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import argparse | |
import cv2 | |
import numpy as np | |
parser = argparse.ArgumentParser(add_help=False) | |
parser.add_argument("--image", default='samples/image.jpg', help="image for prediction") | |
parser.add_argument("--config", default='cfg/yolov3.cfg', help="YOLO config path") | |
parser.add_argument("--weights", default='yolov3.weights', help="YOLO weights path") | |
parser.add_argument("--names", default='data/coco.names', help="class names path") | |
args = parser.parse_args() | |
CONF_THRESH, NMS_THRESH = 0.5, 0.5 | |
# Load the network | |
net = cv2.dnn.readNetFromDarknet(args.config, args.weights) | |
net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV) | |
net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU) | |
# Get the output layer from YOLO | |
layers = net.getLayerNames() | |
output_layers = [layers[i[0] - 1] for i in net.getUnconnectedOutLayers()] | |
# Read and convert the image to blob and perform forward pass to get the bounding boxes with their confidence scores | |
img = cv2.imread(args.image) | |
height, width = img.shape[:2] | |
blob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), swapRB=True, crop=False) | |
net.setInput(blob) | |
layer_outputs = net.forward(output_layers) | |
class_ids, confidences, b_boxes = [], [], [] | |
for output in layer_outputs: | |
for detection in output: | |
scores = detection[5:] | |
class_id = np.argmax(scores) | |
confidence = scores[class_id] | |
if confidence > CONF_THRESH: | |
center_x, center_y, w, h = (detection[0:4] * np.array([width, height, width, height])).astype('int') | |
x = int(center_x - w / 2) | |
y = int(center_y - h / 2) | |
b_boxes.append([x, y, int(w), int(h)]) | |
confidences.append(float(confidence)) | |
class_ids.append(int(class_id)) | |
# Perform non maximum suppression for the bounding boxes to filter overlapping and low confident bounding boxes | |
indices = cv2.dnn.NMSBoxes(b_boxes, confidences, CONF_THRESH, NMS_THRESH).flatten().tolist() | |
# Draw the filtered bounding boxes with their class to the image | |
with open(args.names, "r") as f: | |
classes = [line.strip() for line in f.readlines()] | |
colors = np.random.uniform(0, 255, size=(len(classes), 3)) | |
for index in indices: | |
x, y, w, h = b_boxes[index] | |
cv2.rectangle(img, (x, y), (x + w, y + h), colors[index], 2) | |
cv2.putText(img, classes[class_ids[index]], (x + 5, y + 20), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, colors[index], 2) | |
cv2.imshow("image", img) | |
cv2.waitKey(0) | |
cv2.destroyAllWindows() |
@darius-luca-tech am getting the issue when am using the test image, but no issue for the trained images did you find any resolution to it.
@vinooniv: am getting the error because am getting the confidence, scores as zero with the test images. currently i trained the model with max_batches = 600 instead of the mentioned 4000
again retried by training the model with max_batches = 4000 still am getting the same error
Can you guys please provide some inputs to proceed further on this issue.
@darius-luca-tech I am getting the same error.
Please Help!!
I am also facing same issue
any solution?
Probably one of the three below.
- Make sure you have yolov3 file in your model path. (In my case, the yolov3 file was a file that had not been downloaded)
- Check your image file. (It may not be in that path or it may be the wrong image.)
- The list of b_boxes may be empty.
AttributeError: 'tuple' object has no attribute 'flatten'
Can I have some tips how to solve it?
Following up can you please help if anyone solved?
@darius-luca-tech did you find any solution to the same?
TIA.
AttributeError: 'tuple' object has no attribute 'flatten'
Can I have some tips how to solve it?