#SwingTradingStrategy
$BTC An API model for this image might look like this (in JSON format):
{
"image_description": {
"subject": "A man in a suit",
"gender": "male",
"hair_color": "black",
"hair_style": "slicked back, neatly styled",
"eye_color": "dark",
"facial_expression": "neutral, serious",
"clothing": "dark blue suit jacket, white collared shirt, dark patterned tie",
"background_color": "brown, wood-like texture",
"lighting": "even, well-lit",
"apparent_age_range": "30s-40s",
"overall_mood": "professional, formal"
},
"keywords": [
"man",
"suit",
"professional",
"formal",
"portrait",
"headshot"
]
}
This is a basic descriptive model.
Interpretation 2: You want to know how this image could be used as input to a visual recognition API.
If you were sending this image to an API that performs tasks like:
* Face Detection/Recognition: The API would return bounding box coordinates of the face, and potentially a face ID if it's a known person in a database.
* Attribute Recognition: The API could identify attributes like gender, age range, emotion, presence of glasses, etc.
* Clothing Recognition: The API might identify the type of clothing (suit, shirt, tie) and their colors.
The "API model" here refers to the schema of the data you'd send and receive.
* Request (Input): The image itself (usually as a base64 encoded string or a URL to the image).
{
"image_data": "base64_encoded_string_of_image.png"
// or
// "image_url": "http://example.com/image.png"
}
* Response (Output - example for face detection/attributes):
{
"faces": [
{
"box": {
"x_min": 100,
"y_min": 50,
"x_max": 400,
"y_max": 500
},
"attributes": {
"gender": "male
