def _detect(setting: Dict[Any, Any], image) -> List[Dict[Any, Any]]:
start = datetime.datetime.now()
# Generate output tensor targets for filtered bounding boxes.
# TODO: Wrap these backend operations with Keras layers.
yolo_outputs = yolo_head(setting['yolo_model'].output, setting['anchors'],
len(setting['class_names']))
input_image_shape = kback.placeholder(shape=(2, ))
boxes, scores, classes = yolo_eval(yolo_outputs,
input_image_shape,
score_threshold=args.score_threshold,
iou_threshold=args.iou_threshold)
print('spent time(yolo_eval)', datetime.datetime.now() - start)
if setting[
'is_fixed_size']: # TODO: When resizing we can use minibatch input.
resized_image = image.resize(
tuple(reversed(setting['model_image_size'])), Image.BICUBIC)
image_data = np.array(resized_image, dtype='float32')
else:
# Due to skip connection + max pooling in YOLO_v2, inputs must have
# width and height as multiples of 32.
new_image_size = (image.width - (image.width % 32),
image.height - (image.height % 32))
resized_image = image.resize(new_image_size, Image.BICUBIC)
image_data = np.array(resized_image, dtype='float32')
print(image_data.shape)
image_data /= 255.
image_data = np.expand_dims(image_data, 0) # Add batch dimension.
print('spent time(before run)', datetime.datetime.now() - start)
out_boxes, out_scores, out_classes = setting['sess'].run(
[boxes, scores, classes],
feed_dict={
setting['yolo_model'].input: image_data,
input_image_shape: [image.size[1], image.size[0]],
kback.learning_phase(): 0
})
print('spent time(after run)', datetime.datetime.now() - start)
detected_items = []
for i, c in reversed(list(enumerate(out_classes))):
predicted_class = setting['class_names'][c]
box = out_boxes[i]
score = out_scores[i]
detected_items.append({
'class': predicted_class,
'box': box.tolist(),
'score': score.tolist(),
})
print('detected_items', detected_items)
print('spent time(_detect)', datetime.datetime.now() - start)
return detected_items
def YOLO_predict(img, batch_img, sess, model, anchors, n_classes):
yolo_outputs = yolo_head(model.output, anchors, n_classes)
input_image_shape = K.placeholder(shape=(2, ))
boxes, scores, classes = yolo_eval(yolo_outputs, input_image_shape)
out_boxes, out_scores, out_classes = sess.run(
[boxes, scores, classes],
feed_dict={
model.input: batch_img,
input_image_shape: [img.shape[0], img.shape[1]],
K.learning_phase(): 0
})
return out_boxes, out_scores, out_classes
def draw(model_body, class_names, anchors, image_data, image_set='val',
weights_name='trained_stage_3_best.h5', out_path="output_images", save_all=True):
if image_set == 'train':
image_data = np.array([np.expand_dims(image, axis=0)
for image in image_data[:int(len(image_data)*.9)]])
elif image_set == 'val':
image_data = np.array([np.expand_dims(image, axis=0)
for image in image_data[int(len(image_data)*.9):]])
elif image_set == 'all':
image_data = np.array([np.expand_dims(image, axis=0)
for image in image_data])
else:
ValueError("draw argument image_set must be 'train', 'val', or 'all'")
# model.load_weights(weights_name)
print(image_data.shape)
model_body.load_weights(weights_name)
# Create output variables for prediction.
yolo_outputs = yolo_head(model_body.output, anchors, len(class_names))
input_image_shape = K.placeholder(shape=(2, ))
boxes, scores, classes = yolo_eval(
yolo_outputs, input_image_shape, score_threshold=0.07, iou_threshold=0.0)
# Run prediction on overfit image.
sess = K.get_session() # TODO: Remove dependence on Tensorflow session.
if not os.path.exists(out_path):
os.makedirs(out_path)
for i in range(len(image_data)):
out_boxes, out_scores, out_classes = sess.run(
[boxes, scores, classes],
feed_dict={
model_body.input: image_data[i],
input_image_shape: [image_data.shape[2], image_data.shape[3]],
K.learning_phase(): 0
})
print('Found {} boxes for image.'.format(len(out_boxes)))
print("Boxes:",out_boxes)
# Plot image with predicted boxes.
image_with_boxes = draw_boxes_(image_data[i][0], out_boxes, out_classes,
class_names, out_scores)
print("--------------------------------------------")
# Save the image:
if save_all or (len(out_boxes) > 0):
image = PIL.Image.fromarray(image_with_boxes)
image.save(os.path.join(out_path,str(i)+'.png'))
print("classes[2] = " + str(classes[2].eval()))
print("scores.shape = " + str(scores.eval().shape))
print("boxes.shape = " + str(boxes.eval().shape))
print("classes.shape = " + str(classes.eval().shape))
sess = K.get_session()
class_names = read_classes("datasets/coco_classes.txt")
anchors = read_anchors("datasets/yolo_anchors.txt")
image_shape = (720., 1280.)
yolo_model = load_model("datasets/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):
"""
Runs the graph stored in "sess" to predict boxes for "image_file". Prints and plots the preditions.
Arguments:
sess -- your 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
def _main(args,
frames_paths,
crops_bboxes,
crop_value,
resize_frames=None,
verbose=1,
person_only=True,
allowed_number_of_boxes=100):
'''
:param args: yolo model args like in YAD2K
:param frames_paths: list of paths to frame images
:param crops_bboxes: list of lists - crops per frames
:param verbose:
:param person_only:
:return:
'''
print("frames_paths", len(frames_paths), frames_paths)
print("crops_bboxes", len(crops_bboxes), crops_bboxes)
model_path = os.path.expanduser(args["model_path"])
print(model_path)
assert model_path.endswith('.h5'), 'Keras model must be a .h5 file.'
anchors_path = os.path.expanduser(args["anchors_path"])
classes_path = os.path.expanduser(args["classes_path"])
sess = K.get_session() # TODO: Remove dependence on Tensorflow session.
with open(classes_path) as f:
class_names = f.readlines()
class_names = [c.strip() for c in class_names]
with open(anchors_path) as f:
anchors = f.readline()
anchors = [float(x) for x in anchors.split(',')]
anchors = np.array(anchors).reshape(-1, 2)
yolo_model = load_model(model_path)
# Verify model, anchors, and classes are compatible
num_classes = len(class_names)
num_anchors = len(anchors)
# TODO: Assumes dim ordering is channel last
# model_output_channels = yolo_model.layers[-1].output_shape[-1]
# assert model_output_channels == num_anchors * (num_classes + 5), \
# 'Mismatch between model and given anchor and class sizes. ' \
# 'Specify matching anchors and classes with --anchors_path and ' \
# '--classes_path flags.'
if verbose > 0:
print('{} model, anchors, and classes loaded.'.format(model_path))
# Check if model is fully convolutional, assuming channel last order.
model_image_size = yolo_model.layers[0].input_shape[1:3]
is_fixed_size = model_image_size != (None, None)
# Generate colors for drawing bounding boxes.
hsv_tuples = [(x / len(class_names), 1., 1.)
for x in range(len(class_names))]
colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
colors = list(
map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),
colors))
random.seed(10101) # Fixed seed for consistent colors across runs.
random.shuffle(colors) # Shuffle colors to decorrelate adjacent classes.
random.seed(None) # Reset seed to default.
# Generate output tensor targets for filtered bounding boxes.
# TODO: Wrap these backend operations with Keras layers.
### old
# yolo_outputs = yolo_head(yolo_model.output, anchors, len(class_names))###差异
# input_image_shape = K.placeholder(shape=(2, ))
###
if ("v2" in model_path):
from YAD2K.yad2k.models.keras_yolo import yolo_eval, yolo_head ###两个yoloV2
yolo_outputs = yolo_head(yolo_model.output, anchors, len(class_names))
elif ("v3" in model_path):
from kyolov3.ky.yolo3.model import yolo_head, yolo_eval
yolo_outputs = yolo_model.output
else:
from kyolov3.ky.yolo3.model import yolo_head, yolo_eval
yolo_outputs = yolo_model.output
### 看看会不会错 yolo_model.output == feat == yolo_outputs[l]
input_image_shape = K.placeholder(shape=(2, ))
####### EVALUATION
boxes, scores, classes = yolo_eval(yolo_outputs,
anchors,
num_classes,
input_image_shape,
score_threshold=args["score_threshold"],
iou_threshold=args["iou_threshold"],
max_boxes=allowed_number_of_boxes)
pureEval_times = []
ioPlusEval_times = []
bboxes = []
images_processed = 0
evaluation_time = 0
### PROCESS FILES
for frame_i in range(0, len(frames_paths)):
start_loop = timer()
images_processed += 1
frame_path = frames_paths[frame_i]
frame = Image.open(frame_path)
if resize_frames is not None:
resize_to = resize_frames[frame_i]
ow, oh = frame.size
nw = ow * resize_to
nh = oh * resize_to
frame = frame.resize((int(nw), int(nh)), Image.ANTIALIAS)
crops_in_frame = crops_bboxes[frame_i]
#print("Frame", frame_i, " with ", len(crops_in_frame), " crops.")
sys.stdout.write("\rFrame " + str(frame_i) + " with " +
str(len(crops_in_frame)) + " crops.")
sys.stdout.flush()
for crop_i in range(0, len(crops_in_frame)):
crop = crops_in_frame[crop_i]
area = crop[1]
#area = (int(w_crop[0]), int(h_crop[0]), int(w_crop[0] + scale * crop), int(h_crop[0] + scale * crop))
cropped_img = frame.crop(box=area)
cropped_img = cropped_img.resize(
(int(crop_value), int(crop_value)), resample=Image.ANTIALIAS)
cropped_img.load()
"""
print("////////////////////////////////////////////////")
print("area",area)
print("crop_value",crop_value)
print("cropped_img", cropped_img.size)
cropped_img.show()
print("////////////////////////////////////////////////")
"""
image = cropped_img
"""
image_data = np.array(image, dtype='float32')
"""
if is_fixed_size: # TODO: When resizing we can use minibatch input.
resized_image = image.resize(tuple(reversed(model_image_size)),
Image.BICUBIC)
image_data = np.array(resized_image, dtype='float32')
else:
# Due to skip connection + max pooling in YOLO_v2, inputs must have
# width and height as multiples of 32.
new_image_size = (image.width - (image.width % 32),
image.height - (image.height % 32))
resized_image = image.resize(new_image_size, Image.BICUBIC)
image_data = np.array(resized_image, dtype='float32')
print(image_data.shape)
image_data /= 255.
image_data = np.expand_dims(image_data, 0) # Add batch dimension.
if images_processed < 2:
print("# image size: ", image_data.shape, image.size)
################# START #################
start_eval = timer()
out_boxes, out_scores, out_classes = sess.run(
[boxes, scores, classes],
feed_dict={
yolo_model.input: image_data,
input_image_shape: [image.size[1], image.size[0]],
K.learning_phase(): 0
})
end_eval = timer()
################# END #################
evaluation_time = (end_eval - start_eval)
pureEval_times.append(evaluation_time)
people = 0
bboxes_image = []
#print(num_frames, num_crops)
for i, c in reversed(list(enumerate(out_classes))):
predicted_class = class_names[c]
if predicted_class == 'person':
people += 1
if person_only and (predicted_class != 'person'):
continue
box = out_boxes[i]
score = out_scores[i]
#print(predicted_class, box, score)
bboxes_image.append([predicted_class, box, score, c])
if verbose > 0:
num = len(out_boxes)
if person_only:
num = people
print('Found {} boxes in crop {} of frame {} in {}s'.format(
num, crop_i, frame_i, evaluation_time))
bboxes.append(bboxes_image)
end_loop = timer()
loop_time = (end_loop - start_loop)
ioPlusEval_times.append(loop_time - evaluation_time)
#sess.close()
return bboxes
def run_nn(hWnd):
global loaded
model_path = os.path.expanduser('YAD2K/model_data/yolo.h5')
anchors_path = os.path.expanduser('YAD2K/model_data/yolo_anchors.txt')
classes_path = os.path.expanduser('YAD2K/model_data/league_classes.txt')
sess = K.get_session() # TODO: Remove dependence on Tensorflow session.
with open(classes_path) as f:
class_names = f.readlines()
class_names = [c.strip() for c in class_names]
with open(anchors_path) as f:
anchors = f.readline()
anchors = [float(x) for x in anchors.split(',')]
anchors = np.array(anchors).reshape(-1, 2)
yolo_model, _ = create_model(anchors, class_names)
yolo_model.load_weights('trained_stage_3_best.h5')
# Verify model, anchors, and classes are compatible
num_classes = len(class_names)
num_anchors = len(anchors)
# TODO: Assumes dim ordering is channel last
model_output_channels = yolo_model.layers[-1].output_shape[-1]
assert model_output_channels == num_anchors * (
num_classes +
5), 'Mismatch between model and given anchor and class sizes. '
print('{} model, anchors, and classes loaded.'.format(model_path))
# Check if model is fully convolutional, assuming channel last order.
model_image_size = yolo_model.layers[0].input_shape[1:3]
is_fixed_size = model_image_size != (None, None)
# Generate colors for drawing bounding boxes.
hsv_tuples = [(x / len(class_names), 1., 1.)
for x in range(len(class_names))]
colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
colors = list(
map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),
colors))
random.seed(10101) # Fixed seed for consistent colors across runs.
random.shuffle(colors) # Shuffle colors to decorrelate adjacent classes.
random.seed(None) # Reset seed to default.
# Generate output tensor targets for filtered bounding boxes.
# TODO: Wrap these backend operations with Keras layers.
yolo_outputs = yolo_head(yolo_model.output, anchors, len(class_names))
input_image_shape = K.placeholder(shape=(2, ))
boxes, scores, classes = yolo_eval(yolo_outputs,
input_image_shape,
score_threshold=args.score_threshold,
iou_threshold=args.iou_threshold)
# Save the output into a compact JSON file.
outfile = open('output/game_data.json', 'w')
# This will be appended with an object for every frame.
data_to_write = []
loaded = True
win32gui.RedrawWindow(hWnd, None, None,
win32con.RDW_INVALIDATE | win32con.RDW_ERASE)
with mss.mss() as sct:
monitor = {'top': 0, 'left': 0, 'width': 1920, 'height': 1080}
while 'Screen capturing':
last_time = time.time()
# Get raw pixels from the screen, save it to a Numpy array
img = np.array(sct.grab(monitor))
img = Image.fromarray(img)
img.load()
background = Image.new("RGB", img.size, (255, 255, 255))
background.paste(img, mask=img.split()[3])
test_yolo(background, is_fixed_size, model_image_size, sess, boxes,
scores, classes, yolo_model, input_image_shape,
class_names, colors)
win32gui.RedrawWindow(hWnd, None, None,
win32con.RDW_INVALIDATE | win32con.RDW_ERASE)
# img = test_yolo(background)
# basewidth = 700
# wpercent = (basewidth/float(img.size[0]))
# hsize = int((float(img.size[1])*float(wpercent)))
# img = img.resize((basewidth,hsize), Image.ANTIALIAS)
# img = np.array(img)
# # Display the picture
# cv2.imshow('OpenCV/Numpy normal', img)
# Press "q" to quit
if cv2.waitKey(25) & 0xFF == ord(';'):
cv2.destroyAllWindows()
break
sess.close()
return
def _main(args):
voc_path = os.path.expanduser(args.data_path)
classes_path = os.path.expanduser(args.classes_path)
anchors_path = os.path.expanduser(args.anchors_path)
with open(classes_path) as f:
class_names = f.readlines()
class_names = [c.strip() for c in class_names]
if os.path.isfile(anchors_path):
with open(anchors_path) as f:
anchors = f.readline()
anchors = [float(x) for x in anchors.split(',')]
anchors = np.array(anchors).reshape(-1, 2)
else:
anchors = YOLO_ANCHORS
voc = h5py.File(voc_path, 'r')
image = PIL.Image.open(io.BytesIO(voc['train/images'][28]))
orig_size = np.array([image.width, image.height])
orig_size = np.expand_dims(orig_size, axis=0)
# Image preprocessing.
image = image.resize((416, 416), PIL.Image.BICUBIC)
image_data = np.array(image, dtype=np.float)
image_data /= 255.
# Box preprocessing.
# Original boxes stored as 1D list of class, x_min, y_min, x_max, y_max.
boxes = voc['train/boxes'][28]
boxes = boxes.reshape((-1, 5))
# Get extents as y_min, x_min, y_max, x_max, class for comparision with
# model output.
boxes_extents = boxes[:, [2, 1, 4, 3, 0]]
# Get box parameters as x_center, y_center, box_width, box_height, class.
boxes_xy = 0.5 * (boxes[:, 3:5] + boxes[:, 1:3])
boxes_wh = boxes[:, 3:5] - boxes[:, 1:3]
boxes_xy = boxes_xy / orig_size
boxes_wh = boxes_wh / orig_size
boxes = np.concatenate((boxes_xy, boxes_wh, boxes[:, 0:1]), axis=1)
# Precompute detectors_mask and matching_true_boxes for training.
# Detectors mask is 1 for each spatial position in the final conv layer and
# anchor that should be active for the given boxes and 0 otherwise.
# Matching true boxes gives the regression targets for the ground truth box
# that caused a detector to be active or 0 otherwise.
detectors_mask_shape = (13, 13, 5, 1)
matching_boxes_shape = (13, 13, 5, 5)
detectors_mask, matching_true_boxes = preprocess_true_boxes(
boxes, anchors, [416, 416])
# Create model input layers.
image_input = Input(shape=(416, 416, 3))
boxes_input = Input(shape=(None, 5))
detectors_mask_input = Input(shape=detectors_mask_shape)
matching_boxes_input = Input(shape=matching_boxes_shape)
print('Boxes:')
print(boxes)
print('Box corners:')
print(boxes_extents)
print('Active detectors:')
print(np.where(detectors_mask == 1)[:-1])
print('Matching boxes for active detectors:')
print(matching_true_boxes[np.where(detectors_mask == 1)[:-1]])
# Create model body.
model_body = yolo_body(image_input, len(anchors), len(class_names))
model_body = Model(image_input, model_body.output)
# Place model loss on CPU to reduce GPU memory usage.
with tf.device('/cpu:0'):
# TODO: Replace Lambda with custom Keras layer for loss.
model_loss = Lambda(yolo_loss,
output_shape=(1, ),
name='yolo_loss',
arguments={
'anchors': anchors,
'num_classes': len(class_names)
})([
model_body.output, boxes_input,
detectors_mask_input, matching_boxes_input
])
model = Model(
[image_input, boxes_input, detectors_mask_input, matching_boxes_input],
model_loss)
model.compile(
optimizer='adam', loss={
'yolo_loss': lambda y_true, y_pred: y_pred
}) # This is a hack to use the custom loss function in the last layer.
# Add batch dimension for training.
image_data = np.expand_dims(image_data, axis=0)
boxes = np.expand_dims(boxes, axis=0)
detectors_mask = np.expand_dims(detectors_mask, axis=0)
matching_true_boxes = np.expand_dims(matching_true_boxes, axis=0)
num_steps = 1000
# TODO: For full training, put preprocessing inside training loop.
# for i in range(num_steps):
# loss = model.train_on_batch(
# [image_data, boxes, detectors_mask, matching_true_boxes],
# np.zeros(len(image_data)))
model.fit([image_data, boxes, detectors_mask, matching_true_boxes],
np.zeros(len(image_data)),
batch_size=1,
epochs=num_steps)
model.save_weights('overfit_weights.h5')
# Create output variables for prediction.
yolo_outputs = yolo_head(model_body.output, anchors, len(class_names))
input_image_shape = K.placeholder(shape=(2, ))
boxes, scores, classes = yolo_eval(yolo_outputs,
input_image_shape,
score_threshold=.3,
iou_threshold=.9)
# Run prediction on overfit image.
sess = K.get_session() # TODO: Remove dependence on Tensorflow session.
out_boxes, out_scores, out_classes = sess.run(
[boxes, scores, classes],
feed_dict={
model_body.input: image_data,
input_image_shape: [image.size[1], image.size[0]],
K.learning_phase(): 0
})
print('Found {} boxes for image.'.format(len(out_boxes)))
print(out_boxes)
# Plot image with predicted boxes.
image_with_boxes = draw_boxes(image_data[0], out_boxes, out_classes,
class_names, out_scores)
plt.imshow(image_with_boxes, interpolation='nearest')
plt.show()
def _main(args):
model_path = os.path.expanduser(args.model_path)
assert model_path.endswith('.h5'), 'Keras model must be a .h5 file.'
anchors_path = os.path.expanduser(args.anchors_path)
classes_path = os.path.expanduser(args.classes_path)
test_path = os.path.expanduser(args.test_path)
output_path = os.path.expanduser(args.output_path)
if not os.path.exists(output_path):
print('Creating output path {}'.format(output_path))
os.mkdir(output_path)
sess = K.get_session() # TODO: Remove dependence on Tensorflow session.
with open(classes_path) as f:
class_names = f.readlines()
class_names = [c.strip() for c in class_names]
with open(anchors_path) as f:
anchors = f.readline()
anchors = [float(x) for x in anchors.split(',')]
anchors = np.array(anchors).reshape(-1, 2)
yolo_model = load_model(model_path)
# Verify model, anchors, and classes are compatible
num_classes = len(class_names)
num_anchors = len(anchors)
# TODO: Assumes dim ordering is channel last
model_output_channels = yolo_model.layers[-1].output_shape[-1]
assert model_output_channels == num_anchors * (num_classes + 5), \
'Mismatch between model and given anchor and class sizes. ' \
'Specify matching anchors and classes with --anchors_path and ' \
'--classes_path flags.'
print('{} model, anchors, and classes loaded.'.format(model_path))
# Check if model is fully convolutional, assuming channel last order.
model_image_size = yolo_model.layers[0].input_shape[1:3]
is_fixed_size = model_image_size != (None, None)
# Generate colors for drawing bounding boxes.
hsv_tuples = [(x / len(class_names), 1., 1.)
for x in range(len(class_names))]
colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
colors = list(
map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),
colors))
random.seed(10101) # Fixed seed for consistent colors across runs.
random.shuffle(colors) # Shuffle colors to decorrelate adjacent classes.
random.seed(None) # Reset seed to default.
# Generate output tensor targets for filtered bounding boxes.
# TODO: Wrap these backend operations with Keras layers.
yolo_outputs = yolo_head(yolo_model.output, anchors, len(class_names))
input_image_shape = K.placeholder(shape=(2, ))
boxes, scores, classes = yolo_eval(yolo_outputs,
input_image_shape,
score_threshold=args.score_threshold,
iou_threshold=args.iou_threshold)
for image_file in os.listdir(test_path):
try:
image_type = imghdr.what(os.path.join(test_path, image_file))
if not image_type:
continue
except IsADirectoryError:
continue
image = Image.open(os.path.join(test_path, image_file))
if is_fixed_size: # TODO: When resizing we can use minibatch input.
resized_image = image.resize(tuple(reversed(model_image_size)),
Image.BICUBIC)
image_data = np.array(resized_image, dtype='float32')
else:
# Due to skip connection + max pooling in YOLO_v2, inputs must have
# width and height as multiples of 32.
new_image_size = (image.width - (image.width % 32),
image.height - (image.height % 32))
resized_image = image.resize(new_image_size, Image.BICUBIC)
image_data = np.array(resized_image, dtype='float32')
print(image_data.shape)
image_data /= 255.
image_data = np.expand_dims(image_data, 0) # Add batch dimension.
out_boxes, out_scores, out_classes = sess.run(
[boxes, scores, classes],
feed_dict={
yolo_model.input: image_data,
input_image_shape: [image.size[1], image.size[0]],
K.learning_phase(): 0
})
print('Found {} boxes for {}'.format(len(out_boxes), image_file))
font = ImageFont.truetype(
font=
'E:\\virtualdestop\AttentionPipeline-master\\video_parser_v1\YAD2K\\font\FiraMono-Medium.otf',
size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))
thickness = (image.size[0] + image.size[1]) // 300
for i, c in reversed(list(enumerate(out_classes))):
predicted_class = class_names[c]
box = out_boxes[i]
score = out_scores[i]
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
top, left, bottom, right = box
top = max(0, np.floor(top + 0.5).astype('int32'))
left = max(0, np.floor(left + 0.5).astype('int32'))
bottom = min(image.size[1], np.floor(bottom + 0.5).astype('int32'))
right = min(image.size[0], np.floor(right + 0.5).astype('int32'))
print(label, (left, top), (right, bottom))
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
# My kingdom for a good redistributable image drawing library.
for i in range(thickness):
draw.rectangle([left + i, top + i, right - i, bottom - i],
outline=colors[c])
draw.rectangle(
[tuple(text_origin),
tuple(text_origin + label_size)],
fill=colors[c])
draw.text(text_origin, label, fill=(0, 0, 0), font=font)
del draw
image.save(os.path.join(output_path, image_file), quality=90)
sess.close()