get player jersey colors for whole video

getJerseyColorsFromMP4.py

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
    bb_dict = read_json_dict(jsonList[jval])#This is for first video in the LCamera folder

    #Find first frame with detections
    firstFrame = findFirstFrame(bb_dict)

    #Determine frame spacing
    frameSpacing = findFrameSpacing(bb_dict)
    
    #Which frame to look at
    whichFrame =  0
    whichFrameAdj = firstFrame + whichFrame*frameSpacing #Adjust for video data to match json detection
    nf = int(count_frames(mp4List[whichVideo])/10) #Number of frames in video
    print('Initializing arrays...')
    #Initialize arrays 
    dom_color1, dom_color2,dom_color3 = [],[],[]
    frame_list,bb_list,video_list = [],[],[]
    #Insert loop here for frames
    print('Starting jersey color detection ...')    
    while whichFrameAdj < nf:
        for i in tqdm(range(nf), desc="Processing Frame"):#Add progress bar for frames processed            
            #Get a frame from video
            frame = get_frame(mp4List[whichVideo], whichFrameAdj)
            #Convert color from BGR to RGB
            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
            #Make a copy of the frame to store for display of all the bounding boxes
            frame_copy = frame.copy()
            #Determine number of bounding boxes in current frame
            n_bbs = count_bboxes(bb_dict,whichFrame)
            #Get BB coordinates for current frame
            bbs_frame = get_bb4frame(bb_dict,whichFrame) #BB coordinates for current frame
            #Loop over bounding boxes in current frame
            for bb in range(n_bbs):
                #print('****Frame ' + str(whichFrameAdj) + ' BB ' + str(bb) + '****')
                frame_list.append(whichFrameAdj) #Append frame ID to list
                bb_list.append(bb)
                video_list.append(whichVideo)

                x1 ,y1 ,x2 ,y2 = makeRectangleFromJSON(bbs_frame,bb) #Coordinates for current BB
                currentbox = frame[y1:y2,x1:x2]
                cv2.rectangle(frame_copy, (x1, y1),  (x2, y2), (0, 0, 255), 2)
                #Crop the bounding box
                croped_bb = crop_image(currentbox,howMuch)
                #Do the clustering
                rgb_array = KMeansMaskGreen(croped_bb, nClusters,
                                            lowHue, highHue, lowSat, highSat, loBright, hiBright)
                #Append dominant RGB colors into respective arrays
                dom_color1.append(rgb_array[0])
                dom_color2.append(rgb_array[1])
                dom_color3.append(rgb_array[2])
            
            whichFrame += 1
            whichFrameAdj = firstFrame + whichFrame*frameSpacing #Adjust for video data to match json
    
    print('Making pandas dataframe containing results...')
    jerseyColor_df = pd.DataFrame({'Video ID': video_list,
                                   'Frame ID': frame_list,
                                   'BB in Frame': bb_list,
                                   'Jersey Color 1': dom_color1,
                                   'Jersey Color 2': dom_color2,
                                   'Jersey Color 3': dom_color3})
    print('PROCESS COMPLETED')
    return jerseyColor_df