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Can someone help me out on how to resolve this issue. It was first detecting the test image in colab but when I run it again it gives me this error. And when it gives me the bounding box on the test image, the font size is too small. I have tried different methods but its not working. I am doing all this training and testing in colab.
Here is the code. Object detection Api setup runs successfully and model is trained and tested.
img = cv2.imread(IMAGE_PATH)
image_np = np.array(img)
File "/content/drive/MyDrive/Tensorflow/Research/models/research/object_detection/core/preprocessor.py", line 3327, in resize_image *
new_image = tf.image.resize_images(
ValueError: 'images' must have either 3 or 4 dimension
The text was updated successfully, but these errors were encountered:
Your camera is not taking the input
make sure you have deleted the extra cap.release() cell
and try to alter the numbers in cv2.VideoCapture(number) mostly its either 0 or 1
Your camera is not taking the input make sure you have deleted the extra cap.release() cell and try to alter the numbers in cv2.VideoCapture(number) mostly its either 0 or 1
No I am not using camera. I am detecting using image stored in a folder. This error is removed now thank you for your reply. Kindly can you guide me on my second problem which I have already asked above. This problem "And when it gives me the bounding box on the test image, the font size is too small. I have tried different methods but its not working. I am doing all this training and testing in colab." Please if you can give some guidance
Can someone help me out on how to resolve this issue. It was first detecting the test image in colab but when I run it again it gives me this error. And when it gives me the bounding box on the test image, the font size is too small. I have tried different methods but its not working. I am doing all this training and testing in colab.
Here is the code. Object detection Api setup runs successfully and model is trained and tested.
img = cv2.imread(IMAGE_PATH)
image_np = np.array(img)
input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.float32)
detections = detect_fn(input_tensor)
num_detections = int(detections.pop('num_detections'))
detections = {key: value[0, :num_detections].numpy()
for key, value in detections.items()}
detections['num_detections'] = num_detections
detection_classes should be ints.
detections['detection_classes'] = detections['detection_classes'].astype(np.int64)
label_id_offset = 1
image_np_with_detections = image_np.copy()
viz_utils.visualize_boxes_and_labels_on_image_array(
image_np_with_detections,
detections['detection_boxes'],
detections['detection_classes']+label_id_offset,
detections['detection_scores'],
category_index,
use_normalized_coordinates=True,
line_thickness=9,
max_boxes_to_draw=5,
min_score_thresh=.3,
agnostic_mode=False)
image=cv2.resize(image_np_with_detections,(1500, 800),interpolation = cv2.INTER_NEAREST)
plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
plt.show()
Here is the error
File "/content/drive/MyDrive/Tensorflow/Research/models/research/object_detection/core/preprocessor.py", line 3327, in resize_image *
new_image = tf.image.resize_images(
The text was updated successfully, but these errors were encountered: