# Run OpenCV face detection on an image | Modal Docs
- **URL:** https://modal.com/docs/examples/count_faces
- **Summary:** This example shows how you can use OpenCV on Modal to detect faces in an image. We use the opencv-python package to load the image and the opencv library to detect faces. The function count_faces takes an image as input and returns the number of faces detected in the image.
[View on GitHub](https://github.com/modal-labs/modal-examples/blob/main/07_web/count_faces.py)
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Run OpenCV face detection on an image
=====================================
This example shows how you can use OpenCV on Modal to detect faces in an image. We use the `opencv-python` package to load the image and the `opencv` library to detect faces. The function `count_faces` takes an image as input and returns the number of faces detected in the image.
The code below also shows how you can create wrap this function in a simple FastAPI server to create a web interface.
import os
import modal
app = modal.App("example-count-faces")
open_cv_image = (
modal.Image.debian_slim(python_version="3.11")
.apt_install("python3-opencv")
.uv_pip_install(
"fastapi[standard]==0.115.4",
"opencv-python~=4.10.0",
"numpy<2",
)
)
@app.function(image=open_cv_image)
def count_faces(image_bytes: bytes) -> int:
import cv2
import numpy as np
# Example borrowed from https://towardsdatascience.com/face-detection-in-2-minutes-using-opencv-python-90f89d7c0f81
# Load the cascade
face_cascade = cv2.CascadeClassifier(
os.path.join(cv2.data.haarcascades, "haarcascade_frontalface_default.xml")
)
# Read the input image
np_bytes = np.frombuffer(image_bytes, dtype=np.uint8)
img = cv2.imdecode(np_bytes, cv2.IMREAD_COLOR)
# Convert into grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Detect faces
faces = face_cascade.detectMultiScale(gray, 1.1, 4)
return len(faces)
@app.function(
image=modal.Image.debian_slim(python_version="3.11").uv_pip_install("inflect")
)
@modal.asgi_app()
def web():
import inflect
from fastapi import FastAPI, File, HTTPException, UploadFile
from fastapi.responses import HTMLResponse
app = FastAPI()
@app.get("/", response_class=HTMLResponse)
async def index():
"""
Render an HTML form for file upload.
"""
return """
Face Counter
Upload an Image to Count Faces
"""
@app.post("/process", response_class=HTMLResponse)
async def process(file: UploadFile = File(...)):
"""
Process the uploaded image and return the number of faces detected.
"""
try:
file_content = await file.read()
num_faces = await count_faces.remote.aio(file_content)
return f"""
Face Counter Result
{inflect.engine().number_to_words(num_faces).title()} {"Face" if num_faces == 1 else "Faces"} Detected
{"😀" * num_faces}
Go back
"""
except Exception as e:
raise HTTPException(
status_code=400, detail=f"Error processing image: {str(e)}"
)
return app
[Run OpenCV face detection on an image](https://modal.com/docs/examples/count_faces#run-opencv-face-detection-on-an-image)
> Common reference appendix (shared error/status catalog): see [../_shared-appendix.md](../_shared-appendix.md).
modal serve 07_web/count_faces.py