export_basename = 'tmp/' + os.path.splitext(os.path.basename(__file__))[0]
p = player.Player(export_basename)
p.click()
for i in range(3):
p.move_down()
p.click()
for i in range(5):
p.move_down()
p.click()
p.endstop_z()
p.wait_ms(300)
p.endstop_z()
p.wait_ms(100)
p.endstop_x()
p.wait_ms(300)
p.endstop_x()
p.wait_ms(100)
p.endstop_y()
p.wait_ms(300)
p.endstop_y()
p.wait_ms(500)
p.transfer_off()
events = p.run()
timeline = decoder.decode(export_basename + '.bin')
p.close()
def main(argv):
if (len(sys.argv) != 3):
print('Usage ./makeTrainCoco.py [root_dir] [config.yml]')
sys.exit(1)
#Load data
root_dir = argv[1] + '/' #in case we forgot
print('Opening file' + root_dir + argv[2])
with open(root_dir + argv[2], 'r') as configfile:
config = yaml.safe_load(configfile)
image_dir = root_dir + config['data_dir']
train_dir = root_dir + config['data_dir']
weights_dir = root_dir + config['weights_dir']
your_weights = weights_dir + config['generic_weights']
annotations_file = train_dir + config['untrained_annotations_fname']
train_files_regex = config['generic_train_files_regex']
train_images = glob.glob(image_dir + train_files_regex)
max_l = 100
min_l = 10
im_size = 864 #size of training imageas for yolo
##################################################
#im_size=416 #size of training imageas for yolo
yolov3 = get_yolo_model(im_size, im_size, trainable=False)
yolov3.load_weights(your_weights, by_name=True)
########################################
im_num = 1
all_imgs = []
for imagename in train_images:
im = cv2.imread(imagename)
print('processing image ' + imagename + ', ' + str(im_num) + ' of ' +
str(len(train_images)) + '...')
height, width = im.shape[:2]
im_num += 1
n_count = 0
for x in np.arange(0, width - im_size, im_size):
for y in np.arange(0, height - im_size, im_size):
img_data = {
'object': []
} #dictionary? key-value pair to store image data
head, tail = os.path.split(imagename)
noext, ext = os.path.splitext(tail)
save_name = train_dir + '/TR_' + noext + '-' + str(
n_count) + '.png'
box_name = train_dir + '/bbox/' + noext + '-' + str(
n_count) + '.png'
img = im[y:y + im_size, x:x + im_size, :]
cv2.imwrite(save_name, img)
img_data['filename'] = save_name
img_data['width'] = im_size
img_data['height'] = im_size
n_count += 1
# use the yolov3 model to predict 80 classes on COCO
# preprocess the image
image_h, image_w, _ = img.shape
new_image = img[:, :, ::-1] / 255.
new_image = np.expand_dims(new_image, 0)
# run the prediction
yolos = yolov3.predict(new_image)
boxes = decode(yolos, obj_thresh=0.005, nms_thresh=0.5)
for b in boxes:
xmin = int(b[0])
xmax = int(b[2])
ymin = int(b[1])
ymax = int(b[3])
obj = {}
obj['name'] = 'aoi'
if xmin < 0: continue
if ymin < 0: continue
if xmax > im_size: continue
if ymax > im_size: continue
if (xmax - xmin) < min_l: continue
if (xmax - xmin) > max_l: continue
if (ymax - ymin) < min_l: continue
if (ymax - ymin) > max_l: continue
obj['xmin'] = xmin
obj['ymin'] = ymin
obj['xmax'] = xmax
obj['ymax'] = ymax
img_data['object'] += [obj]
cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 255, 0),
2)
cv2.imwrite(box_name, img)
all_imgs += [img_data]
#print(all_imgs)
print('Saving data to ' + annotations_file)
with open(annotations_file, 'w') as handle:
yaml.dump(all_imgs, handle)
print('Finished! :o)')
def main(argv):
if (len(sys.argv) != 3):
print('Usage ./makeTrain.py [root_dir] [config.yml]')
sys.exit(1)
#Load data
root_dir = argv[1] + '/' #in case we forgot
print('Opening file' + root_dir + argv[2])
with open(root_dir + argv[2], 'r') as configfile:
config = yaml.safe_load(configfile)
image_dir = root_dir + config['data_dir']
train_dir = root_dir + config['data_dir']
your_weights = root_dir + config['weights_dir'] + config['specific_weights']
trained_annotations_fname = train_dir + config['trained_annotations_fname']
train_files_regex = config['specific_train_files_regex']
train_images = glob.glob(train_dir + train_files_regex)
shuffle(train_images)
max_l = 100
min_l = 10
im_size = 864 #size of training imageas for yolo
##################################################
#im_size=416 #size of training imageas for yolo
yolov3 = get_yolo_model(im_size, im_size, num_class=1, trainable=False)
yolov3.load_weights(your_weights)
########################################
im_num = 1
all_imgs = []
for imagename in train_images:
img = cv2.imread(imagename)
print('processing image ' + imagename + ', ' + str(im_num) + ' of ' +
str(len(train_images)) + '...')
im_num += 1
img_data = {
'object': []
} #dictionary? key-value pair to store image data
head, tail = os.path.split(imagename)
noext, ext = os.path.splitext(tail)
box_name = train_dir + '/bbox/' + tail
img_data['filename'] = tail
img_data['width'] = im_size
img_data['height'] = im_size
# use the trained yolov3 model to predict
# preprocess the image
image_h, image_w, _ = img.shape
new_image = img[:, :, ::-1] / 255.
new_image = np.expand_dims(new_image, 0)
# run the prediction
yolos = yolov3.predict(new_image)
boxes = decode(yolos, obj_thresh=0.2, nms_thresh=0.3)
for b in boxes:
xmin = int(b[0])
xmax = int(b[2])
ymin = int(b[1])
ymax = int(b[3])
obj = {}
obj['name'] = 'aoi'
if xmin < 0: continue
if ymin < 0: continue
if xmax > im_size: continue
if ymax > im_size: continue
if (xmax - xmin) < min_l: continue
if (xmax - xmin) > max_l: continue
if (ymax - ymin) < min_l: continue
if (ymax - ymin) > max_l: continue
obj['xmin'] = xmin
obj['ymin'] = ymin
obj['xmax'] = xmax
obj['ymax'] = ymax
img_data['object'] += [obj]
cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 255, 0), 2)
cv2.imwrite(box_name, img)
all_imgs += [img_data]
#print(all_imgs)
with open(trained_annotations_fname, 'w') as handle:
yaml.dump(all_imgs, handle)
#cv2.imwrite(save_name, img)
img_data['filename'] = tail
img_data['width'] = im_size
img_data['height'] = im_size
n_count+=1
# use the yolov3 model to predict 80 classes on COCO
# preprocess the image
image_h, image_w, _ = img.shape
new_image = img[:,:,::-1]/255.
new_image = np.expand_dims(new_image, 0)
# run the prediction
yolos = yolov3.predict(new_image)
boxes = decode(yolos, obj_thresh=0.40, nms_thresh=0.3)
for b in boxes:
xmin=int(b[0])
xmax=int(b[2])
ymin=int(b[1])
ymax=int(b[3])
obj = {}
obj['name'] = 'aoi'
# xmin = point['xcoord'] - x - sz_2
# xmax = point['xcoord'] - x + sz_2
# ymin = point['ycoord'] - y - sz_2
# ymax = point['ycoord'] - y + sz_2
def main(argv):
if(len(sys.argv) != 3):
print('Usage ./postTrainTest.py [data_dir] [config.yml]')
sys.exit(1)
#Load data
data_dir = argv[1] + '/' #in case we forgot '/'
print('Opening file' + argv[2])
with open(argv[2], 'r') as configfile:
config = yaml.safe_load(configfile)
image_dir = data_dir
train_dir = data_dir
weights_dir = data_dir + config['weights_dir']
#Training type dependent
trained_weights = weights_dir + config['trained_weights']
#based on get_yolo_model defaults and previous makTrain.py files
num_class=config['specific']['num_class']
obj_thresh=config['specific']['obj_thresh']
nms_thresh=config['specific']['nms_thresh']
list_of_train_files = config['checked_annotations_fname']
annotations_file = train_dir + config['untrained_annotations_fname']
with open (annotations_file, 'r') as fp:
all_imgs = yaml.load(fp)
max_l=config['MAX_L'] #maximal object size in pixels
min_l=config['MIN_L']
im_size=config['IMAGE_H'] #size of training imageas for yolo
##################################################
print("Loading YOLO models")
yolov3 = get_yolo_model(im_size,im_size,num_class,trainable=False)
yolov3.load_weights(trained_weights,by_name=True) #TODO is by_name necessary here?
print("YOLO models loaded, my dear.")
########################################
#read in all images from checked annotations (GROUND TRUTH)
for i in range(len(all_imgs)):
basename = os.path.basename(all_imgs[i]['filename'])
#remove extension from basename:
name_seed_split = basename.split('.')[:-1]
name_seed = '.'.join(name_seed_split)
fname_gt = image_dir + "/groundtruths/" + name_seed + ".txt"
fname_pred = image_dir + "/predictions/" + name_seed + ".txt"
img_data = {'object':[]}
img_data['filename'] = basename
img_data['width'] = all_imgs[i]['width']
img_data['height'] = all_imgs[i]['height']
#Reading ground truth
boxes_gt=[]
for obj in all_imgs[i]['object']:
boxes_gt.append([obj['xmin'],obj['ymin'],obj['xmax'],obj['ymax']])
sys.stdout.write('GT objects:')
sys.stdout.write(str(len(boxes_gt)))
sys.stdout.flush()
#do box processing
img = cv2.imread(image_dir + basename)
with open(fname_gt, 'w') as file_gt: #left top righ bottom
for b in boxes_gt:
obj = {}
if ((b[2]-b[0])*(b[3]-b[1]))<10:
continue
obj['name'] = 'aoi'
obj['xmin'] = int(b[0])
obj['ymin'] = int(b[1])
obj['xmax'] = int(b[2])
obj['ymax'] = int(b[3])
img_data['object'] += [obj]
file_gt.write(obj['name'] + " " )
file_gt.write(str(obj['xmin']) + " " )
file_gt.write(str(obj['ymin']) + " " )
file_gt.write(str(obj['xmax']) + " " )
file_gt.write(str(obj['ymax']))
file_gt.write('\n')
# preprocess the image
image_h, image_w, _ = img.shape
new_image = img[:,:,::-1]/255.
new_image = np.expand_dims(new_image, 0)
# run the prediction
sys.stdout.write('Yolo predicting...')
sys.stdout.flush()
yolos = yolov3.predict(new_image)
sys.stdout.write('decoding...')
sys.stdout.flush()
boxes_predict = decode(yolos, obj_thresh, nms_thresh)
sys.stdout.write('done!#of boxes_predict:')
sys.stdout.write(str(len(boxes_predict)))
sys.stdout.write('\n')
sys.stdout.flush()
with open(fname_pred, 'w') as file_pred: #left top righ bottom
for b in boxes_predict:
xmin=int(b[0])
xmax=int(b[2])
ymin=int(b[1])
ymax=int(b[3])
confidence=float(b[4])
objpred = {}
objpred['name'] = 'aoi'
if xmin<0: continue
if ymin<0: continue
if xmax>im_size: continue
if ymax>im_size: continue
if (xmax-xmin)<min_l: continue
if (xmax-xmin)>max_l: continue
if (ymax-ymin)<min_l: continue
if (ymax-ymin)>max_l: continue
objpred['xmin'] = xmin
objpred['ymin'] = ymin
objpred['xmax'] = xmax
objpred['ymax'] = ymax
objpred['confidence'] = confidence
file_pred.write(objpred['name'] + " " )
file_pred.write(str(objpred['confidence']) + " " )
file_pred.write(str(objpred['xmin']) + " " )
file_pred.write(str(objpred['ymin']) + " " )
file_pred.write(str(objpred['xmax']) + " " )
file_pred.write(str(objpred['ymax']))
file_pred.write('\n')
#precision = tp / (tp + fp)
# for box_gt in boxes_gt:
# for box_predict in boxes_predict:
# iou_val = bbox_iou(box_predict,box_gt)
# print(iou_val)
#count prediction which reache a threshold of let's say 0.5
# if we cahnge the dection threshold I think we'll get ROC curve - that'd be cute.
print('Finished! :o)')
def main(argv):
if(len(sys.argv) != 3):
print('Usage ./prepTrain.py [data_dir] [config.yml]')
sys.exit(1)
#Load data
data_dir = argv[1] + '/' #in case we forgot '/'
print('Opening file' + argv[2])
with open(argv[2], 'r') as configfile:
config = yaml.safe_load(configfile)
#TODO: since this is the first file to use, maybe add a check if all directories exist?
image_dir = data_dir
train_dir = data_dir
weights_dir = data_dir + config['weights_dir']
#Training type dependent
training_type = config['training_type']
print("Training type is " + training_type)
print(config[training_type])
your_weights = weights_dir + config[training_type]['weights']
#check md5 of a weights file if available
md5check(config[training_type]['weights_md5'],your_weights)
train_files_regex = config[training_type]['train_files_regex']
#based on get_yolo_model defaults and previous makTrain.py files
num_class=config[training_type]['num_class']
obj_thresh=config[training_type]['obj_thresh']
nms_thresh=config[training_type]['nms_thresh']
train_images = glob.glob( image_dir + train_files_regex )
annotations_file = train_dir + config['untrained_annotations_fname']
max_l=config['MAX_L'] #maximal object size in pixels
min_l=config['MIN_L']
im_width=config['IMAGE_W'] #size of training imageas for yolo
im_height=config['IMAGE_H']
##################################################
print("Loading YOLO models")
yolov3 = get_yolo_model(im_width,im_height,num_class,trainable=False)
yolov3.load_weights(your_weights,by_name=True) #TODO is by_name necessary here?
# Creating another model to provide visualisation and/or extraction of high level features
yolov3_feats = get_yolo_model_feats(im_width,im_height,num_class,trainable=False)
yolov3_feats.load_weights(your_weights,by_name=True) #TODO is by_name necessary here?
print("YOLO models loaded, my dear.")
########################################
im_num=1
all_imgs = []
for imagename in train_images:
im = cv2.imread(imagename)
print('processing image ' + imagename + ', ' + str(im_num) + ' of ' + str(len(train_images)) + '...')
im_yolo = makeYoloCompatible(im)
height, width = im_yolo.shape[:2]
im_num+=1
n_count=0
for x in np.arange(0,1+width-im_width,im_width):#'1+' added to allow case when image has exactly size of one window
for y in np.arange(0,1+height-im_height,im_height):
img_data = {'object':[]} #dictionary? key-value pair to store image data
head, tail = os.path.split(imagename)
noext, ext = os.path.splitext(tail)
save_name = train_dir + '/TR_' + noext + '-' + str(n_count) + '.png'
box_name = train_dir + '/bbox/' + noext + '-' + str(n_count) + '.png'
img = im[y:y+im_height,x:x+im_width,:]
cv2.imwrite(save_name, img)
img_data['filename'] = save_name
img_data['width'] = im_width
img_data['height'] = im_height
n_count+=1
# use the yolov3 model to predict 80 classes on COCO
# preprocess the image
image_h, image_w, _ = img.shape
new_image = img[:,:,::-1]/255.
new_image = np.expand_dims(new_image, 0)
# run the prediction
sys.stdout.write('Yolo predicting...')
sys.stdout.flush()
yolos = yolov3.predict(new_image)
# yolo_feats = yolov3_feats.predict(new_image)
# print(type(yolo_feats))
# print(type(yolo_feats[1]))
# print(yolo_feats[1].shape)
# print(yolo_feats[1].dtype)
# fileObject = open("feats.pickle",'wb')
# pickle.dump(yolo_feats[1],fileObject)
# fileObject.close()
# print("pickedleeee")
# cv2.imshow("heatmap",yolo_feats[1][:,:,1])
# k = cv2.waitKey(0)
sys.stdout.write('Decoding...')
sys.stdout.flush()
boxes = decode(yolos, obj_thresh, nms_thresh)
sys.stdout.write('Done!#of boxes:')
sys.stdout.write(str(len(boxes)))
sys.stdout.flush()
for b in boxes:
xmin=int(b[0])
xmax=int(b[2])
ymin=int(b[1])
ymax=int(b[3])
obj = {}
obj['name'] = 'aoi'
if xmin<0: continue
if ymin<0: continue
if xmax>im_width: continue
if ymax>im_height: continue
if (xmax-xmin)<min_l: continue
if (xmax-xmin)>max_l: continue
if (ymax-ymin)<min_l: continue
if (ymax-ymin)>max_l: continue
obj['xmin'] = xmin
obj['ymin'] = ymin
obj['xmax'] = xmax
obj['ymax'] = ymax
img_data['object'] += [obj]
cv2.rectangle(img, (xmin,ymin), (xmax,ymax), (0,255,0), 2)
cv2.imwrite(box_name, img)
all_imgs += [img_data]
#print(all_imgs)
print('Saving data to ' + annotations_file)
with open(annotations_file, 'w') as handle:
yaml.dump(all_imgs, handle)
print('Finished! :o)')
