matterport / Mask_RCNN

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
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IndexError: list index out of range #2499

Open fatana1 opened 3 years ago

fatana1 commented 3 years ago

here is the error i got it . Traceback (most recent call last): File "C:\Users\刘\Desktop\yolov3-coco-tower\yolo_custom_detection\yolo_object_detection.py", line 68, in label = str(classes[class_ids[i]]) IndexError: list index out of range

Process finished with exit code 1

Here is the code.

 import cv2

import numpy as np import glob import random

Load Yolo

net = cv2.dnn.readNet("custom-yolov4-detector_best.weights", "yolov4.cfg")

Name custom object

classes = ["tower"]

Images path

images_path = glob.glob(r"mix*.jpg")

layer_names = net.getLayerNames() output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()] colors = np.random.uniform(0, 255, size=(len(classes), 3))

Insert here the path of your images

random.shuffle(images_path)

loop through all the images

for img_path in images_path:

Loading image

img = cv2.imread(img_path)
img = cv2.resize(img, None, fx=0.4, fy=0.4)
height, width, channels = img.shape

# Detecting objects
blob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, crop=False)

net.setInput(blob)
outs = net.forward(output_layers)

# Showing informations on the screen
class_ids = []
confidences = []
boxes = []
for out in outs:
    for detection in out:
        scores = detection[5:]
        class_id = np.argmax(scores)
        confidence = scores[class_id]
        if confidence > 0.3:
            # Object detected
            print(class_id)
            center_x = int(detection[0] * width)
            center_y = int(detection[1] * height)
            w = int(detection[2] * width)
            h = int(detection[3] * height)

            # Rectangle coordinates
            x = int(center_x - w / 2)
            y = int(center_y - h / 2)

            boxes.append([x, y, w, h])
            confidences.append(float(confidence))
            class_ids.append(class_id)

indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
print(indexes)
font = cv2.FONT_HERSHEY_PLAIN
for i in range(len(boxes)):
    if i in indexes:
        x, y, w, h = boxes[i]
        label = str(classes[class_ids[i]])
        color = colors[class_ids[i]]
        cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)
        cv2.putText(img, label, (x, y + 30), font, 3, color, 2)

cv2.imshow("Image", img)
key = cv2.waitKey(0)

cv2.destroyAllWindows()

srositad commented 2 years ago

Hello, I am sorry, did you solved this? I got same error. Thankyou

Pranav2105 commented 2 years ago

please answer if anyone solved this problem. Same error

fsl60 commented 4 months ago

Hey did anyone have the answer for this