How to detect circular erosion/dilation Hi, I would like to detect circular erosions and dilations on a line. For dilat

Python is fine. For beginning there's one camera but later I might add more; could apply stitching - doesn't matter - wh

This is just a hypotethical example. The main thing I would like to solve is to track arbitrary points and assess their

Is there a way to mark the moving points and in the end determine the position of every point? Hi, Suppose there is a c

Is there a way to mark the moving points and in the end determine the position of every point? Hi, Suppose there is a c

Is there a way to mark the moving points and in the end determine the position of every point? Hi, Suppose there is a c

Unable to convert retrained TensorFlow model to IR Hi, I retrained the ssd_mobilenet_v2_coco to detect custom set of ob

Unable to convert retrained TensorFlow model to IR Hi, I retrained the ssd_mobilenet_v2_coco to detect custom set of ob

Unable to convert retrained TensorFlow model to IR Hi, I retrained the ssd_mobilenet_v2_coco to detect custom set of ob

Unable to convert retrained TensorFlow model to IR Hi, I retrained the ssd_mobilenet_v2_coco to detect custom set of ob

Hi,

After successfully running python face detection example, I tried to modify the code in order to run vehicle and licence plate detection, but the model didn't detect anything. As I haven't figured out what's the issue, I would appreciate any suggestion regarding the problem.

The script I wrote looks as follows:

import cv2

def predict(frame, net):
    # Prepare input blob and perform an inference
    blob = cv2.dnn.blobFromImage(frame, size=(300, 300), ddepth=cv2.CV_8U)
    net.setInput(blob)
    out = net.forward()

    predictions = []

    # The net outputs a blob with the shape: [1, 1, N, 7], where N is the number of detected bounding boxes.
    # For each detection, the description has the format: [image_id, label, conf, x_min, y_min, x_max, y_max]

    # Draw detected faces on the frame
    for detection in out.reshape(-1, 7):
        image_id, label, conf, x_min, y_min, x_max, y_max = detection

        if conf > 0.5:
            predictions.append(detection)

    # return the list of predictions to the calling function
    return predictions

# Load the model
net = cv2.dnn.readNet('models/vehicle-license-plate-detection-barrier-0106.xml', 'models/vehicle-license-plate-detection-barrier-0106.bin')

# Specify target device
net.setPreferableTarget(cv2.dnn.DNN_TARGET_MYRIAD)

# Read an image
frame = cv2.imread('screenshots/mercedes.jpg')

predictions = predict(frame, net)

# Draw detected faces on the frame
for prediction in predictions:
    confidence = float(detection[2])
    xmin = int(detection[3] * frame.shape[1])
    ymin = int(detection[4] * frame.shape[0])
    xmax = int(detection[5] * frame.shape[1])
    ymax = int(detection[6] * frame.shape[0])

    cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), color=(0, 255, 0))

# Save the frame to an image file
cv2.imwrite('out.png', frame)

Basically, I have downloaded the model files from this adddress and then modified input dimensions according to specifications.

To test the detection, I used the image attached below

The model I wanted to test is described here

Update

Now I tried to use vehicle-detection-adas-0002 model and it works. The differences I noticed are in the models:

• vehicle-license-plate-detection-barrier-0106 is a MobileNetV2 + SSD-based vehicle and license plate detector for the "Barrier" use case

• vehicle-detection-adas-0002 is a vehicle detection network based on an SSD framework with tuned MobileNet v1 as a feature extractor

So I guess these models might have to be initialized differently.

Thanks! Why isn't there another bounding box for a car, same as in the example, provided here

How to run pretrained model with OpenVINO on RPi Hi, After successfully running python face detection example, I tried

How to run pretrained model with OpenVINO on RPi Hi, After successfully running python face detection example, I tried

How to run pretrained model with OpenVINO on RPi Hi, After successfully running python face detection example, I tried

How to run pretrained model with OpenVINO on RPi Hi, After successfully running python face detection example, I tried

Hi,

To detect a black object in a cluttered scene I tried to follow this tutorial. Due to natural light the color of the object (e.g. black) is not uniform on the captured image and therefore it was not possible to filter it out using the method described in the tutorial mentioned above.

Does anyone know what is the best way to detect objects by color in cluttered scene?

Thanks!

@Tetragramm thanks for reminding me to clarify the question!

Hi,

I have trained a cascade classifier for a particular object. The classifier is supposed to be used in android app. For testing purposes I've written a python script and the model performance seemed great running it on PC. Then, when I tried to use the same model in android app, the performance was poor, both, in terms of speed and accuracy. Does anyone know what could be the cause for different accuracy of the same model?

Thanks!