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YOLO.py
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YOLO.py
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import os
import matplotlib.pyplot as plt
import scipy.io
import scipy.misc
import tensorflow as tf
from keras import backend as K
from keras.models import load_model, Model
from yolo_utils import read_classes, read_anchors, generate_colors, preprocess_image, draw_boxes, scale_boxes
from yad2k.models.keras_yolo import yolo_head, yolo_boxes_to_corners, preprocess_true_boxes, yolo_loss, yolo_body
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
"""
box_scores = box_confidence * box_class_probs # 19*19*5*80
box_classes = K.argmax(box_scores, axis=-1) # 19*19*5
box_class_scores = K.max(box_scores, axis=-1, keepdims=False) # 19*19*5
filtering_mask = box_class_scores >= threshold # 19*19*5
scores = tf.boolean_mask(box_class_scores, filtering_mask) # (? ,)
boxes = tf.boolean_mask(boxes, filtering_mask) # (? , 4)
classes = tf.boolean_mask(box_classes, filtering_mask) # (? ,)
return scores, boxes, classes
def yolo_non_max_suppression(scores, boxes, classes, max_boxes=10, iou_threshold=0.5):
"""
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 the image size
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 your 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, in this notebook we use (608., 608.) (has to be float32 dtype)
max_boxes -- integer, maximum number of predicted boxes you'd like
score_threshold -- real value, if [ highest class probability score < threshold], then get rid of the corresponding box
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 (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=score_threshold)
boxes = scale_boxes(boxes, image_shape)
scores, boxes, classes = yolo_non_max_suppression(scores, boxes, classes, max_boxes, iou_threshold)
return scores, boxes, classes
sess = K.get_session()
class_names = read_classes("model_data/coco_classes.txt")
anchors = read_anchors("model_data/yolo_anchors.txt")
image_shape = (720., 1280.)
yolo_model = load_model("model_data/yolo.h5")
yolo_model.summary()
yolo_outputs = yolo_head(yolo_model.output, anchors, len(class_names))
scores, boxes, classes = yolo_eval(yolo_outputs, image_shape)
def predict(sess, image_file,specificPath=False):
"""
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
"""
if specificPath:
image, image_data = preprocess_image( image_file, model_image_size=(608, 608))
else:
image, image_data = preprocess_image("temp/" +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})
output = ('Found {} Objects in {}'.format(len(out_boxes), image_file))
colors = generate_colors(class_names)
draw_boxes(image, out_scores, out_boxes, out_classes, class_names, colors)
image.save(os.path.join("out", image_file), quality=90)
output_image = scipy.misc.imread(os.path.join("out", image_file))
##plt.imshow(output_image)
##plt.show()
#return out_scores, out_boxes, out_classes , os.path.join("out", image_file)
return output, out_boxes, out_classes , os.path.join("out", image_file)