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import dlib |
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import numpy as np |
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import os |
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from PIL import Image |
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from PIL import ImageOps |
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from scipy.ndimage import gaussian_filter |
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import cv2 |
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MODEL_PATH = "shape_predictor_5_face_landmarks.dat" |
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detector = dlib.get_frontal_face_detector() |
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def align(image_in, face_index=0, output_size=256): |
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try: |
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image_in = ImageOps.exif_transpose(image_in) |
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except: |
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print("exif problem, not rotating") |
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landmarks = list(get_landmarks(image_in)) |
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n_faces = len(landmarks) |
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face_index = min(n_faces-1, face_index) |
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if n_faces == 0: |
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aligned_image = image_in |
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quad = None |
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else: |
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aligned_image, quad = image_align(image_in, landmarks[face_index], output_size=output_size) |
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return aligned_image, n_faces, quad |
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def composite_images(quad, img, output): |
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"""Composite an image into and output canvas according to transformed co-ords""" |
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output = output.convert("RGBA") |
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img = img.convert("RGBA") |
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input_size = img.size |
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src = np.array(((0, 0), (0, input_size[1]), input_size, (input_size[0], 0)), dtype=np.float32) |
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dst = np.float32(quad) |
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mtx = cv2.getPerspectiveTransform(dst, src) |
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img = img.transform(output.size, Image.PERSPECTIVE, mtx.flatten(), Image.BILINEAR) |
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output.alpha_composite(img) |
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return output.convert("RGB") |
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def get_landmarks(image): |
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"""Get landmarks from PIL image""" |
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shape_predictor = dlib.shape_predictor(MODEL_PATH) |
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max_size = max(image.size) |
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reduction_scale = int(max_size/512) |
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if reduction_scale == 0: |
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reduction_scale = 1 |
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downscaled = image.reduce(reduction_scale) |
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img = np.array(downscaled) |
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detections = detector(img, 0) |
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for detection in detections: |
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try: |
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face_landmarks = [(reduction_scale*item.x, reduction_scale*item.y) for item in shape_predictor(img, detection).parts()] |
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yield face_landmarks |
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except Exception as e: |
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print(e) |
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def image_align(src_img, face_landmarks, output_size=512, transform_size=2048, enable_padding=True, x_scale=1, y_scale=1, em_scale=0.1, alpha=False): |
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lm = np.array(face_landmarks) |
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lm_eye_left = lm[2:3] |
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lm_eye_right = lm[0:1] |
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eye_left = np.mean(lm_eye_left, axis=0) |
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eye_right = np.mean(lm_eye_right, axis=0) |
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eye_avg = (eye_left + eye_right) * 0.5 |
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eye_to_eye = 0.71*(eye_right - eye_left) |
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mouth_avg = lm[4] |
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eye_to_mouth = 1.35*(mouth_avg - eye_avg) |
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x = eye_to_eye.copy() |
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x /= np.hypot(*x) |
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x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8) |
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x *= x_scale |
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y = np.flipud(x) * [-y_scale, y_scale] |
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c = eye_avg + eye_to_mouth * em_scale |
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quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y]) |
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quad_orig = quad.copy() |
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qsize = np.hypot(*x) * 2 |
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img = src_img.convert('RGBA').convert('RGB') |
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shrink = int(np.floor(qsize / output_size * 0.5)) |
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if shrink > 1: |
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rsize = (int(np.rint(float(img.size[0]) / shrink)), int(np.rint(float(img.size[1]) / shrink))) |
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img = img.resize(rsize, Image.ANTIALIAS) |
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quad /= shrink |
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qsize /= shrink |
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border = max(int(np.rint(qsize * 0.1)), 3) |
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crop = (int(np.floor(min(quad[:,0]))), int(np.floor(min(quad[:,1]))), int(np.ceil(max(quad[:,0]))), int(np.ceil(max(quad[:,1])))) |
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crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]), min(crop[3] + border, img.size[1])) |
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if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]: |
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img = img.crop(crop) |
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quad -= crop[0:2] |
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pad = (int(np.floor(min(quad[:,0]))), int(np.floor(min(quad[:,1]))), int(np.ceil(max(quad[:,0]))), int(np.ceil(max(quad[:,1])))) |
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pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - img.size[0] + border, 0), max(pad[3] - img.size[1] + border, 0)) |
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if enable_padding and max(pad) > border - 4: |
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pad = np.maximum(pad, int(np.rint(qsize * 0.3))) |
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img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect') |
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h, w, _ = img.shape |
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y, x, _ = np.ogrid[:h, :w, :1] |
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mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w-1-x) / pad[2]), 1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h-1-y) / pad[3])) |
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blur = qsize * 0.02 |
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img += (gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0) |
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img += (np.median(img, axis=(0,1)) - img) * np.clip(mask, 0.0, 1.0) |
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img = np.uint8(np.clip(np.rint(img), 0, 255)) |
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if alpha: |
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mask = 1-np.clip(3.0 * mask, 0.0, 1.0) |
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mask = np.uint8(np.clip(np.rint(mask*255), 0, 255)) |
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img = np.concatenate((img, mask), axis=2) |
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img = Image.fromarray(img, 'RGBA') |
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else: |
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img = Image.fromarray(img, 'RGB') |
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quad += pad[:2] |
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img = img.transform((transform_size, transform_size), Image.QUAD, (quad + 0.5).flatten(), Image.BILINEAR) |
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if output_size < transform_size: |
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img = img.resize((output_size, output_size), Image.ANTIALIAS) |
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return img, quad_orig |
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