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import cv2 |
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import gradio as gr |
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import numpy as np |
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import matplotlib.pyplot as plt |
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from math import atan2 |
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from os import listdir, path |
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from PIL import Image as PImage |
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OUT_W = 130 |
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OUT_H = 170 |
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OUT_EYE_SPACE = 64 |
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OUT_NOSE_TOP = 72 |
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EYE_0_IDX = 36 |
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EYE_1_IDX = 45 |
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haarcascade = "./models/haarcascade_frontalface_alt2.xml" |
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face_detector = cv2.CascadeClassifier(haarcascade) |
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LBFmodel = "./models/lbfmodel.yaml" |
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landmark_detector = cv2.face.createFacemarkLBF() |
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landmark_detector.loadModel(LBFmodel) |
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NUM_OUTS = 16 |
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all_outputs = [gr.Image(format="jpeg") for _ in range(NUM_OUTS)] |
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def face(img_in): |
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out_pad = NUM_OUTS * [gr.Image(visible=False)] |
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if img_in is None: |
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return out_pad |
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pimg = img_in.convert("L") |
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pimg.thumbnail((1000,1000)) |
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imgg = np.array(pimg).copy() |
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iw,ih = pimg.size |
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faces = face_detector.detectMultiScale(imgg) |
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if len(faces) < 1: |
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return out_pad |
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biggest_faces = faces[np.argsort(-faces[:,2])] |
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_, landmarks = landmark_detector.fit(imgg, biggest_faces) |
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if len(landmarks) < 1: |
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return out_pad |
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out_images = [] |
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for landmark in landmarks: |
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eye0 = np.array(landmark[0][EYE_0_IDX]) |
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eye1 = np.array(landmark[0][EYE_1_IDX]) |
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mid = np.mean([eye0, eye1], axis=0) |
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eye_line = eye1 - eye0 |
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tilt = atan2(eye_line[1], eye_line[0]) |
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tilt_deg = 180 * tilt / np.pi |
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scale = OUT_EYE_SPACE / abs(eye0[0] - eye1[0]) |
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pimgs = pimg.resize((int(iw * scale), int(ih * scale)), resample=PImage.Resampling.LANCZOS) |
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new_mid = [int(c * scale) for c in mid] |
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crop_box = (new_mid[0] - (OUT_W // 2), |
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new_mid[1] - OUT_NOSE_TOP, |
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new_mid[0] + (OUT_W // 2), |
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new_mid[1] + (OUT_H - OUT_NOSE_TOP)) |
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img_out = pimgs.rotate(tilt_deg, center=new_mid, resample=PImage.Resampling.BICUBIC).crop(crop_box) |
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out_images.append(gr.Image(img_out, visible=True)) |
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out_images += out_pad |
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return out_images[:NUM_OUTS] |
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with gr.Blocks() as demo: |
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gr.Markdown(""" |
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# 9103H 2024F Face Alignment Tool. |
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## Interface for face detection, alignment, cropping\ |
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to help create dataset for [HWXX](https://github.com/DM-GY-9103-2024F-H/). |
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""") |
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gr.Interface( |
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face, |
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inputs=gr.Image(type="pil"), |
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outputs=all_outputs, |
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cache_examples=True, |
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examples=[["./imgs/03.webp"], ["./imgs/11.jpg"]] |
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) |
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if __name__ == "__main__": |
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demo.launch() |
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