align.py

import numpy as np
import mediapipe as mp
from PIL import Image
import PIL
import scipy
import scipy.ndimage

mp_face_detection = mp.solutions.face_detection
face_detection = mp_face_detection.FaceDetection(min_detection_confidence=0.5, model_selection=0)

def aligns(pil_image,enable_padding=True,output_size=512,model_type="dlib",max_people=7):
    w,h = pil_image.size
    scale = 1
    if min(w,h) > output_size*2:
        scale = min(w,h) / (output_size*2)
        new_w = int(w/scale)
        new_h = int(h/scale)
        pil_image = pil_image.resize((new_w,new_h),PIL.Image.BILINEAR)
    
    numpy_im = np.array(pil_image)
    
    #Find the locations of faces
    locations,context = get_locations(numpy_im,model_type)#face_recognition.face_locations(numpy_im)
    n_found = len(locations)
    print("Faces found",n_found)
    if (n_found == 0):
        return []
    
    #How many are we going to return?
    n_to_return = min(n_found,max_people)
        
    #Return the largest ones
    areas = [(l[2] - l[0])*(l[1] - l[3]) for l in locations]
    indices = np.argpartition(areas, -n_to_return)[-n_to_return:]
    
    #Find the landmarks
    face_landmarks_list = get_landmarks(numpy_im,[locations[i] for i in indices],context,model_type)#face_recognition.face_landmarks(numpy_im,[locations[i]])

    #Package them up
    to_return = []
    for face in face_landmarks_list:
        im,quad = image_align(pil_image,face,enable_padding=enable_padding,output_size=output_size,transform_size=output_size)
        to_return.append((im,quad*scale))

    #Return them
    return to_return

def get_landmarks(numpy_array,locations,context,model_type="dlib"):
    '''
    model_type can be "dlib" or "mediapipe"
    context is the second result from get_locations
    '''
    assert(model_type in ["dlib","mediapipe"])

    if model_type == "dlib":
        return face_recognition.face_landmarks(numpy_array,locations)
    else:
        return [context[tuple(l)] for l in locations]

def landmarks_from_result(result,np_array):
    keypoint_names = ["left_eye", "right_eye", "nose" ,"mouth", "left_ear", "right_ear"]

    landmarks = {}
    for i,k in enumerate(result.location_data.relative_keypoints):
        x = round(k.x * np_array.shape[1])
        y = round(k.y * np_array.shape[0])
        landmarks[keypoint_names[i]] = np.array([x,y])

    return landmarks

def get_locations(numpy_array,model_type="dlib"):
    '''
    model_type can be "dlib" or "mediapipe"
    returns face locations and a context for fast landmark finding
    '''
    assert(model_type in ["dlib","mediapipe"])

    if model_type == "dlib":
        return face_recognition.face_locations(numpy_array),None
    else:
        results = face_detection.process(np.array(numpy_array))
        to_return = None
        im_h,im_w = numpy_array.shape[:2]
        box_list = []
        landmarks = {}
        if results.detections is None:
            return box_list,landmarks
        for result in results.detections:
            x = round(result.location_data.relative_bounding_box.xmin*im_w)
            y = round(result.location_data.relative_bounding_box.ymin*im_h)
            w = round(result.location_data.relative_bounding_box.width*im_w)
            h = round(result.location_data.relative_bounding_box.height*im_h)
            box_list.append([x,y,x+w-1,y+h-1])
            landmarks[(x,y,x+w-1,y+h-1)] = landmarks_from_result(result,numpy_array)

        return box_list,landmarks

def align(pil_image,enable_padding=True,output_size=512,model_type="dlib"):
    w,h = pil_image.size
    scale = 1
    if min(w,h) > output_size*2:
        scale = min(w,h) / (output_size*2)
        new_w = int(w/scale)
        new_h = int(h/scale)
        pil_image = pil_image.resize((new_w,new_h),PIL.Image.BILINEAR)
    
    numpy_im = np.array(pil_image)
    locations,context = get_locations(numpy_im,model_type)#face_recognition.face_locations(numpy_im)
    if (len(locations) == 0):
        return None
    areas = [(l[2] - l[0])*(l[1] - l[3]) for l in locations]
    i = np.argmax(areas)
    face_landmarks_list = get_landmarks(numpy_im,[locations[i]],context,model_type)#face_recognition.face_landmarks(numpy_im,[locations[i]])
    im,quad = image_align(Image.fromarray(numpy_im),face_landmarks_list[0],enable_padding=enable_padding,output_size=output_size,transform_size=4*output_size)
    return im,quad*scale
    
def image_align(img, lm, output_size=1024, transform_size=4096, enable_padding=True, x_scale=1, y_scale=1, em_scale=0.1, alpha=False):
    # Align function from FFHQ dataset pre-processing step
    # https://github.com/NVlabs/ffhq-dataset/blob/master/download_ffhq.py

    # Compute the land marks differently depending on what face finding model has been used
    if type(lm["left_eye"]) == np.ndarray and lm["left_eye"].size == 2:
        #Media pipe
        eye_left = lm["left_eye"]
        eye_right = lm["right_eye"]
        mouth_avg = lm["mouth"]
    else:
        #DLIB
        eye_left = np.mean(lm["left_eye"], axis=0)
        eye_right = np.mean(lm["right_eye"], axis=0)
        mouth_avg = (np.mean( lm["top_lip"],axis=0) + np.mean(lm["bottom_lip"],axis=0)) * 0.5

    # Calculate auxiliary vectors.
    eye_avg      = (eye_left + eye_right) * 0.5
    eye_to_eye   = eye_right - eye_left
    eye_to_mouth = mouth_avg - eye_avg

    # Choose oriented crop rectangle.
    x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]
    x /= np.hypot(*x)
    x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)
    x *= x_scale
    y = np.flipud(x) * [-y_scale, y_scale]
    c = eye_avg + eye_to_mouth * em_scale
    quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y]) #Quad means box
    qsize = np.hypot(*x) * 2
    original_quad = np.copy(quad)

    # Load in-the-wild image.
    #img = img.convert('RGBA').convert('RGB')  #I've already taken care of this

    # Shrink.
    shrink = int(np.floor(qsize / output_size * 0.5))
    if shrink > 1:
        rsize = (int(np.rint(float(img.size[0]) / shrink)), int(np.rint(float(img.size[1]) / shrink)))
        img = img.resize(rsize, PIL.Image.ANTIALIAS)
        quad /= shrink
        qsize /= shrink

    # Crop.
    border = max(int(np.rint(qsize * 0.1)), 3)
    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]))))
    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]))
    if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]:
        img = img.crop(crop)
        quad -= crop[0:2]

    # Pad.
    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]))))
    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))
    if enable_padding and max(pad) > border - 4:
        pad = np.maximum(pad, int(np.rint(qsize * 0.3)))
        img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')
        h, w, _ = img.shape
        y, x, _ = np.ogrid[:h, :w, :1]
        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]))
        blur = qsize * 0.02
        img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
        img += (np.median(img, axis=(0,1)) - img) * np.clip(mask, 0.0, 1.0)
        img = np.uint8(np.clip(np.rint(img), 0, 255))
        if alpha:
            mask = 1-np.clip(3.0 * mask, 0.0, 1.0)
            mask = np.uint8(np.clip(np.rint(mask*255), 0, 255))
            img = np.concatenate((img, mask), axis=2)
            img = PIL.Image.fromarray(img, 'RGBA')
        else:
            img = PIL.Image.fromarray(img, 'RGB')
        quad += pad[:2]

    # Transform.
    img = img.transform((transform_size, transform_size), PIL.Image.QUAD, (quad + 0.5).flatten(), PIL.Image.BILINEAR)
    if output_size < transform_size:
        img = img.resize((output_size, output_size), PIL.Image.LANCZOS)

    # Save aligned image.
    return img,original_quad