def main():
# python somehow needs to know about the data vector type, so we provide it
buffer = model.FloatVector(224 * 224 * 3)
results = model.FloatVector(1000)
# Pick the model you want to work with
helper = mh.ModelHelper(sys.argv, "darknetReference",
["darknet.cfg", "darknet.weights"],
"darknetImageNetLabels.txt")
# Initialize image source
helper.init_image_source()
lastPrediction = ""
while (True):
# Grab next frame
frame = helper.get_next_frame()
# Prepare the image to send to the model.
# This involves scaling to the required input dimension and re-ordering from BGR to RGB
data = helper.prepare_image_for_predictor(frame)
# Get the compiled model to classify the image, by returning a list of probabilities for the classes it can detect
model.darknet_reference_predict(data, results)
# Get the (at most) top 5 predictions that meet our threshold. This is returned as a list of tuples,
# each with the text label and the prediction score.
top5 = helper.get_top_n(results, 5)
# Turn the top5 into a text string to display
text = "".join([
str(element[0]) + "(" + str(int(100 * element[1])) + "%) "
for element in top5
])
if (text != lastPrediction):
print(text)
lastPrediction = text
# Draw the text on the frame
frameToShow = frame
helper.draw_label(frameToShow, text)
helper.draw_fps(frameToShow)
# Show the new frame
cv2.imshow('frame', frameToShow)
# Wait for Esc key
if cv2.waitKey(1) & 0xFF == 27:
break
# print profiling info if model is compiled with profiling on
if hasattr(model, "darknet_reference_print_model_profiling_info"):
print("model statistics:")
model.darknet_reference_print_model_profiling_info()
if hasattr(model, "darknet_reference_print_node_profiling_info"):
print("node statistics:")
model.darknet_reference_print_node_profiling_info()
def main():
# python somehow needs to know about the data vector type, so we provide it
buffer = model.FloatVector(224 * 224 * 3)
results = model.FloatVector(1000)
camera = 0
if (len(sys.argv) > 1):
camera = int(sys.argv[1])
# Start video capture device
cap = cv2.VideoCapture(camera)
# Pick the model you want to work with
helper = mh.ModelHelper("darknetReference",
["darknet.cfg", "darknet.weights"],
"darknetImageNetLabels.txt")
lastPrediction = ""
while (True):
# grab a frame
ret, frame = cap.read()
# Prepare the image to send to the model.
# This involves scaling to the required input dimension and re-ordering from BGR to RGB
data = helper.prepare_image_for_predictor(frame)
# Get the compiled model to classify the image, by returning a list of probabilities for the classes it can detect
model.darknet_reference_darknet_reference(data, results)
# Get the (at most) top 5 predictions that meet our threshold. This is returned as a list of tuples,
# each with the text label and the prediction score.
top5 = helper.get_top_n(results, 5)
# Turn the top5 into a text string to display
text = "".join([
str(element[0]) + "(" + str(int(100 * element[1])) + "%) "
for element in top5
])
if (text != lastPrediction):
print(text)
lastPrediction = text
# Draw the text on the frame
frameToShow = frame
helper.draw_label(frameToShow, text)
helper.draw_fps(frameToShow)
# Show the new frame
cv2.imshow('frame', frameToShow)
# Wait for Esc key
if cv2.waitKey(1) & 0xFF == 27:
break