def __init__(self,
paths,
score_threshold=0.3,
iou_threshold=0.6,
max_detections=15):
# Load classes and anchors.
json_path, anchors_path, classes_path, weights_path = paths
with open(classes_path) as f:
self.class_names = f.readlines()
self.class_names = [c.strip() for c in self.class_names]
with open(anchors_path) as f:
self.anchors = f.readline()
self.anchors = [float(x) for x in self.anchors.split(',')]
self.anchors = np.array(self.anchors).reshape(-1, 2)
self.image_input = Input(shape=(None, None, 3))
# yolo_json_file = open(json_path, 'r') # no longer loading model directly, using create_model
# yolo_json = yolo_json_file.read()
# yolo_json_file.close()
self.yolo_model, garbage = create_model(self.anchors, self.class_names,
False, 0, json_path)
self.yolo_model.load_weights(weights_path)
self.yolo_model.summary()
self.max_boxes = max_detections,
self.score_threshold = score_threshold,
self.iou_threshold = iou_threshold
print('yolo object created')
def __init__(self, viden_yolo_weights_path, viden_crnn_weights_path,
classes_path, out_path):
self.act_model = create_crnn_model(train=False)
self.act_model.load_weights(
viden_crnn_weights_path
) # 'viden_trained_models\\viden_crnn_14May2021.hdf5')
class_names = get_classes(classes_path)
#print(class_names)
self.out_path = out_path
#self.anchors = YOLO_ANCHORS
self.yolo_model_body, self.yolo_model = create_model(
YOLO_ANCHORS,
class_names,
load_pretrained=False,
freeze_body=False)
self.yolo_model_body.load_weights(
viden_yolo_weights_path
) #'viden_trained_models\\viden_yolo_14May2021.h5')
self.yolo_outputs = yolo_head(self.yolo_model_body.output,
YOLO_ANCHORS, len(class_names))
self.yolo_input_image_shape = K.placeholder(shape=(2, ))
self.boxes, self.scores, self.classes = yolo_eval(
self.yolo_outputs,
self.yolo_input_image_shape,
max_boxes=1,
score_threshold=.7,
iou_threshold=0.5)
def _main(args):
# Current directory
img_path = args.data_path
classes_path = args.classes_path
print("processing path:" + img_path)
class_names = get_classes(classes_path)
anchors = YOLO_ANCHORS #read_anchors("model_data/yolo_anchors.txt")
model_body, model = create_model(anchors, class_names)
predict_draw(model_body,
class_names,
anchors,
img_path,
weights_name='trained_stage_3_best.h5',
out_path="output_images",
save_all=False)
def _main(args):
def predict(sess, image):
# Preprocess your image
image, image_data = preprocess_image(image,
model_image_size=(416, 416))
# Run the session with the correct tensors and choose the correct placeholders in the feed_dict.
# You'll need to use feed_dict={yolo_model.input: ... , K.learning_phase(): 0})
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'.format(len(out_boxes)))
# Generate colors for drawing bounding boxes.
colors = generate_colors(class_names)
# Draw bounding boxes on the image file
out_image = draw_boxes(image, out_scores, out_boxes, out_classes,
class_names, colors)
return out_image, out_scores, out_boxes, out_classes
video_path = os.path.join(INPUT_PATH, args.input)
input = cv2.VideoCapture(video_path)
if (input.isOpened() == False):
print("Error opening video file.")
return
height = input.get(cv2.CAP_PROP_FRAME_HEIGHT)
width = input.get(cv2.CAP_PROP_FRAME_WIDTH)
fps = input.get(cv2.CAP_PROP_FPS)
if not os.path.exists(OUTPUT_PATH):
os.mkdir(OUTPUT_PATH)
output_file_path = os.path.join(OUTPUT_PATH, args.input)
output = cv2.VideoWriter(output_file_path, cv2.VideoWriter_fourcc(*'MP4V'),
fps, (int(width), int(height)))
image_shape = (height, width)
sess = K.get_session()
class_names = read_classes(CLASS_LIST_PATH)
anchors = read_anchors(YOLO_ANCHORS_PATH)
yolo_model, model = create_model(anchors, class_names)
yolo_model.load_weights(args.weights)
yolo_outputs = yolo_head(yolo_model.output, anchors, len(class_names))
boxes, scores, classes = yolo_eval(yolo_outputs,
image_shape,
score_threshold=args.score_threshold,
iou_threshold=args.iou_threshold)
n_frame = 0
while input.isOpened():
ret, original_frame = input.read()
if ret:
n_frame = n_frame + 1
print("processing frame {} ...".format(n_frame))
process_frame = cv2.cvtColor(original_frame, cv2.COLOR_BGR2RGB)
process_frame = Image.fromarray(process_frame)
process_frame, out_scores, out_boxes, out_classes = predict(
sess, process_frame)
process_frame = cv2.cvtColor(process_frame, cv2.COLOR_BGR2RGB)
output.write(process_frame)
else:
break
print("Finished. Output file: ", output_file_path)
input.release()
output.release()
cv2.destroyAllWindows()
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)
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. ' \
'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]
def _main(args):
def predict(sess, image):
# Preprocess your image
image, image_data = preprocess_image(image,
model_image_size=(416, 416))
# Run the session with the correct tensors and choose the correct placeholders in the feed_dict.
# You'll need to use feed_dict={yolo_model.input: ... , K.learning_phase(): 0})
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'.format(len(out_boxes)))
# Generate colors for drawing bounding boxes.
colors = generate_colors(class_names)
# Draw bounding boxes on the image file
out_image = draw_boxes(image, out_scores, out_boxes, out_classes,
class_names, colors)
return out_image, out_scores, out_boxes, out_classes
image_path = os.path.join(INPUT_PATH, args.input)
input = cv2.imread(image_path)
if input is None:
print("Error opening image file")
return
height = float(input.shape[0])
width = float(input.shape[1])
image_shape = (height, width)
sess = K.get_session()
class_names = read_classes(CLASS_LIST_PATH)
anchors = read_anchors(YOLO_ANCHORS_PATH)
yolo_model, model = create_model(anchors, class_names)
yolo_model.load_weights(args.weights)
yolo_outputs = yolo_head(yolo_model.output, anchors, len(class_names))
boxes, scores, classes = yolo_eval(yolo_outputs,
image_shape,
score_threshold=args.score_threshold,
iou_threshold=args.iou_threshold)
process_img = cv2.cvtColor(input, cv2.COLOR_BGR2RGB)
process_img = Image.fromarray(process_img)
process_img, out_scores, out_boxes, out_classes = predict(
sess, process_img)
output = cv2.cvtColor(process_img, cv2.COLOR_BGR2RGB)
cv2.imshow('prediction', output)
cv2.waitKey(0)
output_file_path = os.path.join(OUTPUT_PATH, args.input)
if not os.path.exists(OUTPUT_PATH):
os.mkdir(OUTPUT_PATH)
cv2.imwrite(output_file_path, output)
print("Saved: ", output_file_path)
def _main(args):
input_path = args.input_path
out_path = args.out_path
classes_path = args.classes_path
outF = open(out_path + "\prediction.txt", "w")
# model to be used at test time
act_model = create_crnn_model(train=False)
# load the saved best model weights
act_model.load_weights('viden_trained_models\\viden_crnn_14May2021.hdf5')
class_names = get_classes(classes_path)
anchors = YOLO_ANCHORS
yolo_model_body, yolo_model = create_model(anchors, class_names)
yolo_model_body.load_weights(
'viden_trained_models\\viden_yolo_14May2021.h5')
yolo_outputs = yolo_head(yolo_model_body.output, anchors, len(class_names))
yolo_input_image_shape = K.placeholder(shape=(2, ))
boxes, scores, classes = yolo_eval(yolo_outputs,
yolo_input_image_shape,
max_boxes=6,
score_threshold=.7,
iou_threshold=0.5)
config = ('-l eng --oem 1 --psm 3')
time_lst = []
orig_img_name_lst = []
for img_path in glob.iglob(input_path + "*.jpg"):
orig_img_name = ''
print("Evaluating :" + img_path)
start_time = time.time()
pred_boxes, pred_box_classes = get_bounding_boxes(
yolo_model_body,
boxes,
scores,
classes,
yolo_input_image_shape,
img_path,
class_names,
out_path,
save=True)
outF.write("File Name:" + img_path)
outF.write("\n")
for i, box in list(enumerate(pred_boxes)):
box_class = class_names[pred_box_classes[i]]
# box = pred_boxes[i]
top, left, bottom, right = box
image = PIL.Image.open(img_path)
pred_obj_img = image.crop((left, top, right, bottom))
txt = get_text(act_model, pred_obj_img, box_class)
outF.write(box_class + ":" + txt)
outF.write("\n")
print(txt)
_, tail = ntpath.split(img_path)
orig_img_name = os.path.splitext(tail)[0]
pred_obj_img.save(out_path + "\\" + txt + "_" + str(i) + "_" +
box_class + ".jpg")
end_time = time.time()
time_lst.append(end_time - start_time)
orig_img_name_lst.append(orig_img_name)
outF.write("\n")
outF.close()
plot_time(out_path, orig_img_name_lst, time_lst)
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)
label_file = args.label_file
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]
# TODO: USE THIS INSTEAD
# 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)
# ALEXANDER HACK
anchors = np.array(
((0.57273, 0.677385), (1.87446, 2.06253), (3.33843, 5.47434),
(7.88282, 3.52778), (9.77052, 9.16828)))
yolo_model, model = create_model(anchors,
class_names,
load_pretrained=False,
freeze_body=True)
model.load_weights(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)
lf = open(label_file)
num_tests = 10
count = 0
while (count < num_tests):
count += 1
line = lf.readline()
line = line.split(' ')
image_file = os.path.join(utils.home(), 'data', line[0])
image = Image.open(image_file)
image_file = line[0]
pts = line[1:]
pts = np.asarray(pts, dtype=np.float)
image = np.array(image)
image = utils.imwarp(image, pts, sz=(416, 416))
image_data = image
image_data = np.expand_dims(image_data, 0)
image = Image.fromarray(image)
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='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()
def _main(args):
def predict(sess, image):
# Preprocess your image
image, image_data = preprocess_image(image,
model_image_size=(416, 416))
# Run the session with the correct tensors and choose the correct placeholders in the feed_dict.
# You'll need to use feed_dict={yolo_model.input: ... , K.learning_phase(): 0})
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'.format(len(out_boxes)))
# Generate colors for drawing bounding boxes.
colors = generate_colors(class_names)
# Draw bounding boxes on the image file
out_image = draw_boxes(image, out_scores, out_boxes, out_classes,
class_names, colors)
return out_image, out_scores, out_boxes, out_classes
cap = cv2.VideoCapture(args.input)
if (cap.isOpened() == False):
print("Error opening video capture device.")
return
height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
image_shape = (height, width)
sess = K.get_session()
class_names = read_classes(CLASS_LIST_PATH)
anchors = read_anchors(YOLO_ANCHORS_PATH)
yolo_model, model = create_model(anchors, class_names)
yolo_model.load_weights(args.weights)
yolo_outputs = yolo_head(yolo_model.output, anchors, len(class_names))
boxes, scores, classes = yolo_eval(yolo_outputs,
image_shape,
score_threshold=args.score_threshold,
iou_threshold=args.iou_threshold)
while True:
ret, original_frame = cap.read()
cv2.imshow('original', original_frame)
process_frame = cv2.cvtColor(original_frame, cv2.COLOR_BGR2RGB)
process_frame = Image.fromarray(process_frame)
process_frame, out_scores, out_boxes, out_classes = predict(
sess, process_frame)
process_frame = cv2.cvtColor(process_frame, cv2.COLOR_BGR2RGB)
cv2.imshow('output', process_frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
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
