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openvino_tiny-yolov3_test.py
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openvino_tiny-yolov3_test.py
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import sys, os, cv2, time
import numpy as np, math
from argparse import ArgumentParser
from openvino.inference_engine import IENetwork, IEPlugin
m_input_size = 416
yolo_scale_13 = 13
yolo_scale_26 = 26
yolo_scale_52 = 52
classes = 80
coords = 4
num = 3
anchors = [10,14, 23,27, 37,58, 81,82, 135,169, 344,319]
LABELS = ("person", "bicycle", "car", "motorbike", "aeroplane",
"bus", "train", "truck", "boat", "traffic light",
"fire hydrant", "stop sign", "parking meter", "bench", "bird",
"cat", "dog", "horse", "sheep", "cow",
"elephant", "bear", "zebra", "giraffe", "backpack",
"umbrella", "handbag", "tie", "suitcase", "frisbee",
"skis", "snowboard", "sports ball", "kite", "baseball bat",
"baseball glove", "skateboard", "surfboard","tennis racket", "bottle",
"wine glass", "cup", "fork", "knife", "spoon",
"bowl", "banana", "apple", "sandwich", "orange",
"broccoli", "carrot", "hot dog", "pizza", "donut",
"cake", "chair", "sofa", "pottedplant", "bed",
"diningtable", "toilet", "tvmonitor", "laptop", "mouse",
"remote", "keyboard", "cell phone", "microwave", "oven",
"toaster", "sink", "refrigerator", "book", "clock",
"vase", "scissors", "teddy bear", "hair drier", "toothbrush")
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 = 320
camera_height = 240
fps = ""
framepos = 0
frame_count = 0
vidfps = 0
skip_frame = 0
elapsedTime = 0
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_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("data/input/testvideo.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))
time.sleep(1)
plugin = IEPlugin(device=args.device)
if "CPU" in args.device:
plugin.add_cpu_extension("lib/libcpu_extension.so")
net = IENetwork(model=model_xml, weights=model_bin)
input_blob = next(iter(net.inputs))
exec_net = plugin.load(network=net)
while cap.isOpened():
t1 = time.time()
## Uncomment only when playing video files
#cap.set(cv2.CAP_PROP_POS_FRAMES, framepos)
ret, image = cap.read()
if not ret:
break
prepimg = cv2.resize(image, (m_input_size, m_input_size))
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})
#output_name = detector/yolo-v3-tiny/Conv_12/BiasAdd/YoloRegion
#output_name = detector/yolo-v3-tiny/Conv_9/BiasAdd/YoloRegion
objects = []
for output in outputs.values():
objects = ParseYOLOV3Output(output, m_input_size, m_input_size, camera_height, camera_width, 0.2, objects)
# 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 < 0.2:
continue
label = obj.class_id
confidence = obj.confidence
#if confidence >= 0.2:
label_text = LABELS[label] + " (" + "{:.1f}".format(confidence * 100) + "%)"
cv2.rectangle(image, (obj.xmin, obj.ymin), (obj.xmax, obj.ymax), box_color, box_thickness)
cv2.putText(image, label_text, (obj.xmin, obj.ymin - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.6, label_text_color, 1)
cv2.putText(image, fps, (camera_width - 170, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (38, 0, 255), 1, cv2.LINE_AA)
cv2.imshow("Result", image)
if cv2.waitKey(1)&0xFF == ord('q'):
break
elapsedTime = time.time() - t1
fps = "(Playback) {:.1f} FPS".format(1/elapsedTime)
## frame skip, video file only
#skip_frame = int((vidfps - int(1/elapsedTime)) / int(1/elapsedTime))
#framepos += skip_frame
cv2.destroyAllWindows()
del net
del exec_net
del plugin
if __name__ == '__main__':
sys.exit(main_IE_infer() or 0)