print('Starting Video Analysis...')
with open('ssd/label_mapping.csv') as f:
mapping = {
int(x.split(',')[0].strip()): x.split(',')[1].strip()
for x in f.readlines()
}
vs = cv2.VideoCapture(video)
while True:
ret, frame = vs.read()
if not ret:
break
results = detector.getBoundingBoxes(frame)
if len(results) == 0:
continue
detections = []
for res in results:
det_id = 'N/A'
if jaguars_identifier.belongsToClass(res.classid):
det_id = jaguars_identifier.getIdFromBoundingBox(
frame, res.bounding_box)
elif tigers_identifier.belongsToClass(res.classid):
det_id = tigers_identifier.getIdFromBoundingBox(
frame, res.bounding_box)
elif elephants_identifier.belongsToClass(res.classid):
det_id = elephants_identifier.getIdFromBoundingBox(
idNum = None
if len(args) > 4:
idNum = int(args[4])
rev_map = {}
for x in id_map:
if not x[1] in rev_map:
rev_map[x[1]] = []
rev_map[x[1]].append(x[0])
print('Loading Object Detector')
det = ObjectDetector(objdetmodel)
det.SCORE_THRESHOLD = 0.7
det.loadModel()
# Warming up
det.getBoundingBoxes(readImageFromFile(os.path.join(folder,
rev_map[0][0])))
det.getBoundingBoxes(readImageFromFile(os.path.join(folder,
rev_map[0][0])))
print('Loading Object Identifier')
idn = SvmIdentifier(modelPath, det)
idn.loadModel()
if not idNum is None and not idn.isPresent(idNum):
print(
'Provided Id {} is not present in trained model, will choose random Id'
)
idNum = None
if idNum is None:
idNum = np.random.choice(list(idn.trained_ids))
print('Measuring performance over {} images...'.format(len(images)))
config = args[1]
weights = args[2]
#model = YoloObjectDetector(config, weights, '../darknet/darknet/custom_data/detector.data')
#model.loadModel()
model = ObjectDetector(config)
model.loadModel()
print('Warming up...')
#model.getBoundingBoxes(images[0])
#model.getBoundingBoxes(images[1])
model.getBoundingBoxes(cv2.imread(images[0]))
model.getBoundingBoxes(cv2.imread(images[1]))
[cv2.imread(x) for x in imagelist]
print('Starting...')
times = []
for im in imagelist:
#model.getBoundingBoxes(im)
img = cv2.imread(im)
st = time.time()
model.getBoundingBoxes(img)
times.append(time.time() - st)
print(
'Total time taken: {:.4f}s, Average time per detection: {:.4f}s, Min time: {:.4f}s, Max time: {:.4f}s'
.format(sum(times), (sum(times)) / n, min(times), max(times)))
print('Running Detection on {} images'.format(n))
box_map = {}
with open(args[2]) as f:
box_map = json.load(f)
f.close()
print('Loading Object Detector...')
det = ObjectDetector('ssd_quantized_1L_steps/saved_model')
det.loadModel()
#det.SCORE_THRESHOLD = 0.25
#det.IOU_THRESHOLD = 0.01
print('Warming up model...')
det.getBoundingBoxes(cv2.imread(imageFiles[0]))
det.getBoundingBoxes(cv2.imread(imageFiles[0]))
batchSize = 10
if 'jaguar' in folder.lower():
expected_classid = 1 # Only tigers
elif 'elp' in folder.lower() or 'elephant' in folder.lower():
expected_classid = 2
elif 'amur' in folder.lower():
expected_classid = 3
mAP = 0.
numBoxes = 0
wrong = 0
correct = 0
start = time.time()
rootCa=rootCa,
private_key=private_key,
certificate=certificate)
if not shadowClient:
print('Failed to connect to AWS IOT device')
exit(1)
deviceShadow = shadowClient.createShadowHandlerWithName(device, False)
CAMERA_IP = '<CAMERA IP>:<PORT>'
print('Loading Model...')
det = ObjectDetector('ssd/saved_model')
det.loadModel()
print('Model Load completed...')
print('Warming up Model...')
det.getBoundingBoxes(readImage(ip=CAMERA_IP))
det.getBoundingBoxes(readImage(ip=CAMERA_IP))
classes = loadClasses('ssd/label_mapping.csv')
app = Flask(__name__, static_url_path='')
if __name__ == '__main__':
image = readImage(ip=CAMERA_IP)
numPersons = countPeople(image)
print('Found {} people in the image!'.format(numPersons))
@app.route('/')
def loadUi():
return app.send_static_file('index.html')
print('Registered with MQTT server, publishing messages on channel: {}'.
format(DEVICE_NAME))
with picamera.PiCamera(resolution=(640, 480), framerate=30) as camera:
#camera.start_recording('my_video.h264')
with picamera.array.PiRGBArray(camera) as stream:
camera.start_preview()
time.sleep(2) # Camera warmup
for _ in camera.capture_continuous(stream, format='rgb'):
image = stream.array
#image = image[:,:,(2,1,0)]
stream.truncate()
stream.seek(0)
start = time.time()
results, image = detector.getBoundingBoxes(image)
if len(results) > 0:
# publisher.publishDetection(results)
sendImageAsync(imagePublisher, image, results)
print('Got results: {}, Time: {:.4f}s'.format(
results,
time.time() - start))
if display:
image = cv2.resize(
image, (detector.IMG_HEIGHT, detector.IMG_WIDTH))
if len(results) > 0:
drawBbOnImage(image, results)
cv2.imshow('test', image)
cv2.waitKey(1)
exit(1)
folder = args[1]
imageFiles = [
'{}/{}'.format(folder, x) for x in os.listdir(folder) if x.endswith('.jpg')
]
n = min(100, len(imageFiles))
print('Loading Model...')
det = ObjectDetector('ssd/saved_model')
det.loadModel()
print('Warming up model...')
# Run a dummy test once for warmup
img = cv2.imread(imageFiles[0])
det.getBoundingBoxes(img)
det.getBoundingBoxes(img)
batchTimes = []
lower = 10
upper = 11
if len(args) == 4:
lower = int(args[2])
upper = int(args[3])
f = open('test_times.csv', 'w')
f.write('Batch Size,Total Time,Average Time\n')
for i in range(lower, upper, 5):
batchSize = i
# Detect batchSize images at a time
