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openvino_tiny-yolov3_single_image.py
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openvino_tiny-yolov3_single_image.py
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import sys, os, cv2, time
import numpy as np, math
from argparse import ArgumentParser
try:
from armv7l.openvino.inference_engine import IENetwork, IEPlugin
except:
from openvino.inference_engine import IENetwork, IEPlugin
m_input_size = 416
yolo_scale_13 = 13
yolo_scale_26 = 26
yolo_scale_52 = 52
classes = 10
coords = 4
num = 3
anchors = [7, 13, 10, 19, 14, 24, 16, 31, 16, 52, 22, 40, 27, 54, 41, 57, 74, 75]
LABELS = (
"signal-1",
"signal-2",
"signal-3",
"signal-4",
"signal-5",
"signal-6",
"signal-7",
"signal-8",
"signal-9",
"signal-start")
label_text_color = (255, 255, 255)
label_background_color = (125, 175, 75)
box_color = (255, 128, 0)
box_thickness = 1
def build_argparser():
parser = ArgumentParser()
parser.add_argument("-d", "--device", help="Specify the target device to infer on; CPU, GPU, FPGA or MYRIAD is acceptable. \
Sample will look for a suitable plugin for device specified (CPU by default)", default="CPU", type=str)
return parser
def EntryIndex(side, lcoords, lclasses, location, entry):
n = int(location / (side * side))
loc = location % (side * side)
return int(n * side * side * (lcoords + lclasses + 1) + entry * side * side + loc)
class DetectionObject():
xmin = 0
ymin = 0
xmax = 0
ymax = 0
class_id = 0
confidence = 0.0
def __init__(self, x, y, h, w, class_id, confidence, h_scale, w_scale):
self.xmin = int((x - w / 2) * w_scale)
self.ymin = int((y - h / 2) * h_scale)
self.xmax = int(self.xmin + w * w_scale)
self.ymax = int(self.ymin + h * h_scale)
self.class_id = class_id
self.confidence = confidence
def IntersectionOverUnion(box_1, box_2):
width_of_overlap_area = min(box_1.xmax, box_2.xmax) - max(box_1.xmin, box_2.xmin)
height_of_overlap_area = min(box_1.ymax, box_2.ymax) - max(box_1.ymin, box_2.ymin)
area_of_overlap = 0.0
if (width_of_overlap_area < 0.0 or height_of_overlap_area < 0.0):
area_of_overlap = 0.0
else:
area_of_overlap = width_of_overlap_area * height_of_overlap_area
box_1_area = (box_1.ymax - box_1.ymin) * (box_1.xmax - box_1.xmin)
box_2_area = (box_2.ymax - box_2.ymin) * (box_2.xmax - box_2.xmin)
area_of_union = box_1_area + box_2_area - area_of_overlap
retval = 0.0
if area_of_union <= 0.0:
retval = 0.0
else:
retval = (area_of_overlap / area_of_union)
return retval
def ParseYOLOV3Output(blob, resized_im_h, resized_im_w, original_im_h, original_im_w, threshold, objects):
out_blob_h = blob.shape[2]
out_blob_w = blob.shape[3]
side = out_blob_h
anchor_offset = 0
if len(anchors) == 18: ## YoloV3
if side == yolo_scale_13:
anchor_offset = 2 * 6
elif side == yolo_scale_26:
anchor_offset = 2 * 3
elif side == yolo_scale_52:
anchor_offset = 2 * 0
elif len(anchors) == 12: ## tiny-YoloV3
if side == yolo_scale_13:
anchor_offset = 2 * 3
elif side == yolo_scale_26:
anchor_offset = 2 * 0
else: ## ???
if side == yolo_scale_13:
anchor_offset = 2 * 6
elif side == yolo_scale_26:
anchor_offset = 2 * 3
elif side == yolo_scale_52:
anchor_offset = 2 * 0
side_square = side * side
output_blob = blob.flatten()
for i in range(side_square):
row = int(i / side)
col = int(i % side)
for n in range(num):
obj_index = EntryIndex(side, coords, classes, n * side * side + i, coords)
box_index = EntryIndex(side, coords, classes, n * side * side + i, 0)
scale = output_blob[obj_index]
if (scale < threshold):
continue
x = (col + output_blob[box_index + 0 * side_square]) / side * resized_im_w
y = (row + output_blob[box_index + 1 * side_square]) / side * resized_im_h
height = math.exp(output_blob[box_index + 3 * side_square]) * anchors[anchor_offset + 2 * n + 1]
width = math.exp(output_blob[box_index + 2 * side_square]) * anchors[anchor_offset + 2 * n]
for j in range(classes):
class_index = EntryIndex(side, coords, classes, n * side_square + i, coords + 1 + j)
prob = scale * output_blob[class_index]
if prob < threshold:
continue
obj = DetectionObject(x, y, height, width, j, prob, (original_im_h / resized_im_h), (original_im_w / resized_im_w))
objects.append(obj)
return objects
def main_IE_infer():
camera_width = 1280
camera_height = 960
fps = ""
framepos = 0
frame_count = 0
vidfps = 0
skip_frame = 0
elapsedTime = 0
new_w = int(camera_width * min(m_input_size/camera_width, m_input_size/camera_height))
new_h = int(camera_height * min(m_input_size/camera_width, m_input_size/camera_height))
args = build_argparser().parse_args()
#model_xml = "lrmodels/tiny-YoloV3/FP32/frozen_tiny_yolo_v3.xml" #<--- CPU
#model_xml = "lrmodels/tiny-YoloV3/FP16/frozen_tiny_yolo_v3.xml" #<--- MYRIAD
model_xml = "frozen_darknet_yolov3_tiny_model_signals_CPU.xml"
model_bin = os.path.splitext(model_xml)[0] + ".bin"
# cap = cv2.VideoCapture(0)
# cap.set(cv2.CAP_PROP_FPS, 30)
# cap.set(cv2.CAP_PROP_FRAME_WIDTH, camera_width)
# cap.set(cv2.CAP_PROP_FRAME_HEIGHT, camera_height)
#cap = cv2.VideoCapture("ableitung.mp4")
#camera_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
#camera_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
#frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
#vidfps = int(cap.get(cv2.CAP_PROP_FPS))
#print("videosFrameCount =", str(frame_count))
#print("videosFPS =", str(vidfps))
img = cv2.imread("signals-dataset/yolov3/signal-1516.jpg")
#cam = cv2.VideoCapture(0)
#s, img = cam.read()
time.sleep(1)
plugin = IEPlugin(device=args.device)
if "CPU" in args.device:
plugin.add_cpu_extension("libcpu_extension_sse4.so")
net = IENetwork(model=model_xml, weights=model_bin)
input_blob = next(iter(net.inputs))
exec_net = plugin.load(network=net)
start_time = time.time()
resized_image = cv2.resize(img, (new_w, new_h), interpolation = cv2.INTER_CUBIC)
canvas = np.full((m_input_size, m_input_size, 3), 128)
canvas[(m_input_size-new_h)//2:(m_input_size-new_h)//2 + new_h,(m_input_size-new_w)//2:(m_input_size-new_w)//2 + new_w, :] = resized_image
prepimg = canvas
prepimg = prepimg[np.newaxis, :, :, :] # Batch size axis add
prepimg = prepimg.transpose((0, 3, 1, 2)) # NHWC to NCHW
outputs = exec_net.infer(inputs={input_blob: prepimg})
print('time taken: {}'.format(time.time() - start_time))
objects = []
for output in outputs.values():
objects = ParseYOLOV3Output(output, new_h, new_w, camera_height, camera_width, 0.4, objects)
for object in objects:
print(LABELS[object.class_id], object.confidence, "%")
# Filtering overlapping boxes
objlen = len(objects)
for i in range(objlen):
if (objects[i].confidence == 0.0):
continue
for j in range(i + 1, objlen):
if (IntersectionOverUnion(objects[i], objects[j]) >= 0.4):
if objects[i].confidence < objects[j].confidence:
objects[i], objects[j] = objects[j], objects[i]
objects[j].confidence = 0.0
# Drawing boxes
for obj in objects:
if obj.confidence < 0.02:
continue
label = obj.class_id
confidence = obj.confidence
#if confidence >= 0.2:
label_text = LABELS[label] + " (" + "{:.1f}".format(confidence * 100) + "%)"
cv2.rectangle(img, (obj.xmin, obj.ymin), (obj.xmax, obj.ymax), box_color, box_thickness)
cv2.putText(img, label_text, (obj.xmin, obj.ymin - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.6, label_text_color, 1)
cv2.putText(img, fps, (camera_width - 170, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (38, 0, 255), 1, cv2.LINE_AA)
cv2.imwrite("Result.jpg", img)
cv2.destroyAllWindows()
del net
del exec_net
del plugin
if __name__ == '__main__':
sys.exit(main_IE_infer() or 0)