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ddda863 f59e7c5 65ae6d0 363d6df ddda863 363d6df ddda863 65ae6d0 f59e7c5 ddda863 f59e7c5 ddda863 65ae6d0 ddda863 b8731d7 363d6df 65ae6d0 ddda863 23102fd b8731d7 4f86615 23102fd ae5fc80 23102fd b8731d7 23102fd 363d6df ddda863 23102fd 363d6df | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 | from fastapi import FastAPI, File, UploadFile
import deepface.DeepFace as DeepFace
from fastapi.responses import JSONResponse
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
import numpy as np
from mivolo.predictor import Predictor
import base64
from PIL import Image
import numpy as np
from AttributesHolder import Namespace
import cv2
config = {
"checkpoint": 'models/mivolo_imbd.pth.tar',
"detector_weights": 'models/yolov8x_person_face.pt',
"device": 'cpu',
"draw": False,
"with_persons": True,
"disable_faces": False,
"output": 'output'
}
namespace = Namespace()
setattr(namespace, 'checkpoint', 'models/mivolo_imbd.pth.tar')
setattr(namespace, 'detector_weights', 'models/yolov8x_person_face.pt')
setattr(namespace, 'device', 'cpu')
setattr(namespace, 'draw', False)
setattr(namespace, 'with_persons', True)
setattr(namespace, 'disable_faces', False)
setattr(namespace, 'output', 'output')
predictor = Predictor(config=namespace)
app = FastAPI()
class Base64Data(BaseModel):
base64_data: str
origins = ["*"]
app.add_middleware(
CORSMiddleware,
allow_origins=origins,
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
@app.get("/")
def index():
return {"message": "Hello, World!!!"}
# post image to server
@app.post("/predict-actor/")
async def create_upload_file(contents: Base64Data):
try:
# Read the file
print("-----Starting Prediction----------")
loaded_image = base64_to_cv2(contents.base64_data)
detected_objects, out_im = predictor.recognize(loaded_image)
age = detected_objects.ages[0]
gender = detected_objects.genders[0]
contents = contents.base64_data
df = DeepFace.find(img_path = contents, db_path = "dataset/",model_name ='GhostFaceNet', threshold=0.9)
filename = df[0].head()['identity'][0]
filename = filename.replace("\\", "/")
print(f"filename: {filename}")
# Convert the image to base64
base64_output = image_to_base64("dataset/" + filename)
print("------Prediction Done-----------")
return JSONResponse(content={
"celeb_image": base64_output,
"celeb":df[0].head()['identity'][0], "res":{
"age": age,
"gender": gender
} }, status_code=200)
except Exception as e:
return JSONResponse(content={"message": "Error processing the file.", "error": str(e)}, status_code=500)
def image_to_base64(image_path):
with open(image_path, "rb") as img_file:
# Read the image file
img_data = img_file.read()
# Encode the image data as base64
base64_data = base64.b64encode(img_data)
# Decode bytes-like object to ASCII string
base64_str = base64_data.decode("ascii")
return base64_str
def base64_to_cv2(base64_string):
base64_string = base64_string.split(",")[1]
# Decode the base64 string into bytes
decoded_bytes = base64.b64decode(base64_string)
# Convert bytes to numpy array
np_array = np.frombuffer(decoded_bytes, np.uint8)
# Decode the numpy array into an image
image = cv2.imdecode(np_array, cv2.IMREAD_COLOR)
return image |