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ssd_object_detection.py
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68 lines (50 loc) · 2.1 KB
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from imutils.video import FPS
import numpy as np
import imutils
import cv2
use_gpu = True
live_video = False
confidence_level = 0.5
fps = FPS().start()
ret = True
CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
"bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
"dog", "horse", "motorbike", "person", "pottedplant", "sheep",
"sofa", "train", "tvmonitor"]
COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))
net = cv2.dnn.readNetFromCaffe('ssd_files/MobileNetSSD_deploy.prototxt', 'ssd_files/MobileNetSSD_deploy.caffemodel')
if use_gpu:
print("[INFO] setting preferable backend and target to CUDA...")
net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)
net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)
print("[INFO] accessing video stream...")
if live_video:
vs = cv2.VideoCapture(0)
else:
vs = cv2.VideoCapture('test.mp4')
while ret:
ret, frame = vs.read()
if ret:
frame = imutils.resize(frame, width=400)
(h, w) = frame.shape[:2]
blob = cv2.dnn.blobFromImage(frame, 0.007843, (300, 300), 127.5)
net.setInput(blob)
detections = net.forward()
for i in np.arange(0, detections.shape[2]):
confidence = detections[0, 0, i, 2]
if confidence > confidence_level:
idx = int(detections[0, 0, i, 1])
box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
(startX, startY, endX, endY) = box.astype("int")
label = "{}: {:.2f}%".format(CLASSES[idx], confidence * 100)
cv2.rectangle(frame, (startX, startY), (endX, endY), COLORS[idx], 2)
y = startY - 15 if startY - 15 > 15 else startY + 15
cv2.putText(frame, label, (startX, y), cv2.FONT_HERSHEY_DUPLEX, 0.5, COLORS[idx], 1)
frame = imutils.resize(frame,height=400)
cv2.imshow('Live detection',frame)
if cv2.waitKey(1)==27:
break
fps.update()
fps.stop()
print("[INFO] elasped time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))