# loop over the detections
for i in range(0, detections.shape[2]):
    # extract the confidence (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 > min_confidence:
        # compute the (x, y)-coordinates of the bounding box for the
        # object
        box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
        (startX, startY, endX, endY) = box.astype("int")
        
        # draw the bounding box of the face along with the associated
        # probability
        text = "{:.2f}%".format(confidence * 100)
        y = startY - 10 if startY - 10 > 10 else startY + 10
        cv2.rectangle(image, (startX, startY), (endX, endY),
            (0, 0, 255), 2)
            # (0,0,255) - red color
        cv2.putText(image, text, (startX, y),
            cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)
        
# show the output image
cv2.imshow("Output", image)
cv2.waitKey(0)

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