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#Find first frame that contains detections
def findFirstFrame(bb_dict):
    """
    Args:
        bb_dict:      (dict) dictionary from json file
    Returns:
        firstFrame:        (int) First frame to process in video
    """
    
    firstFrame =  bb_dict['frames'][0]['frame_index']

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#Determine number of bounding boxes in frame
def count_bboxes(bb_dict,frame_index):
    """
    Args:
        bb_dict:      (dict) dictionary from json file
        frame:        (int) what frame is being processed
    Returns:
        nDetections:        (int) Number of bounding boxes in frame
    """
    

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#Get dictionary from json file                            
def read_json_dict(path2json):
    """
    Args:
        path2json:     (str) path to .MP4 json file containing player bounding boxes
    Returns:
        bb_dict:       (dict) Dictionary containing bounding boxes in each frame
    """
    # Opening JSON file                     
    f = open(path2json)

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def matchJSON2MP4(jsonList, jsonPath, MP4list, MP4Path, whichMP4):
    
    json_strip = [s.replace(directory + jsonPath + '\\', '') for s in jsonList]
    json_strip = [s.replace(".json", '') for s in json_strip]
    
    mp4_strip = [s.replace(directory + MP4Path + '\\', '') for s in MP4list]
    
    mp4Name = mp4_strip[whichMP4]
    index    = json_strip.index(mp4Name)
    

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#Determine number of frames in video
def count_frames(video_file):
    """
    Args:
        video_file:     (str) path to .MP4 video file
    Returns:
        nFrames:        (int) Number of frames in mp4
    """
    cap = cv2.VideoCapture(video_file)
    length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))

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def get_frame(video_file, frame_index):
    """
    Args:
        video_file:     (str) path to .MP4 video file
        frame_index:    (int) query frame index
    Returns:
        frame:          (ndarray, size (y, x, 3)) video frame
                        Uses OpenCV BGR channels
    """

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def getListOfFiles(rPath , fType):
    """
    Args:
        rPath:     (str) path to file
        fType:     (str) type of file to look for (i.e., .mp4, .json, etc.)
    Returns:
        lFiles:    (list) List of files in rPath of type fType
    """ 
    #1. Establish the current working directory
    directory = os.getcwd()

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#Detecting humans with HOG
path2xml = r'C:\Users\vmurc\Documents\GitHub\opencv\data\haarcascades\haarcascade_fullbody.xml'
fbCascade = cv2.CascadeClassifier(path2xml)
# Initializing the HOG person detector
image = cv2.cvtColor(rgb_img, cv2.COLOR_RGB2GRAY)

hog = cv2.HOGDescriptor()
hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
   
# Resizing the Image

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def KMeansTest(img,clusters):
    """
    Args:
        path2img   : (str) path to cropped player bounding box
        clusters   : (int) how many clusters to use for KMEANS
    Returns:
        rgb_array :  (tuple) Dominant colors in image in RGB format
    """
    org_img = img.copy()
    #print('Org image shape --> ',img.shape)

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#Determine optimal k value for clustering using elbow method
distortions = [] #Initialize array with distortions from each clustering run
K = range(1,11) #Explore k values between 1 and 10
#Run the clustering routine 
for k in K:
    #Convert image into a 1D array
    flat_img = np.reshape(rgb_img,(-1,3))
    kmeanModel = KMeans(n_clusters=k)
    kmeanModel.fit(flat_img)
    distortions.append(kmeanModel.inertia_)