add face detector

align_all_parallel.py

@@ -0,0 +1,240 @@
+"""
+brief: face alignment with FFHQ method (https://github.com/NVlabs/ffhq-dataset)
+author: lzhbrian (https://lzhbrian.me)
+date: 2020.1.5
+note: code is heavily borrowed from 
+	https://github.com/NVlabs/ffhq-dataset
+	http://dlib.net/face_landmark_detection.py.html
+
+requirements:
+	apt install cmake
+	conda install Pillow numpy scipy
+	pip install dlib
+	# download face landmark model from:
+	# http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
+"""
+from argparse import ArgumentParser
+import time
+import numpy as np
+import PIL
+import PIL.Image
+import os
+import scipy
+import scipy.ndimage
+import dlib
+import multiprocessing as mp
+import math
+
+
+SHAPE_PREDICTOR_PATH = "shape_predictor_68_face_landmarks.dat"
+
+
+def get_landmark(filepath, predictor, i=None):
+    """get landmark with dlib
+    :return: np.array shape=(68, 2)
+    """
+    detector = dlib.get_frontal_face_detector()
+
+    img = dlib.load_rgb_image(filepath)
+    dets = detector(img, 1)
+
+    #for k, d in enumerate(dets):
+    if i is None:
+        i = len(dets) - 1
+    try:
+        shape = predictor(img, dets[i])
+    except IndexError:
+        print("Face not found")
+        return 
+    t = list(shape.parts())
+    a = []
+    for tt in t:
+        a.append([tt.x, tt.y])
+    lm = np.array(a)
+    return lm
+
+
+def align_face(filepath, predictor, idx=None):
+    """
+    :param filepath: str
+    :return: PIL Image
+    """
+
+    lm = get_landmark(filepath, predictor, i=idx)
+
+    lm_chin = lm[0:17]  # left-right
+    lm_eyebrow_left = lm[17:22]  # left-right
+    lm_eyebrow_right = lm[22:27]  # left-right
+    lm_nose = lm[27:31]  # top-down
+    lm_nostrils = lm[31:36]  # top-down
+    lm_eye_left = lm[36:42]  # left-clockwise
+    lm_eye_right = lm[42:48]  # left-clockwise
+    lm_mouth_outer = lm[48:60]  # left-clockwise
+    lm_mouth_inner = lm[60:68]  # left-clockwise
+
+    # Calculate auxiliary vectors.
+    eye_left = np.mean(lm_eye_left, axis=0)
+    eye_right = np.mean(lm_eye_right, axis=0)
+    eye_avg = (eye_left + eye_right) * 0.5
+    eye_to_eye = eye_right - eye_left
+    mouth_left = lm_mouth_outer[0]
+    mouth_right = lm_mouth_outer[6]
+    mouth_avg = (mouth_left + mouth_right) * 0.5
+    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)
+    y = np.flipud(x) * [-1, 1]
+    c = eye_avg + eye_to_mouth * 0.1
+    quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
+    qsize = np.hypot(*x) * 2
+
+    # read image
+    img = PIL.Image.open(filepath)
+
+    output_size = 256
+    transform_size = 256
+    enable_padding = True
+
+    # 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 = PIL.Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), "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.ANTIALIAS)
+
+    # Save aligned image.
+    return img
+
+
+def chunks(lst, n):
+    """Yield successive n-sized chunks from lst."""
+    for i in range(0, len(lst), n):
+        yield lst[i : i + n]
+
+
+def extract_on_paths(file_paths):
+    predictor = dlib.shape_predictor(SHAPE_PREDICTOR_PATH)
+    pid = mp.current_process().name
+    print(f"\t{pid} is starting to extract on #{len(file_paths)} images")
+    tot_count = len(file_paths)
+    count = 0
+    for file_path, res_path in file_paths:
+        count += 1
+        if count % 100 == 0:
+            print(f"{pid} done with {count}/{tot_count}")
+        try:
+            res = align_face(file_path, predictor)
+            res = res.convert("RGB")
+            os.makedirs(os.path.dirname(res_path), exist_ok=True)
+            res.save(res_path)
+        except Exception:
+            continue
+    print("\tDone!")
+
+
+def parse_args():
+    parser = ArgumentParser(add_help=False)
+    parser.add_argument("--num_threads", type=int, default=1)
+    parser.add_argument("--root_path", type=str, default="")
+    args = parser.parse_args()
+    return args
+
+
+def run(args):
+    root_path = args.root_path
+    out_crops_path = root_path + "_crops"
+    if not os.path.exists(out_crops_path):
+        os.makedirs(out_crops_path, exist_ok=True)
+
+    file_paths = []
+    for root, dirs, files in os.walk(root_path):
+        for file in files:
+            file_path = os.path.join(root, file)
+            fname = os.path.join(out_crops_path, os.path.relpath(file_path, root_path))
+            res_path = f"{os.path.splitext(fname)[0]}.jpg"
+            if os.path.splitext(file_path)[1] == ".txt" or os.path.exists(res_path):
+                continue
+            file_paths.append((file_path, res_path))
+
+    file_chunks = list(
+        chunks(file_paths, int(math.ceil(len(file_paths) / args.num_threads)))
+    )
+    print(len(file_chunks))
+    pool = mp.Pool(args.num_threads)
+    print(f"Running on {len(file_paths)} paths\nHere we goooo")
+    tic = time.time()
+    pool.map(extract_on_paths, file_chunks)
+    toc = time.time()
+    print(f"Mischief managed in {str(toc - tic)}s")
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    run(args)

app.py

@@ -23,11 +23,14 @@ def run_alignment(image_path,idx=None):
     predictor = dlib.shape_predictor("pretrained_models/shape_predictor_68_face_landmarks.dat")
     aligned_image = align_face(filepath=image_path, predictor=predictor, idx=idx) 
     print("Aligned image has shape: {}".format(aligned_image.size))
+
     return aligned_image 
 
 def predict(inp):
   #with torch.no_grad():     
-  return inp
+  inp.save("imgs/input.png")
+  out = run_alignment("imgs/input.png", idx=0)
+  return out
 
 
 gr.Interface(fn=predict, 

pretrained_models/shape_predictor_68_face_landmarks.dat

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fbdc2cb80eb9aa7a758672cbfdda32ba6300efe9b6e6c7a299ff7e736b11b92f
+size 99693937