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2cdc276 2be1e86 2cdc276 2be1e86 2cdc276 171ec6c 9acecc2 2cdc276 2be1e86 2cdc276 5b10e55 2be1e86 5b10e55 2be1e86 2cdc276 2be1e86 2cdc276 2be1e86 2cdc276 2be1e86 5b10e55 2be1e86 5b10e55 2be1e86 5b10e55 2cdc276 2be1e86 2cdc276 9acecc2 2be1e86 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 | from fastapi import FastAPI, UploadFile, File, Form, HTTPException, Depends
import os
import shutil
import numpy as np
import cv2
from PIL import Image, UnidentifiedImageError
import io
from typing import List, Dict, Set
import uvicorn
import pickle
app = FastAPI()
# Change the base directory to a location with write permissions
# Use a relative path in the app directory or /tmp
BASE_DIR = os.path.join(os.getcwd(), "uploaded_images")
# Alternative: BASE_DIR = "/tmp/uploaded_images"
# Ensure base directory exists
os.makedirs(BASE_DIR, exist_ok=True)
faculty_assignments: Dict[tuple, Set[str]] = {} # Dictionary to track faculties assigned to folders
def get_org_folder_path(org_name: str, folder_name: str):
return os.path.join(BASE_DIR, org_name, folder_name)
def load_student_encodings(folder_path: str):
"""Load student images from a given folder and extract face features."""
students = {}
if not os.path.exists(folder_path):
return {"error": f"Folder '{folder_path}' does not exist"}
# Create face detector
face_detector = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
# Check if a model already exists for this folder
model_path = os.path.join(folder_path, "face_model.pkl")
if os.path.exists(model_path):
with open(model_path, 'rb') as f:
students = pickle.load(f)
return students
# Process each image to extract face features
for filename in os.listdir(folder_path):
if filename.endswith(".jpg") or filename.endswith(".png"):
name = filename.split(".")[0]
image_path = os.path.join(folder_path, filename)
# Read image and convert to grayscale
img = cv2.imread(image_path)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Detect faces
faces = face_detector.detectMultiScale(gray, 1.3, 5)
if len(faces) > 0:
# Take the first face
x, y, w, h = faces[0]
face_img = gray[y:y+h, x:x+w]
# Resize for consistency
face_img = cv2.resize(face_img, (100, 100))
# Store the face image
students[name] = face_img
# Save the model
with open(model_path, 'wb') as f:
pickle.dump(students, f)
return students
def process_image(image_array, students):
"""Compare uploaded image with stored student images using OpenCV."""
try:
# Convert to grayscale
gray = cv2.cvtColor(image_array, cv2.COLOR_BGR2GRAY)
# Initialize face detector
face_detector = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
# Detect faces
faces = face_detector.detectMultiScale(gray, 1.3, 5)
if len(faces) == 0:
return {"error": "No faces detected in the image"}
# List to store recognized students
present_students = set()
# Compare each detected face with student face images
for (x, y, w, h) in faces:
face_img = gray[y:y+h, x:x+w]
face_img = cv2.resize(face_img, (100, 100))
for name, student_face in students.items():
# Simple template matching for demonstration
# In a production system, you'd use a proper face recognition algorithm
result = cv2.matchTemplate(face_img, student_face, cv2.TM_CCOEFF_NORMED)
similarity = np.max(result)
# Threshold for recognition
if similarity > 0.6:
present_students.add(name)
missing_students = set(students.keys()) - present_students
return {"present_students": list(present_students), "missing_students": list(missing_students)}
except Exception as e:
return {"error": f"Error processing image: {str(e)}"}
@app.post("/create_folder/")
async def create_folder(org_name: str = Form(...), folder_name: str = Form(...), files: List[UploadFile] = File(...)):
"""Creates a new folder under an organization and stores images."""
org_path = os.path.join(BASE_DIR, org_name)
folder_path = os.path.join(org_path, folder_name)
os.makedirs(folder_path, exist_ok=True)
for file in files:
file_path = os.path.join(folder_path, file.filename)
with open(file_path, "wb") as buffer:
shutil.copyfileobj(file.file, buffer)
return {"message": f"Folder '{folder_name}' created under organization '{org_name}' with {len(files)} images."}
@app.post("/check_attendance/")
async def check_attendance(org_name: str = Form(...), folder_name: str = Form(...), file: UploadFile = File(...)):
"""Check attendance using an uploaded image, ensuring only assigned faculty can check."""
folder_path = get_org_folder_path(org_name, folder_name)
# Load student encodings from the specified folder
students = load_student_encodings(folder_path)
if "error" in students:
return students
try:
# Read and process uploaded image
image_bytes = await file.read()
image = Image.open(io.BytesIO(image_bytes))
image = image.convert("RGB")
image_array = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
return process_image(image_array, students)
except UnidentifiedImageError:
return {"error": "Invalid image format. Please upload a valid image file (JPG or PNG)."}
except Exception as e:
return {"error": f"Internal server error: {str(e)}"}
@app.get("/")
async def root():
"""Root endpoint to check if the API is running."""
return {"message": "Attendance System API is running"}
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=7860) |