10分钟学会使用YOLO及Opencv实现目标检测（下）|附源码

# import the necessary packagesimport numpy as npimport argparseimport imutilsimport timeimport cv2import os
# construct the argument parse and parse the argumentsap = argparse.ArgumentParser()
ap.add_argument("-i", "--input", required=True,
   help="path to input video")
ap.add_argument("-o", "--output", required=True,
   help="path to output video")
ap.add_argument("-y", "--yolo", required=True,
   help="base path to YOLO directory")
ap.add_argument("-c", "--confidence", type=float, default=0.5,
   help="minimum probability to filter weak detections")
ap.add_argument("-t", "--threshold", type=float, default=0.3,
   help="threshold when applyong non-maxima suppression")
args = vars(ap.parse_args())

# load the COCO class labels our YOLO model was trained on
labelsPath = os.path.sep.join([args["yolo"], "coco.names"])
LABELS = open(labelsPath).read().strip().split("\n")
# initialize a list of colors to represent each possible class label
np.random.seed(42)
COLORS = np.random.randint(0, 255, size=(len(LABELS), 3),
   dtype="uint8")
# derive the paths to the YOLO weights and model configuration
weightsPath = os.path.sep.join([args["yolo"], "yolov3.weights"])
configPath = os.path.sep.join([args["yolo"], "yolov3.cfg"])
# load our YOLO object detector trained on COCO dataset (80 classes)# and determine only the *output* layer names that we need from YOLOprint("[INFO] loading YOLO from disk...")
net = cv2.dnn.readNetFromDarknet(configPath, weightsPath)
ln = net.getLayerNames()
ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]

# initialize the video stream, pointer to output video file, and# frame dimensionsvs = cv2.VideoCapture(args["input"])
writer = None(W, H) = (None, None)
# try to determine the total number of frames in the video filetry:
   prop = cv2.cv.CV_CAP_PROP_FRAME_COUNT if imutils.is_cv2() \        else cv2.CAP_PROP_FRAME_COUNT
   total = int(vs.get(prop))
   print("[INFO] {} total frames in video".format(total))
# an error occurred while trying to determine the total# number of frames in the video fileexcept:
   print("[INFO] could not determine # of frames in video")
   print("[INFO] no approx. completion time can be provided")
   total = -1

# loop over frames from the video file streamwhile True:    # read the next frame from the file
   (grabbed, frame) = vs.read()
   # if the frame was not grabbed, then we have reached the end
   # of the stream
   if not grabbed:        break
   # if the frame dimensions are empty, grab them
   if W is None or H is None:
       (H, W) = frame.shape[:2]

ect Detection with OpenCVPython    # construct a blob from the input frame and then perform a forward
   # pass of the YOLO object detector, giving us our bounding boxes
   # and associated probabilities
   blob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (416, 416),
       swapRB=True, crop=False)
   net.setInput(blob)
   start = time.time()
   layerOutputs = net.forward(ln)
   end = time.time()    # initialize our lists of detected bounding boxes, confidences,
   # and class IDs, respectively
   boxes = []
   confidences = []
   classIDs = []

  # loop over each of the layer outputs
   for output in layerOutputs:        # 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 > args["confidence"]:                # 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)

    # apply non-maxima suppression to suppress weak, overlapping
   # bounding boxes
   idxs = cv2.dnn.NMSBoxes(boxes, confidences, args["confidence"],
       args["threshold"])
   # 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(frame, (x, y), (x + w, y + h), color, 2)
           text = "{}: {:.4f}".format(LABELS[classIDs[i]],
               confidences[i])
           cv2.putText(frame, text, (x, y - 5),
               cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)

    # check if the video writer is None
   if writer is None:        # initialize our video writer
       fourcc = cv2.VideoWriter_fourcc(*"MJPG")
       writer = cv2.VideoWriter(args["output"], fourcc, 30,
           (frame.shape[1], frame.shape[0]), True)
       # some information on processing single frame
       if total > 0:
           elap = (end - start)
           print("[INFO] single frame took {:.4f} seconds".format(elap))
           print("[INFO] estimated total time to finish: {:.4f}".format(
               elap * total))
   # write the output frame to disk
   writer.write(frame)
# release the file pointersprint("[INFO] cleaning up...")
writer.release()
vs.release()

$ python yolo_video.py --input videos/car_chase_01.mp4 \
   --output output/car_chase_01.avi --yolo yolo-coco
[INFO] loading YOLO from disk...
[INFO] 583 total frames in video
[INFO] single frame took 0.3500 seconds
[INFO] estimated total time to finish: 204.0238[INFO] cleaning up...

$ python yolo_video.py --input videos/car_chase_02.mp4 \
   --output output/car_chase_02.avi --yolo yolo-coco
[INFO] loading YOLO from disk...
[INFO] 3132 total frames in video
[INFO] single frame took 0.3455 seconds
[INFO] estimated total time to finish: 1082.0806[INFO] cleaning up...

$ python yolo_video.py --input videos/car_chase_03.mp4 \
   --output output/car_chase_03.avi --yolo yolo-coco
[INFO] loading YOLO from disk...
[INFO] 749 total frames in video
[INFO] single frame took 0.3442 seconds
[INFO] estimated total time to finish: 257.8418[INFO] cleaning up...

$ python yolo_video.py --input videos/overpass.mp4 \
   --output output/overpass.avi --yolo yolo-coco
[INFO] loading YOLO from disk...
[INFO] 812 total frames in video
[INFO] single frame took 0.3534 seconds
[INFO] estimated total time to finish: 286.9583[INFO] cleaning up...