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