Post Processing

Create empty lists for boxes, confidences and classIDs.

boxes = []
confidences = []
classIDs = []

We will iterate through all the outputs in the layerOutputs, and loop over each detection. We will filter weak predictions and update the boxes, confidences, and classIDs lists with stronger predictions:

for output in layerOutputs:
    print ("Shape of each output", output.shape)
    # loop over each of the detections
    for detection in output:
        # extract the class ID and confidence (i.e., probability)
        # of the current object detection
        scores = detection[5:]
        classID = np.argmax(scores)
        confidence = scores[classID]

        # filter out weak predictions by ensuring the detected
        # probability is greater than the minimum probability
        if confidence > 0.3:
            # scale the bounding box coordinates back relative to
            # the size of the image, keeping in mind that YOLO
            # actually returns the center (x, y)-coordinates of
            # the bounding box followed by the boxes' width and
            # height
            box = detection[0:4] * np.array([W, H, W, H])
            (centerX, centerY, width, height) = box.astype("int")

            # use the center (x, y)-coordinates to derive the top
            # and and left corner of the bounding box
            x = int(centerX - (width / 2))
            y = int(centerY - (height / 2))

            # update our list of bounding box coordinates,
            # confidences, and class IDs
            boxes.append([x, y, int(width), int(height)])
            confidences.append(float(confidence))
            classIDs.append(classID)
            print (LABELS[classID], detection[4], confidence)

Print the length of boxes:

print (len(boxes))

There are a total 24 valid predictions. Some of these predictions are overlapping. They are filtered using NMS (Non maxima suppression). It takes an IoU threshold and a confidence threshold:

idxs = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.3)
print (len(idxs))

We will then iterate through these predictions to get the boxes and confidences and put them on our image:

# ensure at least one detection exists
if len(idxs) > 0:
    # loop over the indexes we are keeping
    for i in idxs.flatten():
        # extract the bounding box coordinates
        (x, y) = (boxes[i][0], boxes[i][1])
        (w, h) = (boxes[i][2], boxes[i][3])

        # draw a bounding box rectangle and label on the frame
        color = [int(c) for c in COLORS[classIDs[i]]]
        cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)
        text = "{}: {:.4f}".format(LABELS[classIDs[i]],
            confidences[i])
        cv2.putText(img, text, (x, y - 5),
            cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)

Now display the images along with detections:

plt.imshow(fixColor(img))