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import json |
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from typing import List |
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import cv2 |
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import os |
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import numpy as np |
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import gradio as gr |
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import supervision as sv |
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from inference_sdk import ( |
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InferenceHTTPClient, |
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InferenceConfiguration, |
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VisualisationResponseFormat |
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) |
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def read_json_file(file_path: str) -> dict: |
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with open(file_path, 'r') as file: |
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return json.load(file) |
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def split_and_strip(text: str) -> List[str]: |
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return [part.strip() for part in text.split(',')] |
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MARKDOWN = """ |
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# WORKFLOWS 🛠 |
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Define complex ML pipelines in JSON and execute it, running multiple models, fusing |
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outputs seamlessly. |
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Use self-hosted Inference HTTP [container](https://inference.roboflow.com/inference_helpers/inference_cli/#inference-server-start) |
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or run against Roboflow [API](https://detect.roboflow.com/docs) |
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to get results without single line of code written. |
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""" |
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LICENSE_PLATES_MARKDOWN = """ |
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""" |
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LICENSE_PLATES_EXAMPLES = [ |
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"https://media.roboflow.com/inference/license_plate_1.jpg", |
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"https://media.roboflow.com/inference/license_plate_2.jpg", |
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] |
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LICENSE_PLATES_SPECIFICATION_PATH = 'configs/license_plates.json' |
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LICENSE_PLATES_SPECIFICATION = read_json_file(LICENSE_PLATES_SPECIFICATION_PATH) |
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LICENSE_PLATES_SPECIFICATION_STRING = f""" |
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```json |
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{json.dumps(LICENSE_PLATES_SPECIFICATION, indent=4)} |
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``` |
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""" |
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CAR_BRANDS_MARKDOWN = """ |
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""" |
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CAR_BRANDS_EXAMPLES = [ |
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["Lexus, Honda, Seat", "https://media.roboflow.com/inference/multiple_cars_1.jpg"], |
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["Volkswagen, Renault, Mercedes", "https://media.roboflow.com/inference/multiple_cars_2.jpg"], |
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] |
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CAR_BRANDS_SPECIFICATION_PATH = 'configs/car_brands.json' |
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CAR_BRANDS_SPECIFICATION = read_json_file(CAR_BRANDS_SPECIFICATION_PATH) |
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CAR_BRANDS_SPECIFICATION_STRING = f""" |
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```json |
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{json.dumps(CAR_BRANDS_SPECIFICATION, indent=4)} |
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``` |
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""" |
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API_URL = os.getenv('API_URL', None) |
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API_KEY = os.getenv('API_KEY', None) |
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print("API_URL", API_URL) |
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if API_KEY is None or API_URL is None: |
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raise ValueError("API_URL and API_KEY environment variables are required") |
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CLIENT = InferenceHTTPClient(api_url=API_URL, api_key=API_KEY) |
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CLIENT.configure(InferenceConfiguration( |
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output_visualisation_format=VisualisationResponseFormat.NUMPY)) |
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def annotate_image(image: np.ndarray, detections: sv.Detections) -> np.ndarray: |
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h, w, _ = image.shape |
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annotated_image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) |
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line_thickness = sv.calculate_dynamic_line_thickness(resolution_wh=(w, h)) |
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text_scale = sv.calculate_dynamic_text_scale(resolution_wh=(w, h)) |
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bounding_box_annotator = sv.BoundingBoxAnnotator(thickness=line_thickness) |
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label_annotator = sv.LabelAnnotator( |
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text_scale=text_scale, |
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text_thickness=line_thickness |
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) |
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annotated_image = bounding_box_annotator.annotate( |
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annotated_image, detections) |
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annotated_image = label_annotator.annotate( |
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annotated_image, detections) |
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return cv2.cvtColor(annotated_image, cv2.COLOR_BGR2RGB) |
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def inference_license_plates(input_image: np.ndarray) -> np.ndarray: |
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result = CLIENT.infer_from_workflow( |
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specification=LICENSE_PLATES_SPECIFICATION["specification"], |
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images={"image": input_image}, |
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) |
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detections = sv.Detections.from_inference(result) |
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if len(detections) == 0: |
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return input_image |
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detections['class_name'] = ( |
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result["recognised_plates"] |
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if isinstance(result["recognised_plates"], list) |
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else [result["recognised_plates"]] |
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) |
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return annotate_image(input_image, detections) |
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def inference_car_brands(input_text: str, input_image: np.ndarray) -> np.ndarray: |
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classes = split_and_strip(input_text) |
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result = CLIENT.infer_from_workflow( |
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specification=CAR_BRANDS_SPECIFICATION["specification"], |
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images={"image": input_image}, |
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parameters={"car_types": classes} |
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) |
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detections = sv.Detections.from_inference(result) |
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if len(detections) == 0: |
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return input_image |
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if len(detections) > 1: |
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class_ids = np.argmax(result["clip"], axis=1) |
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else: |
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class_ids = np.array([np.argmax(result["clip"], axis=0)]) |
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detections.class_ids = class_ids |
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detections['class_name'] = [classes[class_id] for class_id in class_ids] |
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return annotate_image(input_image, detections) |
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with gr.Blocks() as demo: |
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gr.Markdown(MARKDOWN) |
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with gr.Tab(label="License Plates"): |
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gr.Markdown(LICENSE_PLATES_MARKDOWN) |
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with gr.Accordion("Configuration JSON", open=False): |
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gr.Markdown(LICENSE_PLATES_SPECIFICATION_STRING) |
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with gr.Row(): |
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license_plates_input_image_component = gr.Image( |
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type='numpy', |
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label='Input Image' |
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) |
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license_plates_output_image_component = gr.Image( |
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type='numpy', |
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label='Output Image' |
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) |
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with gr.Row(): |
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license_plates_submit_button_component = gr.Button('Submit') |
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gr.Examples( |
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fn=inference_license_plates, |
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examples=LICENSE_PLATES_EXAMPLES, |
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inputs=license_plates_input_image_component, |
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outputs=license_plates_output_image_component, |
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cache_examples=True |
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) |
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with gr.Tab(label="Car Brands"): |
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gr.Markdown(CAR_BRANDS_MARKDOWN) |
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with gr.Accordion("Configuration JSON", open=False): |
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gr.Markdown(CAR_BRANDS_SPECIFICATION_STRING) |
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with gr.Row(): |
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with gr.Column(): |
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car_brands_input_image_component = gr.Image( |
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type='numpy', |
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label='Input Image' |
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) |
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car_brands_input_text = gr.Textbox( |
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label='Car Brands', |
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placeholder='Enter car brands separated by comma' |
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) |
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car_brands_output_image_component = gr.Image( |
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type='numpy', |
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label='Output Image' |
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) |
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with gr.Row(): |
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car_brands_submit_button_component = gr.Button('Submit') |
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gr.Examples( |
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fn=inference_car_brands, |
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examples=CAR_BRANDS_EXAMPLES, |
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inputs=[car_brands_input_text, car_brands_input_image_component], |
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outputs=car_brands_output_image_component, |
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cache_examples=True |
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) |
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license_plates_submit_button_component.click( |
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fn=inference_license_plates, |
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inputs=license_plates_input_image_component, |
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outputs=license_plates_output_image_component |
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) |
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car_brands_submit_button_component.click( |
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fn=inference_car_brands, |
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inputs=[car_brands_input_text, car_brands_input_image_component], |
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outputs=car_brands_output_image_component |
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) |
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demo.launch(debug=False, show_error=True) |
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