quick change on opencv_dnn

Edmond Yoo

2018-09-14 292e6083251cd8bdab5a315c413042f276af670f

 opencv_dnn.py

@@ -2,6 +2,7 @@
import numpy as np
import os
import sys
from operator import itemgetter


# Disclaimer: majority of the basic framework in this file is modified from the following tutorial:
@@ -17,7 +18,7 @@


# Remove the bounding boxes with low confidence using non-maxima suppression
def postprocess(frame, outs, classes, thresh_conf, thresh_nms):
def post_process(frame, outs, thresh_conf, thresh_nms):
    frame_height = frame.shape[0]
    frame_width = frame.shape[1]

@@ -42,17 +43,11 @@
                confidences.append(float(confidence))
                boxes.append([left, top, width, height])

    # Perform non maximum suppression to eliminate redundant overlapping boxes with
    # lower confidences.
    indices = cv2.dnn.NMSBoxes(boxes, confidences, thresh_conf, thresh_nms)
    for i in indices:
        i = i[0]
        box = boxes[i]
        left = box[0]
        top = box[1]
        width = box[2]
        height = box[3]
        draw_pred(frame, class_ids[i], classes, confidences[i], left, top, left + width, top + height)
    # Perform non maximum suppression to eliminate redundant overlapping boxes with lower confidences.
    indices = [ind[0] for ind in cv2.dnn.NMSBoxes(boxes, confidences, thresh_conf, thresh_nms)]
    
    ret = [[class_ids[i], confidences[i], boxes[i]] for i in indices]
    return ret


# Draw the predicted bounding box
@@ -73,7 +68,7 @@
    cv2.putText(frame, label, (left, top), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255))


def detect_frame(net, classes, img, thresh_conf=0.5, thresh_nms=0.4, in_dim=(416, 416), out_path=None):
def detect_frame(net, classes, img, thresh_conf=0.5, thresh_nms=0.4, in_dim=(416, 416), display=True, out_path=None):
    # Create a 4D blob from a frame.
    blob = cv2.dnn.blobFromImage(img, 1 / 255, in_dim, [0, 0, 0], 1, crop=False)

@@ -84,7 +79,11 @@
    outs = net.forward(get_outputs_names(net))

    # Remove the bounding boxes with low confidence
    postprocess(img, outs, classes, thresh_conf, thresh_nms)
    obj_list = post_process(img, outs, thresh_conf, thresh_nms)
    for obj in obj_list:
        class_id, confidence, box = obj
        left, top, width, height = box
        draw_pred(img, class_id, classes, confidence, left, top, left + width, top + height)

    # Put efficiency information. The function getPerfProfile returns the
    # overall time for inference(t) and the timings for each of the layers(in layersTimes)
@@ -94,9 +93,14 @@

    if out_path is not None:
        cv2.imwrite(out_path, img.astype(np.uint8))
    if display:
        cv2.imshow('result', img)
        cv2.waitKey(0)

    return obj_list


def detect_video(net, classes, capture, thresh_conf=0.5, thresh_nms=0.4, in_dim=(416, 416), out_path=None):
def detect_video(net, classes, capture, thresh_conf=0.5, thresh_nms=0.4, in_dim=(416, 416), display=True, out_path=None):
    if out_path is not None:
        vid_writer = cv2.VideoWriter(out_path, cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), 30,
                                     (round(capture.get(cv2.CAP_PROP_FRAME_WIDTH)),
@@ -108,27 +112,9 @@
            print("End of video. Press any key to exit")
            cv2.waitKey(0)
            break
        '''
        # Create a 4D blob from a frame.
        blob = cv2.dnn.blobFromImage(frame, 1 / 255, in_dim, [0, 0, 0], 1, crop=False)

        # Sets the input to the network
        net.setInput(blob)

        # Runs the forward pass to get output of the output layers
        outs = net.forward(get_outputs_names(net))

        # Remove the bounding boxes with low confidence
        postprocess(frame, outs, classes, thresh_conf, thresh_nms)

        # Put efficiency information. The function getPerfProfile returns the
        # overall time for inference(t) and the timings for each of the layers(in layersTimes)
        t, _ = net.getPerfProfile()
        label = 'Inference time: %.2f ms' % (t * 1000.0 / cv2.getTickFrequency())
        cv2.putText(frame, label, (0, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255))
        '''
        detect_frame(net, classes, frame,
                     thresh_conf=thresh_conf, thresh_nms=thresh_nms, in_dim=in_dim, out_path=None)
        obj_list = detect_frame(net, classes, frame, thresh_conf=thresh_conf, thresh_nms=thresh_nms, in_dim=in_dim,
                                display=False, out_path=None)
        if display:
        cv2.imshow('result', frame)
        if out_path is not None:
            vid_writer.write(frame.astype(np.uint8))
@@ -137,24 +123,27 @@
    if out_path is not None:
        vid_writer.release()
    cv2.destroyAllWindows()
    pass


def main():
    # Specify paths for all necessary files
    test_path = '../data/test1.mp4'
    test_path = os.path.abspath('../data/test1.mp4')
    weight_path = 'weights/second_general/tiny_yolo_final.weights'
    cfg_path = 'cfg/tiny_yolo.cfg'
    class_path = "data/obj.names"
    out_dir = 'out'
    if not os.path.isfile(test_path):
        print('The test file %s doesn\'t exist!' % os.path.abspath(test_path))
        return
    if not os.path.isfile(weight_path):
        print('The weight file %s doesn\'t exist!' % os.path.abspath(test_path))
        return
    if not os.path.isfile(cfg_path):
        print('The config file %s doesn\'t exist!' % os.path.abspath(test_path))
        return
    if not os.path.isfile(class_path):
        print('The class file %s doesn\'t exist!' % os.path.abspath(test_path))
        return

    # Setup
    # Read class names from text file
@@ -162,8 +151,8 @@
        classes = [line.strip() for line in f.readlines()]
    # Load up the neural net using the config and weights
    net = cv2.dnn.readNetFromDarknet(cfg_path, weight_path)
    #net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV)
    #net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)
    net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV)
    net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)

    # Save the detection result if out_dir is provided
    if out_dir is None or out_dir == '':
@@ -172,6 +161,7 @@
        out_path = out_dir + '/' + os.path.split(test_path)[1]
    # Check if test file is image or video
    test_ext = test_path[test_path.find('.') + 1:]

    if test_ext in ['jpg', 'jpeg', 'bmp', 'png', 'tiff']:
        img = cv2.imread(test_path)
        detect_frame(net, classes, img, out_path=out_path)