batches.py

import PIL.Image
from multiprocessing.pool import ThreadPool
import numpy as np
import pickle
import os
import glob
import cv2
import math


# n_boxes = 8
n_boxes = 1


class BufferedWrapper(object):
    """Fetch next batch asynchronuously to avoid bottleneck during GPU
    training."""
    def __init__(self, gen):
        self.gen = gen
        self.n = gen.n
        self.pool = ThreadPool(1)
        self._async_next()


    def _async_next(self):
        self.buffer_ = self.pool.apply_async(next, (self.gen,))


    def __next__(self):
        result = self.buffer_.get()
        self._async_next()
        return result


def load_img(path, target_size):
    """Load image. target_size is specified as (height, width, channels)
    where channels == 1 means grayscale. uint8 image returned."""
    img = PIL.Image.open(path)
    grayscale = target_size[2] == 1
    if grayscale:
        if img.mode != 'L':
            img = img.convert('L')
    else:
        if img.mode != 'RGB':
            img = img.convert('RGB')
    wh_tuple = (target_size[1], target_size[0])
    if img.size != wh_tuple:
        img = img.resize(wh_tuple, resample = PIL.Image.BILINEAR)

    x = np.asarray(img, dtype = "uint8")
    if len(x.shape) == 2:
        x = np.expand_dims(x, -1)

    return x


def save_image(X, name):
    """Save image as png."""
    fname = os.path.join(out_dir, name + ".png")
    PIL.Image.fromarray(X).save(fname)


def preprocess(x):
    """From uint8 image to [-1,1]."""
    return np.cast[np.float32](x / 127.5 - 1.0)


def postprocess(x):
    """[-1,1] to uint8."""
    x = (x + 1.0) / 2.0
    x = np.clip(255 * x, 0, 255)
    x = np.cast[np.uint8](x)
    return x


def tile(X, rows, cols):
    """Tile images for display."""
    tiling = np.zeros((rows * X.shape[1], cols * X.shape[2], X.shape[3]), dtype = X.dtype)
    for i in range(rows):
        for j in range(cols):
            idx = i * cols + j
            if idx < X.shape[0]:
                img = X[idx,...]
                tiling[
                        i*X.shape[1]:(i+1)*X.shape[1],
                        j*X.shape[2]:(j+1)*X.shape[2],
                        :] = img
    return tiling


def plot_batch(X, out_path):
    """Save batch of images tiled."""
    n_channels = X.shape[3]
    if n_channels > 3:
        X = X[:,:,:,np.random.choice(n_channels, size = 3)]
    X = postprocess(X)
    rc = math.sqrt(X.shape[0])
    rows = cols = math.ceil(rc)
    canvas = tile(X, rows, cols)
    canvas = np.squeeze(canvas)
    PIL.Image.fromarray(canvas).save(out_path)


def make_joint_img(img_shape, jo, joints):
    # three channels: left, right, center
    scale_factor = img_shape[1] / 128
    thickness = int(3 * scale_factor)
    imgs = list()
    for i in range(3):
        imgs.append(np.zeros(img_shape[:2], dtype = "uint8"))

    body = ["lhip", "lshoulder", "rshoulder", "rhip"]
    body_pts = np.array([[joints[jo.index(part),:] for part in body]])
    if np.min(body_pts) >= 0:
        body_pts = np.int_(body_pts)
        cv2.fillPoly(imgs[2], body_pts, 255)

    right_lines = [
            ("rankle", "rknee"),
            ("rknee", "rhip"),
            ("rhip", "rshoulder"),
            ("rshoulder", "relbow"),
            ("relbow", "rwrist")]
    for line in right_lines:
        l = [jo.index(line[0]), jo.index(line[1])]
        if np.min(joints[l]) >= 0:
            a = tuple(np.int_(joints[l[0]]))
            b = tuple(np.int_(joints[l[1]]))
            cv2.line(imgs[0], a, b, color = 255, thickness = thickness)

    left_lines = [
            ("lankle", "lknee"),
            ("lknee", "lhip"),
            ("lhip", "lshoulder"),
            ("lshoulder", "lelbow"),
            ("lelbow", "lwrist")]
    for line in left_lines:
        l = [jo.index(line[0]), jo.index(line[1])]
        if np.min(joints[l]) >= 0:
            a = tuple(np.int_(joints[l[0]]))
            b = tuple(np.int_(joints[l[1]]))
            cv2.line(imgs[1], a, b, color = 255, thickness = thickness)

    rs = joints[jo.index("rshoulder")]
    ls = joints[jo.index("lshoulder")]
    cn = joints[jo.index("cnose")]
    neck = 0.5*(rs+ls)
    a = tuple(np.int_(neck))
    b = tuple(np.int_(cn))
    if np.min(a) >= 0 and np.min(b) >= 0:
        cv2.line(imgs[0], a, b, color = 127, thickness = thickness)
        cv2.line(imgs[1], a, b, color = 127, thickness = thickness)

    cn = tuple(np.int_(cn))
    leye = tuple(np.int_(joints[jo.index("leye")]))
    reye = tuple(np.int_(joints[jo.index("reye")]))
    if np.min(reye) >= 0 and np.min(leye) >= 0 and np.min(cn) >= 0:
        cv2.line(imgs[0], cn, reye, color = 255, thickness = thickness)
        cv2.line(imgs[1], cn, leye, color = 255, thickness = thickness)

    img = np.stack(imgs, axis = -1)
    if img_shape[-1] == 1:
        img = np.mean(img, axis = -1)[:,:,None]
    return img


def valid_joints(*joints):
    j = np.stack(joints)
    return (j >= 0).all()


def get_crop(bpart, joints, jo, wh, o_w, o_h, ar = 1.0):
    bpart_indices = [jo.index(b) for b in bpart]
    part_src = np.float32(joints[bpart_indices])

    # fall backs
    if not valid_joints(part_src):
        if bpart[0] == "lhip" and bpart[1] == "lknee":
            bpart = ["lhip"]
            bpart_indices = [jo.index(b) for b in bpart]
            part_src = np.float32(joints[bpart_indices])
        elif bpart[0] == "rhip" and bpart[1] == "rknee": 
            bpart = ["rhip"]
            bpart_indices = [jo.index(b) for b in bpart]
            part_src = np.float32(joints[bpart_indices])
        elif bpart[0] == "lshoulder" and bpart[1] == "rshoulder" and bpart[2] == "cnose": 
            bpart = ["lshoulder", "rshoulder", "rshoulder"]
            bpart_indices = [jo.index(b) for b in bpart]
            part_src = np.float32(joints[bpart_indices])


    if not valid_joints(part_src):
            return None

    if part_src.shape[0] == 1:
        # leg fallback
        a = part_src[0]
        b = np.float32([a[0],o_h - 1])
        part_src = np.float32([a,b])

    if part_src.shape[0] == 4:
        pass
    elif part_src.shape[0] == 3:
        # lshoulder, rshoulder, cnose
        if bpart == ["lshoulder", "rshoulder", "rshoulder"]:
            segment = part_src[1] - part_src[0]
            normal = np.array([-segment[1],segment[0]])
            if normal[1] > 0.0:
                normal = -normal

            a = part_src[0] + normal
            b = part_src[0]
            c = part_src[1]
            d = part_src[1] + normal
            part_src = np.float32([a,b,c,d])
        else:
            assert bpart == ["lshoulder", "rshoulder", "cnose"]
            neck = 0.5*(part_src[0] + part_src[1])
            neck_to_nose = part_src[2] - neck
            part_src = np.float32([neck + 2*neck_to_nose, neck])

            # segment box
            segment = part_src[1] - part_src[0]
            normal = np.array([-segment[1],segment[0]])
            alpha = 1.0 / 2.0
            a = part_src[0] + alpha*normal
            b = part_src[0] - alpha*normal
            c = part_src[1] - alpha*normal
            d = part_src[1] + alpha*normal
            #part_src = np.float32([a,b,c,d])
            part_src = np.float32([b,c,d,a])
    else:
        assert part_src.shape[0] == 2

        segment = part_src[1] - part_src[0]
        normal = np.array([-segment[1],segment[0]])
        alpha = ar / 2.0
        a = part_src[0] + alpha*normal
        b = part_src[0] - alpha*normal
        c = part_src[1] - alpha*normal
        d = part_src[1] + alpha*normal
        part_src = np.float32([a,b,c,d])

    dst = np.float32([[0.0,0.0],[0.0,1.0],[1.0,1.0],[1.0,0.0]])
    part_dst = np.float32(wh * dst)

    M = cv2.getPerspectiveTransform(part_src, part_dst)
    return M



def normalize(imgs, coords, stickmen, jo, box_factor):
    out_imgs = list()
    out_stickmen = list()

    bs = len(imgs)
    for i in range(bs):
        img = imgs[i]
        joints = coords[i]
        stickman = stickmen[i]

        h,w = img.shape[:2]
        o_h = h
        o_w = w
        h = h // 2**box_factor
        w = w // 2**box_factor
        wh = np.array([w,h])
        wh = np.expand_dims(wh, 0)

        bparts = [
                ["lshoulder","lhip","rhip","rshoulder"],
                ["lshoulder", "rshoulder", "cnose"],
                ["lshoulder","lelbow"],
                ["lelbow", "lwrist"],
                ["rshoulder","relbow"],
                ["relbow", "rwrist"],
                ["lhip", "lknee"],
                ["rhip", "rknee"]]
        ar = 0.5

        part_imgs = list()
        part_stickmen = list()
        for bpart in bparts:
            part_img = np.zeros((h,w,3))
            part_stickman = np.zeros((h,w,3))
            M = get_crop(bpart, joints, jo, wh, o_w, o_h, ar)

            if M is not None:
                part_img = cv2.warpPerspective(img, M, (h,w), borderMode = cv2.BORDER_REPLICATE)
                part_stickman = cv2.warpPerspective(stickman, M, (h,w), borderMode = cv2.BORDER_REPLICATE)

            part_imgs.append(part_img)
            part_stickmen.append(part_stickman)
        img = np.concatenate(part_imgs, axis = 2)
        stickman = np.concatenate(part_stickmen, axis = 2)

        out_imgs.append(img)
        out_stickmen.append(stickman)
    out_imgs = np.stack(out_imgs)
    out_stickmen = np.stack(out_stickmen)
    return out_imgs, out_stickmen



def normalize(imgs, stickmen, box_factor):
    out_imgs = list()
    out_stickmen = list()

    bs = len(imgs)
    for i in range(bs):
        img = imgs[i]
        stickman = stickmen[i]

        h,w = img.shape[:2]
        o_h = h
        o_w = w
        h = h // 2**box_factor
        w = w // 2**box_factor
        img = cv2.resize(img, (h, w))
        stickman = cv2.resize(stickman, (h, w))

        out_imgs.append(img)
        out_stickmen.append(stickman)
    out_imgs = np.stack(out_imgs)
    out_stickmen = np.stack(out_stickmen)
    return out_imgs, out_stickmen


class IndexFlow(object):
    """Batches from index file."""
    def __init__(
            self,
            shape,
            index_path,
            train,
            box_factor,
            fill_batches = True,
            shuffle = True,
            return_keys = ["imgs", "joints", "norm_imgs", "norm_joints"]):
        self.shape = shape
        self.batch_size = self.shape[0]
        self.img_shape = self.shape[1:]
        self.box_factor = box_factor
        # with open(index_path, "rb") as f:
        #     self.index = pickle.load(f)
        self.index = {}
        self.basepath = index_path #os.path.dirname(index_path)
        self.index["imgs"] =  glob.glob(os.path.join(index_path, os.path.join('trainB', '*')))
        trainsize = len(self.index["imgs"])
        print('trainsize = %d'%trainsize)
        self.index["train"] = [True]* trainsize
        self.index["imgs"].extend(glob.glob(os.path.join(index_path, os.path.join('testB', '*'))))
        testsize = len(glob.glob(os.path.join(index_path,os.path.join('testB', '*'))))
        self.index["train"].extend([False]*testsize)
        print('testsize = %d'%testsize)
        self.train = train
        self.fill_batches = fill_batches
        self.shuffle_ = shuffle
        self.return_keys = return_keys

        # self.jo = self.index["joint_order"]
        # rescale joint coordinates to image shape
        # h,w = self.img_shape[:2]
        # wh = np.array([[[w,h]]])
        self.index["joints"] = glob.glob(os.path.join(index_path,os.path.join('trainA', '*')))
        self.index["joints"].extend(glob.glob(os.path.join(index_path,os.path.join('trainA', '*'))))
        self.indices = np.array(
                [i for i in range(len(self.index["train"]))
                    if self._filter(i)])
        self.n = self.indices.shape[0]
        self.shuffle()


    def _filter(self, i):
        good = True
        good = good and (self.index["train"][i] == self.train)
        # joints = self.index["joints"][i]
        # required_joints = ["lshoulder","rshoulder","lhip","rhip"]
        # joint_indices = [self.jo.index(b) for b in required_joints]
        # joints = np.float32(joints[joint_indices])
        # good = good and valid_joints(joints)
        return good


    def __next__(self):
        batch = dict()

        # get indices for batch
        batch_start, batch_end = self.batch_start, self.batch_start + self.batch_size
        batch_indices = self.indices[batch_start:batch_end]
        if self.fill_batches and batch_indices.shape[0] != self.batch_size:
            n_missing = self.batch_size - batch_indices.shape[0]
            batch_indices = np.concatenate([batch_indices, self.indices[:n_missing]], axis = 0)
            assert batch_indices.shape[0] == self.batch_size
        batch_indices = np.array(batch_indices)
        batch["indices"] = batch_indices

        # prepare next batch
        if batch_end >= self.n:
            self.shuffle()
        else:
            self.batch_start = batch_end

        # prepare batch data
        # load images
        batch["imgs"] = list()
        for i in batch_indices:
            relpath = self.index["imgs"][i]
            path = relpath #os.path.join(self.basepath, relpath)
            batch["imgs"].append(load_img(path, target_size = self.img_shape))
        batch["imgs"] = np.stack(batch["imgs"])
        batch["imgs"] = preprocess(batch["imgs"])

        #load joints
        batch["joints"] = list()
        for i in batch_indices:
            relpath = self.index["joints"][i]
            path = relpath
            img = load_img(path, target_size = self.img_shape)
            batch["joints"].append(img)        
        batch["joints"] = np.stack(batch["joints"])
        batch["joints"] = preprocess(batch["joints"])
        # load joint coordinates
        # batch["joints_coordinates"] = [self.index["joints"][i] for i in batch_indices]

        # generate stickmen images from coordinates
        # batch["joints"] = list()
        # for joints in batch["joints_coordinates"]:
        #     img = make_joint_img(self.img_shape, self.jo, joints)
        #     batch["joints"].append(img)
        # batch["joints"] = np.stack(batch["joints"])
        # batch["joints"] = preprocess(batch["joints"])

        imgs, joints = normalize(batch["imgs"], batch["joints"], self.box_factor)
        
        # imgs, joints = batch["imgs"], batch["joints"]
        batch["norm_imgs"] = imgs
        batch["norm_joints"] = joints

        batch_list = [batch[k] for k in self.return_keys]
        return batch_list


    def shuffle(self):
        self.batch_start = 0
        if self.shuffle_:
            np.random.shuffle(self.indices)


def get_batches(
        shape,
        index_path,
        train,
        box_factor,
        fill_batches = True,
        shuffle = True,
        return_keys = ["imgs", "joints", "norm_imgs", "norm_joints"]):
    """Buffered IndexFlow."""
    flow = IndexFlow(shape, index_path, train, box_factor, fill_batches, shuffle, return_keys)
    return BufferedWrapper(flow)


if __name__ == "__main__":
    import sys
    if not len(sys.argv) == 2:
        print("Useage: {} <path to index.p>".format(sys.argv[0]))
        exit(1)

    batches = get_batches(
            shape = (16, 128, 128, 3),
            index_path = sys.argv[1],
            train = True,
            box_factor = 2,
            shuffle = True)
    X, C, XN, CN = next(batches)
    plot_batch(X, "images.png")
    plot_batch(C, "joints.png")