class ImageDetector(object):
def __init__(self):
self.detector = Detector(YOLO_DATA_PATH, YOLO_CFG_PATH,
YOLO_WEIGHTS_PATH)
# @pysnooper.snoop()
def classify_image(self, image_path):
print("Classfy : ", image_path)
res = self.detector.detect(image_path)
print(res)
img = Image.open(image_path)
dr = ImageDraw.Draw(img)
for data in res:
class_name = data['class']
x, y, w, h = data['left'], data['top'], data['right'] - data[
'left'], data['bottom'] - data['top']
# 画矩形框
dr.rectangle((x, y, x + w, y + h), outline=(46, 254, 46), width=3)
# 写文字
# 设置字体和大小
# myfont = ImageFont.truetype("static/glyphicons-halflings-regular.ttf", 100)
dr.text((data['left'], data['top']),
class_name,
font=myfont,
fill='red')
out_image_path = image_path[:image_path.rfind(
'.')] + '_deect' + image_path[image_path.rfind('.'):]
img.save(out_image_path)
return out_image_path
class Yolo():
def __init__(self):
abs_path = '/Users/woffee/www/language_design/yolo/'
self.detector = Detector(abs_path + 'darknet/cfg/coco.data',
abs_path + 'darknet/cfg/yolov3.cfg',
abs_path + 'darknet/yolov3.weights')
def detect(self, img_path):
results = self.detector.detect(img_path)
return results
class AutoLabeler:
def __init__(self):
self.current_directory = os.getcwd()
self.class_list = np.loadtxt("class_list.txt", dtype=np.str)
yolo_cfg_path_absolute = self.current_directory + YOLOCFGPATH
self.detector = Detector(
yolo_cfg_path_absolute + 'cfg/obj.data',
yolo_cfg_path_absolute + 'cfg/yolov3-tiny.cfg',
yolo_cfg_path_absolute + 'yolov3-tiny_final.weights')
if not os.path.isdir(LABEL_PATH):
os.mkdir(LABEL_PATH)
def label_images(self):
files_to_label = glob.glob(IMAGES_TO_LABEL_GLOB)
number_of_files = len(files_to_label)
print(f"{number_of_files} to label in total")
start_time = time.time()
for index, file_path in enumerate(files_to_label):
image = Image.open(file_path)
label_matrix = []
print(f"Labeling {index + 1}/{number_of_files}")
results = self.detector.detect(file_path)
is_first = True
for result in results:
if result["prob"] < 0.6:
continue
c = [
index for index, label in enumerate(self.class_list)
if label == result["class"]
][0]
width = result["right"] - result["left"]
height = result["bottom"] - result["top"]
x_coord = width / 2 + result["left"]
y_coord = height / 2 + result["top"]
width_relative = width / image.width
height_relative = height / image.height
x_coord_relative = x_coord / image.width
y_coord_relative = y_coord / image.height
label_matrix.append([
c, x_coord_relative, y_coord_relative, width_relative,
height_relative
])
label_matrix_np = np.array(label_matrix)
base_label_file_name = os.path.basename(
file_path)[:os.path.basename(file_path).rfind('.')] + ".txt"
label_file_name = f"{LABEL_PATH}/{base_label_file_name}"
np.savetxt(label_file_name, label_matrix_np)
time_elapsed = time.time() - start_time
estimated_time_remaining = ((time_elapsed / (index + 1)) *
(len(files_to_label) -
(index + 1))) / 60
print(
f"Saving file as {label_file_name} -- Est {estimated_time_remaining:.2f} mins remaining"
)
class YOLOThreeObjectDetector(ObjectDetector):
DARKNET_CFG_COCO_DATA = "./yolo_darknet_cfg/coco.data"
DARKNET_CFG_YOLO_CFG = "./yolo_darknet_cfg/yolov3.cfg"
YOLO_WEIGHTS = "./yolov3.weights"
def __init__(self):
self.detector = Detector(self.DARKNET_CFG_COCO_DATA,
self.DARKNET_CFG_YOLO_CFG, self.YOLO_WEIGHTS)
def detect_humans(self, img, img_path, logger):
return self.detector.detect(img_path)
def get_human_boxes(self, outputs, logger):
boxes_list = [[b['left'], b['top'], b['right'], b['bottom']]
for b in outputs
if b['class'] == 'person' and b['prob'] >= 0.95]
logger.log['detected_humans_per_frame'].append(len(boxes_list))
logger.log['boxes_per_frame'].append(boxes_list)
return boxes_list
class Vision:
def __init__(self):
self.rs_pipeline = rs.pipeline()
self.current_directory = os.getcwd()
yolo_cfg_path_absolute = self.current_directory + YOLOCFGPATH
self.image_path = self.current_directory + "/" + IMAGE_NAME
self.detector = Detector(yolo_cfg_path_absolute + 'cfg/obj.data', yolo_cfg_path_absolute + 'cfg/yolov3-tiny.cfg', yolo_cfg_path_absolute + 'yolov3-tiny_final.weights')
self.counter = 0
self.first_run = True
self.results = None
self.orientationCNN = OrientationDetector(ORIENTATION_MODEL_PATH)
def __del__(self):
# Stop streaming
self.rs_pipeline.stop()
def capture_image(self):
if self.first_run:
cfg = rs.config()
# cfg.enable_stream(realsense.stream.depth, 1280, 720, realsense.format.z16, 30)
cfg.enable_stream(rs.stream.color, 1920, 1080, rs.format.rgb8, 30)
profile = self.rs_pipeline.start(cfg)
sensors = profile.get_device().query_sensors()
rgb_camera = sensors[1]
rgb_camera.set_option(rs.option.white_balance, 4600)
rgb_camera.set_option(rs.option.exposure, 80)
#rgb_camera.set_option(rs.option.saturation, 65)
#rgb_camera.set_option(rs.option.contrast, 50)
frames = None
# wait for autoexposure to catch up
for i in range(90):
frames = self.rs_pipeline.wait_for_frames()
self.first_run = False
frames = self.rs_pipeline.wait_for_frames()
color_frame = frames.get_color_frame()
# Convert images to numpy arrays
color_image = np.asanyarray(color_frame.get_data())
color_image_ready_to_save = pimg.fromarray(color_image, 'RGB')
color_image_ready_to_save.save(self.image_path)
return color_image
def find_parts(self, class_id, fuse_index=-1):
class_id1, class_id2 = class_id
part = (-1, -1, -1, -1, -1)
# result is an array of dictionaries
found_class_index = 0
for i in range(len(self.results)):
d = self.results[i]
if (d['class'] == class_id1 or d['class'] == class_id2) and d['prob'] > 0.6:
if fuse_index > -1 and fuse_index != found_class_index:
found_class_index += 1
continue
part_class = d['class']
prob = d['prob']
width = d['right'] - d['left']
height = d['bottom'] - d['top']
x_coord = width / 2 + d['left']
y_coord = height / 2 + d['top']
if height > width:
orientation = OrientationEnum.VERTICAL.value
grip_width = width * 0.58
elif width > height:
orientation = OrientationEnum.HORIZONTAL.value
grip_width = height * 0.58
else:
orientation = OrientationEnum.HORIZONTAL.value
grip_width = height * 0.58
print("[W] Could not determine orientation, using 1 as default")
new_part_id = convert_to_part_id(part_class)
part = (new_part_id, x_coord, y_coord, orientation, grip_width)
break
print(part)
return part
def detect_object(self):
self.results = self.detector.detect(self.image_path)
self.draw_boxes(self.results)
def draw_boxes(self, results):
source_img = pimg.open(self.image_path).convert("RGBA")
for i in range(len(results)):
d = results[i]
if d['prob'] > 0.6:
classify = d['class']
prob = d['prob']
width = d['right'] - d['left']
height = d['bottom'] - d['top']
x_coord = width / 2 + d['left']
y_coord = height / 2 + d['top']
draw = ImageDraw.Draw(source_img)
draw.rectangle(((d['left'], d['top']), (d['right'], d['bottom'])), fill=None, outline=(200, 0, 150), width=6)
draw.text((x_coord, y_coord), d['class'])
source_img.save('boundingboxes.png')
def is_facing_right(self, np_image):
result = self.orientationCNN.is_facing_right(np_image)
print("[INFO] Part is facing right. {}".format(result))
return result
def get_image_path(self):
return self.image_path
from darknetpy.detector import Detector
detector = Detector('/Users/daniel/Workspace/darknet',
'/Users/daniel/Workspace/darknet/cfg/coco.data',
'/Users/daniel/Workspace/darknet/cfg/yolo.cfg',
'/Users/daniel/Workspace/darknet/yolo.weights')
results = detector.detect('/Users/daniel/Workspace/darknet/data/dog.jpg')
print(results)
jsonfile = open('/FilepathHere/blob.json', 'a+')
filenum = 0
if os.path.isfile(index):
filename = open(index, 'r')
if filename:
filenum = filename.read()
filename.close()
#darknetpy
detector = Detector('/FilepathHere/darknet',
'/FilepathHere/darknet/cfg/coco.data',
'/FilepathHere/darknet/cfg/yolov2.cfg',
'/FilepathHere/darknet/yolov2.weights')
#Define array to hold results
results = {}
#JSON
output = {}
output['ImageID'] = ''
output['YOLOOutputString'] = results
for i in range(int(filenum), len(file_list) - 1):
results[i] = detector.detect(img_path + file_list[i])
output['ImageID'] = file_list[i]
output['YOLOOutputString'] = results[i]
json_blob = json.dumps(output)
print(file_list[i] + ' ###COMPLETE###' + '\n')
print(i, file=open('/FilepathHere/index.txt', 'w'))
jsonfile.write(json_blob + '\n')
print(results)
#print (json.loads(stdout_json)['ImageID'])
import cv2
prototxt = "/media/victor/57a90e07-058d-429d-a357-e755d0820324/Projects/Tiny-yolo/yolo_tiny_deploy.prototxt"
model = "/media/victor/57a90e07-058d-429d-a357-e755d0820324/Projects/Tiny-yolo/yolo_tiny.caffemodel"
darknet = "/media/victor/57a90e07-058d-429d-a357-e755d0820324/Projects/darknet"
image = "/home/victor/Projects/Thesis/Testimages/test1.jpg"
conf = 0.1
from darknetpy.detector import Detector
detector = Detector(darknet, '%s/cfg/coco.data' % darknet,
'%s/cfg/tiny-yolo.cfg' % darknet,
'%s/tiny-yolo.weights' % darknet)
start = time.time()
results = detector.detect(image)
end = time.time()
print("[INFO] it took %s seconds." % (end - start))
print(results)
# image = cv2.imread(image)
# (h, w) = image.shape[:2]
# # https://www.pyimagesearch.com/2017/11/06/deep-learning-opencvs-blobfromimage-works/
# blob = cv2.dnn.blobFromImage(cv2.resize(image, (448, 448)), 1, (448, 448), (104, 117, 123))
#
# # cv2.imshow("Output", image)
# # cv2.waitKey(0)
#
#
# # load our serialized model from disk
# need to pip3 install darknetpy
# run on python3
from darknetpy.detector import Detector
detector = Detector('dev/darknet',
'ds/swx_longgun.data',
'ds/swx-yolo-voc.2.0.cfg',
'swx-yolo-voc_18000.weights')
results = detector.detect('data/gun.jpg')
print(results)
