victormurcia

#Get the image from the current working directory
img_name = glob.glob('*.jpg')[0]
#Load the image into my working environment
img = cv2.imread(img_name)
#Show the image
plt.imshow(img)

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#Make image bigger
def makeBigger(img):
 
    dim = (300, 200) #(width, height)
    # resize image
    resized = cv2.resize(img, dim, interpolation = cv2.INTER_AREA)
    return resized

# empty function called when trackbar moves
def emptyFunction():

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def getJerseyColorsFromGame(jsonPath,MP4Path,whichVideo,nClusters):
    mp4_list = getListOfFiles(MP4Path , ".mp4")
    n_mp4 = len(mp4_list)
    df_list = []
    for vid in range(n_mp4):
        jerseyColor_df = getJerseyColorsFromMP4(jsonPath,MP4Path,vid,nClusters)
        df_list.append(jerseyColor_df)
        
    allJerseyColors = pd.concat(df_list)
    return allJerseyColors

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def getJerseyColorsFromMP4(jsonPath,MP4Path,whichVideo,nClusters):
    #Make the list of mp4 and json files from each camera
    print('Retrieving MP4 and JSON files...')
    mp4List  = getListOfFiles(MP4Path  , ".mp4")
    jsonList = getListOfFiles(jsonPath , ".json")
    
    #Find the json file to use for the current video
    jval = matchJSON2MP4(jsonList, jsonPath, mp4List, MP4Path, whichVideo)

    #Get json dictionary of all bounding boxes in video

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def crop_image(image,howMuch):
    """
    Args:
        img        : (array) image of player bounding box
        howMuch    : (int) percent of image to crop (between 0 and 100)
    Returns:
        cropped_img :   (array) cropped image
    """
    val = howMuch/100
    cropped_img = image[0:int(image.shape[0]*val),0:int(image.shape[0])]

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def KMeansMaskGreen(img, clusters, lowHue, highHue, lowSat, highSat, loBright, hiBright):
    """
    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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def KMeansImage(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()
    #Convert image to HSV

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#Extract bounding box coordinates for a specific bounding box in current frame from json
def makeRectangleFromJSON(bb_dict,whichBB):
    """
    Args:
        bb_dict:      (dict) dictionary from json file
        whichBB:        (int) what bounding box is being processed
    Returns:
        x1 ,y1 ,x2 ,y2:    (tuple) tuple containing pixel coordinates for the upper-left and lower-right corners of the bounding box
    """
    x1 ,y1 ,x2 ,y2 = bb_dict[whichBB][0],bb_dict[whichBB][1],bb_dict[whichBB][2],bb_dict[whichBB][3]

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#Extract bounding boxes for a given frame from json
def get_bb4frame(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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#Find first frame that contains detections
def findFrameSpacing(bb_dict):
    """
    Args:
        bb_dict:      (dict) dictionary from json file
    Returns:
        spacing:      (int) Spacing between frames in json
    """
    
    frame0 =  bb_dict['frames'][0]['frame_index']