Testing model training dengan gambar uji.py

import cv2
import numpy as np
import glob
import random
import time

net = cv2.dnn.readNet("yolov3_training_final.weights", "yolov3-training.cfg")

classes = []
with open("classes.txt", "r") as f:
    classes = f.read().splitlines()


images_path = glob.glob(r"C:\Users\user\Documents\kuliah\MSIB\Batch 3\Project Capstone Perancangan Manufaktur\10000 max batch & 64 batch size_fix\10000 max batch & 64 batch size\IMG_20221012_125711.jpg") #ganti dengan direktori gambar anda
layer_names = net.getLayerNames()
output_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()]
colors = np.random.uniform(0, 255, size=(len(classes), 3))
random.shuffle(images_path)
for img_path in images_path:
    img = cv2.imread(img_path)
    img = cv2.resize(img, (480,360))
    height, width, channels = img.shape

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

    net.setInput(blob)
    start = time.time()
    outs = net.forward(output_layers)
    end = time.time()
    print("[INFO] Waktu deteksi yolo {:.6f} detik".format(end - start))


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 > 0.5:
            # Deteksi objek
            center_x = int(detection[0] * width)
            center_y = int(detection[1] * height)
            w = int(detection[2] * width)
            h = int(detection[3] * height)
            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, 1, color,
3)
cv2.imshow("Kardus Detection", img)
cv2.waitKey(0)
cv2.destroyAllWindows()