_with_grid_split(image: Image, grid_size: int = 3, wanted_cel
cell_image = image.crop((left, upper, right, lower)) cell_image = image.crop((left, upper, right, lower))
result_cell = json.loads(process_image(cell_image)) result_cell = json.loads(process_image(cell_image))
if 'plates' in result_cell: for plate in result_cell.get('plates', []):
for plate in result_cell['plates']:
warpedBox = plate['warpedBox'] warpedBox = plate['warpedBox']
x_coords = warpedBox[0::2] x_coords = warpedBox[0::2]
y_coords = warpedBox[1::2] y_coords = warpedBox[1::2]
@ -366,7 +339,7 @@ def find_best_plate_with_grid_split(image: Image, grid_size: int = 3, wanted_cel
predictions_found.append({ predictions_found.append({
'confidence': plate['confidences'][0] / 100, 'confidence': plate['confidences'][0] / 100,
'label': "Plate: " + plate['text'], 'label': f"Plate: {plate['text']}",
'plate': plate['text'], 'plate': plate['text'],
'x_min': x_min, 'x_min': x_min,
'x_max': x_max, 'x_max': x_max,
@ -378,18 +351,16 @@ def find_best_plate_with_grid_split(image: Image, grid_size: int = 3, wanted_cel
def isolate_plate_in_image(image: Image, plate: dict) -> Image: def isolate_plate_in_image(image: Image, plate: dict) -> Image:
x_min = plate['x_min'] x_min, x_max = plate['x_min'], plate['x_max']
x_max = plate['x_max'] y_min, y_max = plate['y_min'], plate['y_max']
y_min = plate['y_min']
y_max = plate['y_max']
offset = 10 offset = 10
image = image.crop((max(0, x_min - offset), max(0, y_min - offset), min(image.size[0], x_max + offset), 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))) min(image.size[1], y_max + offset)))
image = image.resize((int(image.size[0] * 3), int(image.size[1] * 3)), resample=Image.Resampling.LANCZOS) resized_image = cropped_image.resize((int(cropped_image.size[0] * 3), int(cropped_image.size[1] * 3)),
resample=Image.Resampling.LANCZOS)
return image return resized_image
def image_to_base64(img: Image, compress=False): def image_to_base64(img: Image, compress=False):
@ -397,13 +368,11 @@ def image_to_base64(img: Image, compress=False):
buffered = io.BytesIO() buffered = io.BytesIO()
if compress: if compress:
img = img.resize((int(img.size[0] / 2), int(img.size[1] / 2))) img = img.resize((img.size[0] // 2, img.size[1] // 2))
img.save(buffered, format="WEBP", quality=35, lossless=False) img.save(buffered, format="WEBP", quality=35, lossless=False)
else: else:
img.save(buffered, format="WEBP") img.save(buffered, format="WEBP")
print(buffered.__sizeof__())
return base64.b64encode(buffered.getvalue()).decode('utf-8') return base64.b64encode(buffered.getvalue()).decode('utf-8')