YOLO with OpenCV DNN module

opencv_yolo_detector.py

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.

1. Make sure you have yolov3 file in your model path. (In my case, the yolov3 file was a file that had not been downloaded)
2. Check your image file. (It may not be in that path or it may be the wrong image.)
3. 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.