Merge pull request 'fix detached head' (#2) from detached2 into main
Reviewed-on: #2
This commit is contained in:
commit
aa0cf11dd8
170
alpr_api.py
170
alpr_api.py
@ -58,25 +58,20 @@ def load_engine():
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bundle_dir = getattr(sys, '_MEIPASS', os.path.abspath(os.path.dirname(__file__)))
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JSON_CONFIG["assets_folder"] = os.path.join(bundle_dir, "assets")
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JSON_CONFIG["charset"] = "latin"
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JSON_CONFIG["car_noplate_detect_enabled"] = False # Whether to detect and return cars with no plate
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JSON_CONFIG[
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"ienv_enabled"] = False # Whether to enable Image Enhancement for Night-Vision (IENV). More info about IENV at https://www.doubango.org/SDKs/anpr/docs/Features.html#image-enhancement-for-night-vision-ienv. Default: true for x86-64 and false for ARM.
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JSON_CONFIG[
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"openvino_enabled"] = False # Whether to enable OpenVINO. Tensorflow will be used when OpenVINO is disabled
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JSON_CONFIG[
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"openvino_device"] = "GPU" # Defines the OpenVINO device to use (CPU, GPU, FPGA...). More info at https://www.doubango.org/SDKs/anpr/docs/Configuration_options.html#openvino-device
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JSON_CONFIG["npu_enabled"] = False # Whether to enable NPU (Neural Processing Unit) acceleration
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JSON_CONFIG[
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"klass_lpci_enabled"] = False # Whether to enable License Plate Country Identification (LPCI). More info at https://www.doubango.org/SDKs/anpr/docs/Features.html#license-plate-country-identification-lpci
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JSON_CONFIG[
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"klass_vcr_enabled"] = False # Whether to enable Vehicle Color Recognition (VCR). More info at https://www.doubango.org/SDKs/anpr/docs/Features.html#vehicle-color-recognition-vcr
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JSON_CONFIG[
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"klass_vmmr_enabled"] = False # Whether to enable Vehicle Make Model Recognition (VMMR). More info at https://www.doubango.org/SDKs/anpr/docs/Features.html#vehicle-make-model-recognition-vmmr
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JSON_CONFIG[
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"klass_vbsr_enabled"] = False # Whether to enable Vehicle Body Style Recognition (VBSR). More info at https://www.doubango.org/SDKs/anpr/docs/Features.html#vehicle-body-style-recognition-vbsr
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JSON_CONFIG["license_token_file"] = "" # Path to license token file
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JSON_CONFIG["license_token_data"] = "" # Base64 license token data
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JSON_CONFIG.update({
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"charset": "latin",
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"car_noplate_detect_enabled": False,
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"ienv_enabled": False,
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"openvino_enabled": False,
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"openvino_device": "GPU",
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"npu_enabled": False,
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"klass_lpci_enabled": False,
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"klass_vcr_enabled": False,
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"klass_vmmr_enabled": False,
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"klass_vbsr_enabled": False,
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"license_token_file": "",
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"license_token_data": ""
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})
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result = ultimateAlprSdk.UltAlprSdkEngine_init(json.dumps(JSON_CONFIG))
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if not result.isOK():
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@ -108,11 +103,11 @@ def process_image(image: Image) -> str:
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result = ultimateAlprSdk.UltAlprSdkEngine_process(
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image_type,
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image.tobytes(), # type(x) == bytes
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image.tobytes(),
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width,
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height,
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0, # stride
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1 # exifOrientation (already rotated in load_image -> use default value: 1)
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1 # exifOrientation
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)
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if not result.isOK():
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raise RuntimeError("Process failed: %s" % result.phrase())
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@ -123,81 +118,32 @@ def process_image(image: Image) -> str:
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def create_rest_server_flask():
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app = Flask(__name__)
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@app.route('/v1/<string:domain>/<string:module>', methods=['POST'])
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def alpr(domain, module):
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# Only care about the ALPR endpoint
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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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return jsonify({'error': 'No image found'})
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@app.route('/v1/image/alpr', methods=['POST'])
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def alpr():
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interference = time.time()
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image = request.files['upload']
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if image.filename == '':
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return jsonify({'error': 'No selected file'})
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if 'upload' not in request.files:
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return jsonify({'error': 'No image found'})
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image = Image.open(image)
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result = convert_to_cpai_compatible(process_image(image))
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image = request.files['upload']
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if image.filename == '':
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return jsonify({'error': 'No selected file'})
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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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image = Image.open(image)
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result = process_image(image)
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result = convert_to_cpai_compatible(result)
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predictions_found = []
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if not result['predictions']:
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print("No plate found in the image, attempting to split the image")
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width, height = image.size
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cell_width = width // 3
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cell_height = height // 3
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predictions_found = find_best_plate_with_split(image)
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# Define which cells to process (2, 4, 5, 6, 8, 9)
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cells_to_process = [2, 4, 5, 6, 8, 9]
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if predictions_found:
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result['predictions'].append(max(predictions_found, key=lambda x: x['confidence']))
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# Loop through each cell
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for cell_index in range(1, 10):
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# Calculate row and column of the cell
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row = (cell_index - 1) // 3
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col = (cell_index - 1) % 3
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# Calculate bounding box of the cell
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left = col * cell_width
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upper = row * cell_height
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right = left + cell_width
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lower = upper + cell_height
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# Check if this cell should be processed
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if cell_index in cells_to_process:
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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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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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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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else:
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return jsonify({'error': 'Endpoint not implemented'}), 404
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result['processMs'] = round((time.time() - interference) * 1000, 2)
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result['inferenceMs'] = result['processMs']
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return jsonify(result)
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@app.route('/')
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def index():
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@ -228,7 +174,6 @@ def convert_to_cpai_compatible(result):
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if 'plates' in result:
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plates = result['plates']
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for plate in plates:
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warpedBox = plate['warpedBox']
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x_coords = warpedBox[0::2]
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@ -251,6 +196,51 @@ def convert_to_cpai_compatible(result):
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return response
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def find_best_plate_with_split(image, split_size=4, wanted_cells=None):
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if wanted_cells is None:
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wanted_cells = [5, 6, 7, 9, 10, 11, 14, 15] # TODO: use params not specifc to my use case
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predictions_found = []
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width, height = image.size
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cell_width = width // split_size
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cell_height = height // split_size
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for cell_index in range(1, split_size * split_size + 1):
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row = (cell_index - 1) // split_size
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col = (cell_index - 1) % split_size
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left = col * cell_width
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upper = row * cell_height
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right = left + cell_width
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lower = upper + cell_height
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if cell_index in wanted_cells:
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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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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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return predictions_found
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if __name__ == '__main__':
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engine = threading.Thread(target=load_engine, daemon=True)
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engine.start()
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