# Finding bounding box coordinates in Object Detection

In some of the OpenCV implementations for object detection , I don't understand how the co-ordinates of the bounding box of an object are extracted from the image.

For example , In Object Detection using Deep Learning

There is a snippet of code I don't understand .

# loop over the detections
for i in np.arange(0, detections.shape[2]):
# extract the confidence (i.e., probability) associated with the
# prediction
confidence = detections[0, 0, i, 2]
# filter out weak detections by ensuring the confidence is
# greater than the minimum confidence
if confidence > args["confidence"]:
# extract the index of the class label from the detections,
# then compute the (x, y)-coordinates of the bounding box for
# the object
idx = int(detections[0, 0, i, 1])
box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
(startX, startY, endX, endY) = box.astype("int")
# display the prediction
label = "{}: {:.2f}%".format(CLASSES[idx], confidence * 100)
print("[INFO] {}".format(label))
cv2.rectangle(image, (startX, startY), (endX, endY),
COLORS[idx], 2)
y = startY - 15 if startY - 15 > 15 else startY + 15
cv2.putText(image, label, (startX, y),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)



In the line , box = detections[0, 0, i, 3:7] * np.array([w, h, w, h]) , Can someone please tell me why we multiply with the width and height of the image to get the co-ordinates of the box ? Doesn't the output detections[] itself give the co-ordinates ?

Now , my questions are , If 'detections' doesn't directly output the co-ordinates , what else does it output ?

And , Why is it required to multiply with the width and height of the image ?

Can someone please clear my doubts in a simple and effective manner ?