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import cv2
import gradio as gr
import numpy as np
import matplotlib.pyplot as plt
from math import atan2
from os import listdir, path
from PIL import Image as PImage
OUT_W = 130
OUT_H = 170
OUT_EYE_SPACE = 64
OUT_NOSE_TOP = 72
EYE_0_IDX = 36
EYE_1_IDX = 45
haarcascade = "./models/haarcascade_frontalface_alt2.xml"
face_detector = cv2.CascadeClassifier(haarcascade)
LBFmodel = "./models/lbfmodel.yaml"
landmark_detector = cv2.face.createFacemarkLBF()
landmark_detector.loadModel(LBFmodel)
NUM_OUTS = 16
all_outputs = [gr.Image(format="jpeg", visible=False) for _ in range(NUM_OUTS)]
def face(img_in):
out_pad = NUM_OUTS * [gr.Image(visible=False)]
if img_in is None:
return out_pad
pimg = img_in.convert("L")
pimg.thumbnail((1000,1000))
imgg = np.array(pimg).copy()
iw,ih = pimg.size
faces = face_detector.detectMultiScale(imgg)
if len(faces) < 1:
return out_pad
biggest_faces = faces[np.argsort(-faces[:,2])]
_, landmarks = landmark_detector.fit(imgg, biggest_faces)
if len(landmarks) < 1:
return out_pad
out_images = []
for landmark in landmarks:
eye0 = np.array(landmark[0][EYE_0_IDX])
eye1 = np.array(landmark[0][EYE_1_IDX])
mid = np.mean([eye0, eye1], axis=0)
eye_line = eye1 - eye0
tilt = atan2(eye_line[1], eye_line[0])
tilt_deg = 180 * tilt / np.pi
scale = OUT_EYE_SPACE / abs(eye0[0] - eye1[0])
pimgs = pimg.resize((int(iw * scale), int(ih * scale)), resample=PImage.Resampling.LANCZOS)
# rotate around nose
new_mid = [int(c * scale) for c in mid]
crop_box = (new_mid[0] - (OUT_W // 2),
new_mid[1] - OUT_NOSE_TOP,
new_mid[0] + (OUT_W // 2),
new_mid[1] + (OUT_H - OUT_NOSE_TOP))
img_out = pimgs.rotate(tilt_deg, center=new_mid, resample=PImage.Resampling.BICUBIC).crop(crop_box)
out_images.append(gr.Image(img_out, visible=True))
out_images += out_pad
return out_images[:NUM_OUTS]
with gr.Blocks() as demo:
gr.Markdown("""
# 9103H 2024F Face Alignment Tool.
## Interface for face detection, alignment, cropping\
to help create dataset for [HWXX](https://github.com/DM-GY-9103-2024F-H/).
""")
gr.Interface(
face,
inputs=gr.Image(type="pil"),
outputs=all_outputs,
cache_examples=True,
examples=[["./imgs/03.webp"], ["./imgs/11.jpg"]],
allow_flagging="never",
)
if __name__ == "__main__":
demo.launch()
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