classifierWebcam2.py

#RUNS ON WEBCAM BUT WITH STRUCTURE MORE SIMILAR TO DEPTH VERSION:

# REQUIRED LIBRARIES:
from ctypes import *
import math
import random
import cv2 as cv
import numpy as np
from random import randint
import pyrealsense2 as rs

# SUPPORTING STRUCTS:
#Bounding box
class BOX(Structure):
    _fields_ = [("x", c_float),
                ("y", c_float),
                ("w", c_float),
                ("h", c_float)]

#Input image
class IMAGE(Structure):
    _fields_ = [("w", c_int),
                ("h", c_int),
                ("c", c_int),
                ("data", POINTER(c_float))]

#Detection params
class DETECTION(Structure):
    _fields_ = [("bbox", BOX),
                ("classes", c_int),
                ("prob", POINTER(c_float)),
                ("mask", POINTER(c_float)),
                ("objectness", c_float),
                ("sort_class", c_int)]

#Frame metadata
class METADATA(Structure):
    _fields_ = [("classes", c_int),
                ("names", POINTER(c_char_p))]

class IplROI(Structure):
    pass

class IplTileInfo(Structure):
    pass

class IplImage(Structure):
    pass

IplImage._fields_ = [
    ('nSize', c_int),
    ('ID', c_int),
    ('nChannels', c_int),               
    ('alphaChannel', c_int),
    ('depth', c_int),
    ('colorModel', c_char * 4),
    ('channelSeq', c_char * 4),
    ('dataOrder', c_int),
    ('origin', c_int),
    ('align', c_int),
    ('width', c_int),
    ('height', c_int),
    ('roi', POINTER(IplROI)),
    ('maskROI', POINTER(IplImage)),
    ('imageId', c_void_p),
    ('tileInfo', POINTER(IplTileInfo)),
    ('imageSize', c_int),          
    ('imageData', c_char_p),
    ('widthStep', c_int),
    ('BorderMode', c_int * 4),
    ('BorderConst', c_int * 4),
    ('imageDataOrigin', c_char_p)]


class iplimage_t(Structure):
    _fields_ = [('ob_refcnt', c_ssize_t),
                ('ob_type',  py_object),
                ('a', POINTER(IplImage)),
                ('data', py_object),
                ('offset', c_size_t)]

# SYSTEM SETUP:
#lib = CDLL("/home/pjreddie/documents/darknet/libdarknet.so", RTLD_GLOBAL)
lib = CDLL("./models/libdarknet.so", RTLD_GLOBAL)
lib.network_width.argtypes = [c_void_p]
lib.network_width.restype = c_int
lib.network_height.argtypes = [c_void_p]
lib.network_height.restype = c_int

predict = lib.network_predict
predict.argtypes = [c_void_p, POINTER(c_float)]
predict.restype = POINTER(c_float)

set_gpu = lib.cuda_set_device
set_gpu.argtypes = [c_int]

make_image = lib.make_image
make_image.argtypes = [c_int, c_int, c_int]
make_image.restype = IMAGE

get_network_boxes = lib.get_network_boxes
get_network_boxes.argtypes = [c_void_p, c_int, c_int, c_float, c_float, POINTER(c_int), c_int, POINTER(c_int)]
get_network_boxes.restype = POINTER(DETECTION)

make_network_boxes = lib.make_network_boxes
make_network_boxes.argtypes = [c_void_p]
make_network_boxes.restype = POINTER(DETECTION)

free_detections = lib.free_detections
free_detections.argtypes = [POINTER(DETECTION), c_int]

free_ptrs = lib.free_ptrs
free_ptrs.argtypes = [POINTER(c_void_p), c_int]

network_predict = lib.network_predict
network_predict.argtypes = [c_void_p, POINTER(c_float)]

reset_rnn = lib.reset_rnn
reset_rnn.argtypes = [c_void_p]

load_net = lib.load_network
load_net.argtypes = [c_char_p, c_char_p, c_int]
load_net.restype = c_void_p

do_nms_obj = lib.do_nms_obj
do_nms_obj.argtypes = [POINTER(DETECTION), c_int, c_int, c_float]

do_nms_sort = lib.do_nms_sort
do_nms_sort.argtypes = [POINTER(DETECTION), c_int, c_int, c_float]

free_image = lib.free_image
free_image.argtypes = [IMAGE]

letterbox_image = lib.letterbox_image
letterbox_image.argtypes = [IMAGE, c_int, c_int]
letterbox_image.restype = IMAGE

load_meta = lib.get_metadata
lib.get_metadata.argtypes = [c_char_p]
lib.get_metadata.restype = METADATA

load_image = lib.load_image_color
load_image.argtypes = [c_char_p, c_int, c_int]
load_image.restype = IMAGE

rgbgr_image = lib.rgbgr_image
rgbgr_image.argtypes = [IMAGE]

predict_image = lib.network_predict_image
predict_image.argtypes = [c_void_p, IMAGE]
predict_image.restype = POINTER(c_float)

#SUPPORTING FUNCTIONS:
def sample(probs):
    s = sum(probs)
    probs = [a/s for a in probs]
    r = random.uniform(0, 1)
    for i in range(len(probs)):
        r = r - probs[i]
        if r <= 0:
            return i
    return len(probs)-1

def c_array(ctype, values):
    arr = (ctype*len(values))()
    arr[:] = values
    return arr

def classify(net, meta, im):
    out = predict_image(net, im)
    res = []
    for i in range(meta.classes):
        res.append((meta.names[i], out[i]))
    res = sorted(res, key=lambda x: -x[1])
    return res

#Converts a input frame to a array of pixels:
def array_to_image(arr):
    # need to return old values to avoid python freeing memory
    arr = arr.transpose(2,0,1)
    c, h, w = arr.shape[0:3]
    arr = np.ascontiguousarray(arr.flat, dtype=np.float32) / 255.0
    data = arr.ctypes.data_as(POINTER(c_float))
    im = IMAGE(w,h,c,data)
    return im, arr

def predictFrames(frame, net, meta, guiShow, thresh=.8, hier_thresh=.5, nms=.45):
    classes_box_colors = [(0, 0, 255), (0, 255, 0)] 
    classes_font_colors = [(255, 255, 0), (0, 255, 255)]

    rgb_frame = cv.cvtColor(frame, cv.COLOR_BGR2RGB)
    
    #Convert the input frame to a array of pixels
    im, arr = array_to_image(rgb_frame) 
    
    num = c_int(0)
    pnum = pointer(num)
    predict_image(net, im)
    dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, None, 0, pnum)
    num = pnum[0]

    #Store the x, y's, and labels
    x = []
    y = []
    labels = []

    if (nms): do_nms_obj(dets, num, meta.classes, nms);

    for j in range(num):
        for i in range(meta.classes):
            if dets[j].prob[i] > 0: #Detection threshold
                #print (i)
                b = dets[j].bbox
                x.append(b.x)
                y.append(b.y)
                labels.append(meta.names[i].decode('utf-8'))

                #Calculate corners of bounding box
                x1 = int(b.x - b.w / 2.)
                y1 = int(b.y - b.h / 2.)
                x2 = int(b.x + b.w / 2.)
                y2 = int(b.y + b.h / 2.)

                #Draw bounding box on image and add label
                cv.rectangle(frame, (x1, y1), (x2, y2), classes_box_colors[0], 2)
                cv.putText(frame, meta.names[i].decode('utf-8'), (x1, y1 - 20), 1, 1, classes_font_colors[0], 2, cv.LINE_AA)
                        
    cv.imshow('output', frame)
    if cv.waitKey(1) & 0xFF == ord('q'):
        return  

    return x,y,labels,frame      

# Get the target depth
def getTarget(frame, net, meta, guiShow):
    #Network will make predictions on each frame
    x,y,labels,frame = predictFrames(frame, net, meta, guiShow) 
    
    #Calculate angle between camera origin and the centroid of bounding box
    theta = []
    for i in range(len(x)):
        theta.append(x[i] /600*87-87.0/2) 
        if (theta[i] < 0): theta[i] += 360
        if (theta[i] > 180): theta[i] = theta[i] - 360

    #Display aruco tracking
    # if (guiShow == 1):
    #     cv.imshow('OBJECTS DETECTED',frame)
    #     cv.waitKey(1)
    #     if cv.waitKey(1) == ord('q'):
    #         return       

    detections = [x,y,theta,labels]
    return(detections)

if __name__ == "__main__":
    #net = load_net("yolov2-tiny.cfg", "yolov2-tiny.weights", 0)
    #meta = load_meta("voc.data")
    # net = load_net("cfg/yolov3.cfg", "yolov3.weights", 0)
    # meta = load_meta("cfg/coco.data")
    guiShow = 1 #Set to 0 to turn off GUI's

    #Load YOLOv3:
    net = load_net("./models/cfg/yolov3.cfg".encode('utf-8'), "./models/weights/yolov3.weights".encode('utf-8'), 0)
    meta = load_meta("./models/cfg/coco.data".encode('utf-8'))
    
    #Open camera stream:
    vid_source = 0
    video = cv.VideoCapture(vid_source)

    while video.isOpened():   
        # Capture a frame
        ret, frame = video.read()

        if ret:
            #Localize detected objects:
            #x,y,depths,labels,frame = predictFrames(frame, net, meta, guiShow) #Return contains array of [x,y,depth,theta,label] for each detected object
            detections = getTarget(frame, net, meta, guiShow) #Return contains array of [x,y,depth,theta,label] for each detected object
            print(detections)

            cv.imshow('Test', frame)
            if cv.waitKey(1) & 0xFF == ord('q'):
                break