main.py

import argparse
import os
import matplotlib.pyplot as plt
from matplotlib.pyplot import imshow
import scipy.io
import scipy.misc
import numpy as np
import pandas as pd
import PIL
import tensorflow as tf
from keras.layers.merge import concatenate
from keras import backend as K
from keras.layers import Input, Lambda, Conv2D
from keras.models import load_model, Model
from functools import reduce
from obj_utils import read_classes, read_anchors, generate_colors, preprocess_image, draw_boxes, scale_boxes, get_name_string

from keras_darknet19 import (DarknetConv2D, DarknetConv2D_BN_Leaky, darknet_body)


## YOLOv2 defined in Keras, from yad2k ##
def yolo_head(feats, anchors, num_classes):
    """Convert final layer features to bounding box parameters.

    Parameters
    ----------
    feats : tensor
        Final convolutional layer features.
    anchors : array-like
        Anchor box widths and heights.
    num_classes : int
        Number of target classes.

    Returns
    -------
    box_xy : tensor
        x, y box predictions adjusted by spatial location in conv layer.
    box_wh : tensor
        w, h box predictions adjusted by anchors and conv spatial resolution.
    box_conf : tensor
        Probability estimate for whether each box contains any object.
    box_class_pred : tensor
        Probability distribution estimate for each box over class labels.
    """
    num_anchors = len(anchors)
    # Reshape to batch, height, width, num_anchors, box_params.
    anchors_tensor = K.reshape(K.variable(anchors), [1, 1, 1, num_anchors, 2])
    # Static implementation for fixed models.
    # TODO: Remove or add option for static implementation.
    # _, conv_height, conv_width, _ = K.int_shape(feats)
    # conv_dims = K.variable([conv_width, conv_height])

    # Dynamic implementation of conv dims for fully convolutional model.
    conv_dims = K.shape(feats)[1:3]  # assuming channels last
    # In YOLO the height index is the inner most iteration.
    conv_height_index = K.arange(0, stop=conv_dims[0])
    conv_width_index = K.arange(0, stop=conv_dims[1])
    conv_height_index = K.tile(conv_height_index, [conv_dims[1]])


    conv_width_index = K.tile(K.expand_dims(conv_width_index, 0), [conv_dims[0], 1])
    conv_width_index = K.flatten(K.transpose(conv_width_index))
    conv_index = K.transpose(K.stack([conv_height_index, conv_width_index]))
    conv_index = K.reshape(conv_index, [1, conv_dims[0], conv_dims[1], 1, 2])
    conv_index = K.cast(conv_index, K.dtype(feats))

    feats = K.reshape(feats, [-1, conv_dims[0], conv_dims[1], num_anchors, num_classes + 5])
    conv_dims = K.cast(K.reshape(conv_dims, [1, 1, 1, 1, 2]), K.dtype(feats))



    box_confidence = K.sigmoid(feats[..., 4:5])
    box_xy = K.sigmoid(feats[..., :2])
    box_wh = K.exp(feats[..., 2:4])
    box_class_probs = K.softmax(feats[..., 5:])

    # Adjust preditions to each spatial grid point and anchor size.
    # Note: YOLO iterates over height index before width index.
    box_xy = (box_xy + conv_index) / conv_dims
    box_wh = box_wh * anchors_tensor / conv_dims

    return box_confidence, box_xy, box_wh, box_class_probs


def yolo_boxes_to_corners(box_xy, box_wh):
    """Convert YOLO box predictions to bounding box corners."""
    box_mins = box_xy - (box_wh / 2.)
    box_maxes = box_xy + (box_wh / 2.)

    return K.concatenate([
        box_mins[..., 1:2],  # y_min
        box_mins[..., 0:1],  # x_min
        box_maxes[..., 1:2],  # y_max
        box_maxes[..., 0:1]  # x_max
    ])
def compose(*funcs):
    """Compose arbitrarily many functions, evaluated left to right.

    Reference: https://mathieularose.com/function-composition-in-python/
    """
    # return lambda x: reduce(lambda v, f: f(v), funcs, x)
    if funcs:
        return reduce(lambda f, g: lambda *a, **kw: g(f(*a, **kw)), funcs)
    else:
        raise ValueError('Composition of empty sequence not supported.')




def yolo_loss(args,anchors,num_classes,rescore_confidence=False,print_loss=False):

        """YOLO localization loss function.

    Parameters
    ----------
    yolo_output : tensor
        Final convolutional layer features.

    true_boxes : tensor
        Ground truth boxes tensor with shape [batch, num_true_boxes, 5]
        containing box x_center, y_center, width, height, and class.

    detectors_mask : array
        0/1 mask for detector positions where there is a matching ground truth.

    matching_true_boxes : array
        Corresponding ground truth boxes for positive detector positions.
        Already adjusted for conv height and width.

    anchors : tensor
        Anchor boxes for model.

    num_classes : int
        Number of object classes.

    rescore_confidence : bool, default=False
        If true then set confidence target to IOU of best predicted box with
        the closest matching ground truth box.

    print_loss : bool, default=False
        If True then use a tf.Print() to print the loss components.

    Returns
    -------
    mean_loss : float
        mean localization loss across minibatch
    """
    (yolo_output, true_boxes, detectors_mask, matching_true_boxes) = args
    num_anchors = len(anchors)
    object_scale = 5
    no_object_scale = 1
    class_scale = 1
    coordinates_scale = 1
    pred_xy, pred_wh, pred_confidence, pred_class_prob = yolo_head(
        yolo_output, anchors, num_classes)

    # Unadjusted box predictions for loss.

    yolo_output_shape = K.shape(yolo_output)
    feats = K.reshape(yolo_output, [
        -1, yolo_output_shape[1], yolo_output_shape[2], num_anchors,
        num_classes + 5
    ])
    pred_boxes = K.concatenate(
        (K.sigmoid(feats[..., 0:2]), feats[..., 2:4]), axis=-1)


    # IOUs may be off due to different aspect ratio.

    # Expand pred x,y,w,h to allow comparison with ground truth.
    # batch, conv_height, conv_width, num_anchors, num_true_boxes, box_params
    pred_xy = K.expand_dims(pred_xy, 4)
    pred_wh = K.expand_dims(pred_wh, 4)

    pred_wh_half = pred_wh / 2.
    pred_mins = pred_xy - pred_wh_half
    pred_maxes = pred_xy + pred_wh_half

    true_boxes_shape = K.shape(true_boxes)

    # batch, conv_height, conv_width, num_anchors, num_true_boxes, box_params
    true_boxes = K.reshape(true_boxes, [
        true_boxes_shape[0], 1, 1, 1, true_boxes_shape[1], true_boxes_shape[2]
    ])
    true_xy = true_boxes[..., 0:2]
    true_wh = true_boxes[..., 2:4]

    # Find IOU of each predicted box with each ground truth box.
    true_wh_half = true_wh / 2.
    true_mins = true_xy - true_wh_half
    true_maxes = true_xy + true_wh_half

    intersect_mins = K.maximum(pred_mins, true_mins)
    intersect_maxes = K.minimum(pred_maxes, true_maxes)
    intersect_wh = K.maximum(intersect_maxes - intersect_mins, 0.)
    intersect_areas = intersect_wh[..., 0] * intersect_wh[..., 1]

    pred_areas = pred_wh[..., 0] * pred_wh[..., 1]
    true_areas = true_wh[..., 0] * true_wh[..., 1]

    union_areas = pred_areas + true_areas - intersect_areas
    iou_scores = intersect_areas / union_areas

    # Best IOUs for each location.
    best_ious = K.max(iou_scores, axis=4)  # Best IOU scores.
    best_ious = K.expand_dims(best_ious)

    # A detector has found an object if IOU > thresh for some true box.
    object_detections = K.cast(best_ious > 0.6, K.dtype(best_ious))





    # NOTE: YOLO does not use binary cross-entropy here.
    no_object_weights = (no_object_scale * (1 - object_detections) *
                         (1 - detectors_mask))
    no_objects_loss = no_object_weights * K.square(-pred_confidence)

    if rescore_confidence:
        objects_loss = (object_scale * detectors_mask *
                        K.square(best_ious - pred_confidence))
    else:
        objects_loss = (object_scale * detectors_mask *
                        K.square(1 - pred_confidence))
    confidence_loss = objects_loss + no_objects_loss

    # Classification loss for matching detections.
    # NOTE: YOLO does not use categorical cross-entropy loss here.
    matching_classes = K.cast(matching_true_boxes[..., 4], 'int32')
    matching_classes = K.one_hot(matching_classes, num_classes)
    classification_loss = (class_scale * detectors_mask *
                           K.square(matching_classes - pred_class_prob))

    # Coordinate loss for matching detection boxes.
    matching_boxes = matching_true_boxes[..., 0:4]
    coordinates_loss = (coordinates_scale * detectors_mask *
                        K.square(matching_boxes - pred_boxes))

    confidence_loss_sum = K.sum(confidence_loss)
    classification_loss_sum = K.sum(classification_loss)
    coordinates_loss_sum = K.sum(coordinates_loss)
    total_loss = 0.5 * (
        confidence_loss_sum + classification_loss_sum + coordinates_loss_sum)
    if print_loss:
        total_loss = tf.Print(
            total_loss, [
                total_loss, confidence_loss_sum, classification_loss_sum,
                coordinates_loss_sum
            ],
            message='yolo_loss, conf_loss, class_loss, box_coord_loss:')

    return total_loss


def preprocess_true_boxes(true_boxes, anchors, image_size):
    """Find detector in YOLO where ground truth box should appear.

    Parameters
    ----------
    true_boxes : array
        List of ground truth boxes in form of relative x, y, w, h, class.
        Relative coordinates are in the range [0, 1] indicating a percentage
        of the original image dimensions.
    anchors : array
        List of anchors in form of w, h.
        Anchors are assumed to be in the range [0, conv_size] where conv_size
        is the spatial dimension of the final convolutional features.
    image_size : array-like
        List of image dimensions in form of h, w in pixels.

    Returns
    -------
    detectors_mask : array
        0/1 mask for detectors in [conv_height, conv_width, num_anchors, 1]
        that should be compared with a matching ground truth box.
    matching_true_boxes: array
        Same shape as detectors_mask with the corresponding ground truth box
        adjusted for comparison with predicted parameters at training time.
    """
    height, width = image_size
    num_anchors = len(anchors)
    # Downsampling factor of 5x 2-stride max_pools == 32.
    # TODO: Remove hardcoding of downscaling calculations.
    assert height % 32 == 0, 'Image sizes in YOLO_v2 must be multiples of 32.'
    assert width % 32 == 0, 'Image sizes in YOLO_v2 must be multiples of 32.'
    conv_height = height // 32
    conv_width = width // 32
    num_box_params = true_boxes.shape[1]
    detectors_mask = np.zeros(
        (conv_height, conv_width, num_anchors, 1), dtype=np.float32)
    matching_true_boxes = np.zeros(
        (conv_height, conv_width, num_anchors, num_box_params),
        dtype=np.float32)

    for box in true_boxes:
        # scale box to convolutional feature spatial dimensions
        box_class = box[4:5]
        box = box[0:4] * np.array(
            [conv_width, conv_height, conv_width, conv_height])
        i = np.floor(box[1]).astype('int')
        j = min(np.floor(box[0]).astype('int'),1)
        best_iou = 0
        best_anchor = 0

        for k, anchor in enumerate(anchors):
            # Find IOU between box shifted to origin and anchor box.
            box_maxes = box[2:4] / 2.
            box_mins = -box_maxes
            anchor_maxes = (anchor / 2.)
            anchor_mins = -anchor_maxes

            intersect_mins = np.maximum(box_mins, anchor_mins)
            intersect_maxes = np.minimum(box_maxes, anchor_maxes)
            intersect_wh = np.maximum(intersect_maxes - intersect_mins, 0.)
            intersect_area = intersect_wh[0] * intersect_wh[1]
            box_area = box[2] * box[3]
            anchor_area = anchor[0] * anchor[1]
            iou = intersect_area / (box_area + anchor_area - intersect_area)
            if iou > best_iou:
                best_iou = iou
                best_anchor = k

        if best_iou > 0:
            detectors_mask[i, j, best_anchor] = 1
            adjusted_box = np.array(
                [
                    box[0] - j, box[1] - i,
                    np.log(box[2] / anchors[best_anchor][0]),
                    np.log(box[3] / anchors[best_anchor][1]), box_class
                ],
                dtype=np.float32)
            matching_true_boxes[i, j, best_anchor] = adjusted_box
    return detectors_mask, matching_true_boxes


def yolo_body(inputs, num_anchors, num_classes):
    """Create YOLO_V2 model CNN body in Keras."""
    darknet = Model(inputs, darknet_body()(inputs))
    conv20 = compose(
        DarknetConv2D_BN_Leaky(1024, (3, 3)),
        DarknetConv2D_BN_Leaky(1024, (3, 3)))(darknet.output)

    conv13 = darknet.layers[43].output
    conv21 = DarknetConv2D_BN_Leaky(64, (1, 1))(conv13)
    # TODO: Allow Keras Lambda to use func arguments for output_shape?
    conv21_reshaped = Lambda(
        space_to_depth_x2,
        output_shape=space_to_depth_x2_output_shape,
        name='space_to_depth')(conv21)

    x = concatenate([conv21_reshaped, conv20])
    x = DarknetConv2D_BN_Leaky(1024, (3, 3))(x)
    x = DarknetConv2D(num_anchors * (num_classes + 5), (1, 1))(x)
    return Model(inputs, x)
#### / UTILS #####




def yolo_filter_boxes(box_confidence, boxes, box_class_probs, threshold = .6):
    """Filters YOLO boxes by thresholding on object and class confidence.

    Arguments:
    box_confidence -- tensor of shape (19, 19, 5, 1)
    boxes -- tensor of shape (19, 19, 5, 4)
    box_class_probs -- tensor of shape (19, 19, 5, 80)
    threshold -- real value, if [ highest class probability score < threshold], then get rid of the corresponding box

    Returns:
    scores -- tensor of shape (None,), containing the class probability score for selected boxes
    boxes -- tensor of shape (None, 4), containing (b_x, b_y, b_h, b_w) coordinates of selected boxes
    classes -- tensor of shape (None,), containing the index of the class detected by the selected boxes

    Note: "None" is here because you don't know the exact number of selected boxes, as it depends on the threshold.
    For example, the actual output size of scores would be (10,) if there are 10 boxes.
    """

    # Compute box scores

    box_scores = box_confidence*box_class_probs
    # Find the box_classes thanks to the max box_scores
    box_classes = K.argmax(box_scores,axis=-1)
    box_class_scores = K.max(box_scores,axis=-1)


    # Create a filtering mask based on "box_class_scores" by using "threshold".

    filtering_mask = (box_class_scores >= threshold)


    # Apply the mask to scores, boxes and classes

    scores = tf.boolean_mask(box_class_scores,filtering_mask,name='score_filtering')
    boxes = tf.boolean_mask(boxes,filtering_mask,name='box_filtering')
    classes = tf.boolean_mask(box_classes,filtering_mask,name='class_filtering')


    return scores, boxes, classes


def yolo_non_max_suppression(scores, boxes, classes, max_boxes = 10, iou_threshold = 0.5):
    """
    Applies Non-max suppression (NMS) to set of boxes

    Arguments:
    scores -- tensor of shape (None,), output of yolo_filter_boxes()
    boxes -- tensor of shape (None, 4), output of yolo_filter_boxes() that have been scaled to (608,608)
    classes -- tensor of shape (None,), output of yolo_filter_boxes()
    max_boxes -- integer, maximum number of predicted boxes you'd like
    iou_threshold -- real value, "intersection over union" threshold used for NMS filtering

    Returns:
    scores -- tensor of shape (, None), predicted score for each box
    boxes -- tensor of shape (4, None), predicted box coordinates
    classes -- tensor of shape (, None), predicted class for each box

    """

    max_boxes_tensor = K.variable(max_boxes, dtype='int32')     # tensor to be used in tf.image.non_max_suppression()
    K.get_session().run(tf.variables_initializer([max_boxes_tensor])) # initialize variable max_boxes_tensor


    nms_indices = tf.image.non_max_suppression(boxes,scores,max_boxes,iou_threshold)



    scores = K.gather(scores,nms_indices)
    boxes = K.gather(boxes,nms_indices)
    classes = K.gather(classes,nms_indices)

    return scores, boxes, classes

def yolo_eval(yolo_outputs, image_shape = (720., 1280.), max_boxes=10, score_threshold=.6, iou_threshold=.5):
    """
    Converts the output of YOLO encoding (a lot of boxes) to predicted boxes along with their scores, box coordinates and classes.

    Arguments:
    yolo_outputs -- output of the encoding model (for image_shape of (608, 608, 3)), contains 4 tensors:
                    box_confidence: tensor of shape (None, 19, 19, 5, 1)
                    box_xy: tensor of shape (None, 19, 19, 5, 2)
                    box_wh: tensor of shape (None, 19, 19, 5, 2)
                    box_class_probs: tensor of shape (None, 19, 19, 5, 80)
    image_shape -- tensor of shape (2,) containing the input shape, (608,608)
    max_boxes -- integer, maximum number of predicted boxes
    score_threshold -- real value, if [ highest class probability score < threshold], then get rid of the corresponding box


    Returns:
    scores -- tensor of shape (None, ), predicted score for each box
    boxes -- tensor of shape (None, 4), predicted box coordinates
    classes -- tensor of shape (None,), predicted class for each box
    """


    box_confidence, box_xy, box_wh, box_class_probs = yolo_outputs

    boxes = yolo_boxes_to_corners(box_xy, box_wh)


    scores, boxes, classes = yolo_filter_boxes(box_confidence, boxes, box_class_probs, threshold = .6)


    boxes = scale_boxes(boxes, image_shape)


    scores, boxes, classes = yolo_non_max_suppression(scores, boxes, classes, max_boxes = 10, iou_threshold = 0.5)


    return scores, boxes, classes


def predict(sess, image_file):
    """
    Runs the graph stored in "sess" to predict boxes for "image_file". Prints and plots the preditions.

    Arguments:
    sess -- tensorflow/Keras session containing the YOLO graph
    image_file -- name of an image stored in the "images" folder.

    Returns:
    out_scores -- tensor of shape (None, ), scores of the predicted boxes
    out_boxes -- tensor of shape (None, 4), coordinates of the predicted boxes
    out_classes -- tensor of shape (None, ), class index of the predicted boxes

    Note: "None" actually represents the number of predicted boxes, it varies between 0 and max_boxes.
    """

    # Preprocess  image
    image, image_data = preprocess_image("images/" + image_file, model_image_size = (608, 608))


    out_scores, out_boxes, out_classes = sess.run([scores, boxes, classes], feed_dict={yolo_model.input: image_data, K.learning_phase(): 0})


    # Print predictions info
    print('Found {} boxes for {}'.format(len(out_boxes), image_file))
    # Generate colors for drawing bounding boxes.
    colors = generate_colors(class_names)
    # Draw bounding boxes on the image file
    draw_boxes(image, out_scores, out_boxes, out_classes, class_names, colors)
    # Save the predicted bounding box on the image
    image.save(os.path.join("out", image_file), quality=90)
    output_image = scipy.misc.imread(os.path.join("out", image_file))
    imshow(output_image)

    return out_scores, out_boxes, out_classes

def get_name_string(n,length=5):

    """
    Converts a number into 0000n format for reading in frames
    """

    c = "00000"

    return c[:length-len(str(n))] + str(n)


#### Start Session #####

sess = K.get_session()
class_names = read_classes("coco_classes.txt")
anchors = read_anchors("anchors/anchors.txt")
image_shape = (720., 1280.)

yolo_model = load_model("model/yolo_custom.h5")

yolo_outputs = yolo_head(yolo_model.output, anchors, len(class_names))
scores, boxes, classes = yolo_eval(yolo_outputs, image_shape)


# Change th
images_path = "scene1/"
#out_scores, out_boxes, out_classes = predict(sess, "image1.jpg")

for i in range(318):
    file_name = images_path + "obj_det" + get_name_string(19+6*i) + ".jpeg"
    print("Currently predicting image no. " + str(19+6*i))
    predict(sess,file_name)