handai-detector/examples/yolo_obj_detector.py

"""USAGE:

time python examples/test.py --input ~/Desktop/5min.mp4 -o output.mp4
time python examples/test.py --input ~/Desktop/5min.mp4 -l

"""
# import the necessary packages
import numpy as np
import argparse
import imutils
import time
import cv2
import os

# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--input", required=True, help="path to input video")
ap.add_argument("-o", "--output", required=False, help="path to output video")
ap.add_argument("-l", "--live", action='store_true', help="Show live detection")
# 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"])
labelsPath = "/home/sipp11/syncthing/dropbox/tracking-obj/mytrain.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"])

weightsPath = "/home/sipp11/syncthing/dropbox/tracking-obj/mytrain_final.weights"
configPath = "/home/sipp11/syncthing/dropbox/tracking-obj/mytrain.cfg"

# load our YOLO object detector trained on COCO dataset (80 classes)
# and determine only the *output* layer names that we need from YOLO
print("[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 dimensions
vs = cv2.VideoCapture(args["input"])
writer = None
(W, H) = (None, None)

# try to determine the total number of frames in the video file
try:
    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 file
except:
    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 stream
while 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]

    # 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
                )

    if args["live"]:
        cv2.imshow("Frame", frame)
        key = cv2.waitKey(1) & 0xFF

        # if the `q` key was pressed, break from the loop
        if key == ord("q"):
            break

    if args["output"]:
        # 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 pointers
print("[INFO] cleaning up...")
writer.release()
vs.release()