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from ultralyticsplus import YOLO
from PIL import Image
import numpy as np
from swin import load_model, get_embeddings
import matplotlib.pyplot as plt
import sqlite3
import pathlib
def load_detector():
# load model
model = YOLO('https://github.com/akanametov/yolov8-face/releases/download/v0.0.0/yolov8n-face.pt')
# set model parameters
model.overrides['conf'] = 0.25 # NMS confidence threshold
model.overrides['iou'] = 0.45 # NMS IoU threshold
model.overrides['agnostic_nms'] = False # NMS class-agnostic
model.overrides['max_det'] = 50 # maximum number of detections per image
return model
def extract_faces(model, image):
# perform inference
results = model.predict(image)
ids = np.array(results[0].boxes.xyxy).astype(np.int32)
img = Image.open(image)
crops = []
for id in ids:
crops.append(Image.fromarray(np.array(img)[id[1] : id[3], id[0]: id[2]]))
return crops, np.argmax(np.array(results[0].boxes.conf))
def findCosineDistance(source_representation, test_representation):
a = np.matmul(np.transpose(source_representation), test_representation)
b = np.sum(np.multiply(source_representation, source_representation))
c = np.sum(np.multiply(test_representation, test_representation))
return 1 - (a / (np.sqrt(b) * np.sqrt(c)))
def recognition(imgs, ids, names, source_faces, d, source_imgs):
cols = 4
rows = int(np.ceil(len(imgs)/2))
img_count = 0
fig, axes = plt.subplots(nrows=rows, ncols=cols, figsize=(15,rows*3))
for i in range(rows):
for j in range(cols):
if img_count < len(imgs):
if(j%2):
axes[i, j].set_title(f"Confidence: {1 - d[ids[img_count]][img_count]: .2f}")
axes[i, j].imshow(imgs[img_count])
axes[i, j].set_axis_off()
img_count+=1
else:
if names[img_count] == "Unknown":
axes[i, j].set_title(f"Unknown")
else:
axes[i, j].set_title(f"Roll No.:{source_imgs[ids[img_count]].split('/')[-1].split('.')[0]}")
axes[i, j].imshow(source_faces[ids[img_count]])
axes[i, j].set_axis_off()
plt.savefig("Recognition.jpg")
def create(c):
pathlib.Path("images").mkdir(parents=True, exist_ok=True)
pathlib.Path("embeds").mkdir(parents=True, exist_ok=True)
c.execute('''CREATE TABLE IF NOT EXISTS students
(name text, email text, roll_no text, image text, embeddings text)''')
c.execute('''CREATE TABLE IF NOT EXISTS attendance
(date text, roll_no text, present text)''')
def insert(c, name, email, roll_no, image, embeddings):
c.execute("INSERT INTO students VALUES (?, ?, ?, ?, ?)", (name, email, roll_no, image, embeddings)) |