inference.py

import project_path

import torch
from tqdm import tqdm
from functools import partial
import numpy as np
import json
import time
from unittest.mock import patch
import math

# assumes yolov5 on sys.path
from lib.yolov5.models.experimental import attempt_load
from lib.yolov5.utils.torch_utils import select_device
from lib.yolov5.utils.general import clip_boxes, scale_boxes, xywh2xyxy
from lib.yolov5.utils.metrics import box_iou
import torch
import torchvision

from InferenceConfig import InferenceConfig, TrackerType
from lib.fish_eye.tracker import Tracker
from lib.fish_eye.bytetrack import Associate


### Configuration options
WEIGHTS = 'models/v5m_896_300best.pt'
# will need to configure these based on GPU hardware
BATCH_SIZE = 32

CONF_THRES = 0.05 # detection
NMS_IOU  = 0.2 # NMS IOU
MAX_AGE = 14 # time until missing fish get's new id
MIN_HITS = 16 # minimum number of frames with a specific fish for it to count
MIN_LENGTH = 0.3 # minimum fish length, in meters
IOU_THRES = 0.01 # IOU threshold for tracking
MIN_TRAVEL = -1 # Minimum distance a track has to travel
###

def norm(bbox, w, h):
    """
    Normalize a bounding box.
    Args:
        bbox: list of length 4. Can be [x,y,w,h] or [x0,y0,x1,y1]
        w: image width
        h: image height
    """
    bb = bbox.copy()
    bb[0] /= w
    bb[1] /= h
    bb[2] /= w
    bb[3] /= h
    return bb

def do_full_inference(dataloader, image_meter_width, image_meter_height, gp=None, config=InferenceConfig()):
    
    # Set up model
    model, device = setup_model(config.weights)

    # Detect boxes in frames
    inference, image_shapes, width, height = do_detection(dataloader, model, device, gp=gp)

    if config.associative_tracker == TrackerType.BYTETRACK:

        # Find low confidence detections
        low_outputs = do_suppression(inference, image_meter_width, width, conf_thres=config.byte_low_conf, iou_thres=config.nms_iou, max_length=config.max_length, gp=gp)
        low_preds, real_width, real_height = format_predictions(image_shapes, low_outputs, width, height, gp=gp)

        # Find high confidence detections
        high_outputs = do_suppression(inference, image_meter_width, width, conf_thres=config.byte_high_conf, iou_thres=config.nms_iou, max_length=config.max_length, gp=gp)
        high_preds, real_width, real_height = format_predictions(image_shapes, high_outputs, width, height, gp=gp)

        # Perform associative tracking (ByteTrack)
        results = do_associative_tracking(
            low_preds, high_preds, image_meter_width, image_meter_height, 
            reverse=False, min_length=config.min_length, min_travel=config.min_travel, 
            max_age=config.max_age, min_hits=config.min_hits, 
            gp=gp)
    else: 

        # Find confident detections
        outputs = do_suppression(inference, image_meter_width, width, conf_thres=config.conf_thresh, iou_thres=config.nms_iou, max_length=config.max_length, gp=gp)

        if config.associative_tracker == TrackerType.CONF_BOOST:

            # Boost confidence based on found confident detections
            do_confidence_boost(inference, outputs, boost_power=config.boost_power, boost_decay=config.boost_decay, gp=gp)

            # Find confident detections from boosted list
            outputs = do_suppression(inference, image_meter_width, width, conf_thres=config.conf_thresh, iou_thres=config.nms_iou, max_length=config.max_length, gp=gp)

        # Format confident detections
        all_preds, real_width, real_height = format_predictions(image_shapes, outputs, width, height, gp=gp)

        # Perform SORT tracking
        results = do_tracking(
            all_preds, image_meter_width, image_meter_height, 
            min_length=config.min_length, min_travel=config.min_travel, 
            max_age=config.max_age, min_hits=config.min_hits, 
            gp=gp)
 
    return results
    




def setup_model(weights_fp=WEIGHTS, imgsz=896, batch_size=32):
    if torch.cuda.is_available():
        device = select_device('0', batch_size=batch_size)
    else:
        print("CUDA not available. Using CPU inference.")
        device = select_device('cpu', batch_size=batch_size)
    
    # Setup model for inference
    model = attempt_load(weights_fp, device=device)
    half = device.type != 'cpu'  # half precision only supported on CUDA
    if half:
        model.half()
    model.eval()
    
    # Create dataloader for batched inference
    img = torch.zeros((1, 3, imgsz, imgsz), device=device)
    _ = model(img.half() if half else img) if device.type != 'cpu' else None  # run once
    
    return model, device
                       
def do_detection(dataloader, model, device, gp=None, batch_size=BATCH_SIZE, verbose=True):
    """
    Args:
        frames_dir: a directory containing frames to be evaluated
        image_meter_width: the width of each image, in meters (used for fish length calculation)
        gp: a callback function which takes as input 1 parameter, (int) percent complete
        prep_for_marking: re-index fish for manual marking output
    """

    if (gp): gp(0, "Detection...")

    inference = []
    image_shapes = []
    # Run detection
    with tqdm(total=len(dataloader)*batch_size, desc="Running detection", ncols=0, disable=not verbose) as pbar:
        for batch_i, (img, _, shapes) in enumerate(dataloader):

            if gp: gp(batch_i / len(dataloader), pbar.__str__())
            img = img.to(device, non_blocking=True)
            img = img.half() if device.type != 'cpu' else img.float()  # uint8 to fp16/32
            img /= 255.0  # 0 - 255 to 0.0 - 1.0
            size = tuple(img.shape)
            nb, _, height, width = size  # batch size, channels, height, width


            
            # Run model & NMS
            with torch.no_grad():
                inf_out, _ = model(img, augment=False) 



            # Save shapes for resizing to original shape
            batch_shape = []
            for si, pred in enumerate(inf_out):
                batch_shape.append((img[si].shape[1:], shapes[si]))
            image_shapes.append(batch_shape)
            
            inference.append(inf_out)
            pbar.update(1*batch_size)

    return inference, image_shapes, width, height

def do_suppression(inference, image_meter_width, image_pixel_width, gp=None, batch_size=BATCH_SIZE, conf_thres=CONF_THRES, iou_thres=NMS_IOU, max_length=1.5, verbose=True):
    """
    Args:
        frames_dir: a directory containing frames to be evaluated
        image_meter_width: the width of each image, in meters (used for fish length calculation)
        gp: a callback function which takes as input 1 parameter, (int) percent complete
        prep_for_marking: re-index fish for manual marking output
    """
    
    if (gp): gp(0, "Suppression...")
    # keep predictions to feed them ordered into the Tracker
    # TODO: how to deal with large files?
    outputs = []
    with tqdm(total=len(inference)*batch_size, desc="Running suppression", ncols=0, disable=not verbose) as pbar:
        for batch_i, inf_out in enumerate(inference):

            if gp: gp(batch_i / len(inference), pbar.__str__())

            with torch.no_grad():
                output = non_max_suppression(inf_out, image_meter_width, image_pixel_width, conf_thres=conf_thres, iou_thres=iou_thres, max_length=max_length)


            outputs.append(output)

            pbar.update(1*batch_size)
         
    return outputs

def format_predictions(image_shapes, outputs, width, height, gp=None, batch_size=BATCH_SIZE, verbose=True):
    """
    Args:
        frames_dir: a directory containing frames to be evaluated
        image_meter_width: the width of each image, in meters (used for fish length calculation)
        gp: a callback function which takes as input 1 parameter, (int) percent complete
        prep_for_marking: re-index fish for manual marking output
    """
    
    if (gp): gp(0, "Formatting...")
    # keep predictions to feed them ordered into the Tracker
    # TODO: how to deal with large files?
    all_preds = {}
    with tqdm(total=len(image_shapes)*batch_size, desc="Running formatting", ncols=0, disable=not verbose) as pbar:
        for batch_i, batch in enumerate(outputs):

            if gp: gp(batch_i / len(image_shapes), pbar.__str__())

            batch_shapes = image_shapes[batch_i]

            # Format results
            for si, pred in enumerate(batch):
                (image_shape, original_shape) = batch_shapes[si]
                # Clip boxes to image bounds and resize to input shape
                clip_boxes(pred, (height, width))
                box = pred[:, :4].clone()  # xyxy
                confs = pred[:, 4].clone().tolist()
                scale_boxes(image_shape, box, original_shape[0], original_shape[1])  # to original shape
                
                # get boxes into tracker input format - normalized xyxy with confidence score
                # confidence score currently not used by tracker; set to 1.0
                boxes = None
                if box.shape[0]:
                    real_width = original_shape[0][1]
                    real_height = original_shape[0][0]
                    do_norm = partial(norm, w=original_shape[0][1], h=original_shape[0][0])
                    normed = list((map(do_norm, box[:, :4].tolist())))
                    boxes = np.stack([ [*bb, conf] for bb, conf in zip(normed, confs) ])
                frame_num = (batch_i, si)
                all_preds[frame_num] = boxes

            pbar.update(1*batch_size)
         
    return all_preds, real_width, real_height


# ---------------------------------------- TRACKING ------------------------------------------

def do_tracking(all_preds, image_meter_width, image_meter_height, gp=None, max_age=MAX_AGE, iou_thres=IOU_THRES, min_hits=MIN_HITS, min_length=MIN_LENGTH, min_travel=MIN_TRAVEL, verbose=True): 
    """
    Perform SORT tracking based on formatted detections
    """

    if (gp): gp(0, "Tracking...")

    # Initialize tracker
    clip_info = {
        'start_frame': 0,
        'end_frame': len(all_preds),
        'image_meter_width': image_meter_width,
        'image_meter_height': image_meter_height
    }
    tracker = Tracker(clip_info, args={ 'max_age': max_age, 'min_hits': 0, 'iou_threshold': iou_thres}, min_hits=min_hits)
    
    # Run tracking
    with tqdm(total=len(all_preds), desc="Running tracking", ncols=0, disable=not verbose) as pbar:
        for i, key in enumerate(sorted(all_preds.keys())):
            if gp: gp(i / len(all_preds), pbar.__str__())
            boxes = all_preds[key]
            if boxes is not None:
                tracker.update(boxes)
            else:
                tracker.update()
            pbar.update(1)

    json_data = tracker.finalize(min_length=min_length, min_travel=min_travel)

    return json_data

def do_confidence_boost(inference, safe_preds, gp=None, batch_size=BATCH_SIZE, boost_power=1, boost_decay=1, verbose=True):
    """
    Takes in the full YOLO detections 'inference' and formatted non-max suppressed detections 'safe_preds'
    and boosts the confidence of detections around identified fish that are close in space in neighbouring frames. 
    """
    
    if (gp): gp(0, "Confidence Boost...")
    # keep predictions to feed them ordered into the Tracker
    # TODO: how to deal with large files?


    boost_cutoff = 0.01
    boost_range = math.floor(math.sqrt(1/boost_decay * math.log(boost_power / boost_cutoff)))
    boost_scale = boost_power * math.exp(-boost_decay)
    
    with tqdm(total=len(inference), desc="Running confidence boost", ncols=0, disable=not verbose) as pbar:
        for batch_i in range(len(inference)):

            if gp: gp(batch_i / len(inference), pbar.__str__())

            safe = safe_preds[batch_i]
            infer = inference[batch_i]

            for i in range(len(safe)):
                safe_frame = safe[i]
                if len(safe_frame) == 0:
                    continue

                next_batch = inference[batch_i + 1] if batch_i+1 < len(inference) else None
                prev_batch = inference[batch_i - 1] if batch_i-1 >= 0 else None

                
                for dt in range(-boost_range, boost_range+1):
                    if dt == 0: continue
                    idx = i+dt 
                    temp_frame = None
                    if idx >= 0 and idx < len(infer):
                        temp_frame = infer[idx]
                    elif idx < 0 and prev_batch is not None and -idx >= len(prev_batch):
                        temp_frame = prev_batch[idx]
                    elif idx >= len(infer) and next_batch is not None and idx - len(infer) < len(next_batch):
                        temp_frame = next_batch[idx - len(infer)]
                    

                    if temp_frame is not None:
                        boost_frame(safe_frame, temp_frame, dt, power=boost_scale, decay=boost_decay)

            pbar.update(1*batch_size)

def boost_frame(safe_frame, base_frame, dt, power=1, decay=1):
    """
    Boosts confidence of base_frame based on confidence in safe_frame, iou, and the time difference between frames.
    """
    safe_boxes = safe_frame[:, :4]
    boxes = xywh2xyxy(base_frame[:, :4])  # center_x, center_y, width, height) to (x1, y1, x2, y2)≈

    # If running on CPU, you have to convert to double for the .prod() function in box_iou for some reason?
    if torch.cuda.is_available():
        ious = box_iou(boxes, safe_boxes)
    else: 
        ious = box_iou(boxes.double(), safe_boxes).float()
    
    score = torch.matmul(ious, safe_frame[:, 4])
    # score = iou(safe_box, base_box) * confidence(safe_box)
    base_frame[:, 4] *= 1 + power*(score)*math.exp(-decay*(dt*dt-1))
    return base_frame

# ByteTrack
def do_associative_tracking(low_preds, high_preds, image_meter_width, image_meter_height, reverse=False, gp=None, max_age=MAX_AGE, iou_thres=IOU_THRES, min_hits=MIN_HITS, min_length=MIN_LENGTH, min_travel=MIN_TRAVEL, verbose=True):

    if (gp): gp(0, "Tracking...")


    # Initialize tracker
    clip_info = {
        'start_frame': 0,
        'end_frame': len(low_preds),
        'image_meter_width': image_meter_width,
        'image_meter_height': image_meter_height
    }
    print("Tracking using Associate")
    tracker = Tracker(clip_info, algorithm=Associate, reverse=reverse, args={ 'max_age': max_age, 'min_hits': 0, 'iou_threshold': iou_thres}, min_hits=min_hits)
    
    # Run tracking
    with tqdm(total=len(low_preds), desc="Running tracking", ncols=0, disable=not verbose) as pbar:
        for i, key in enumerate(sorted(low_preds.keys(), reverse=reverse)):
            if gp: gp(i / len(low_preds), pbar.__str__())
            low_boxes = low_preds[key]
            high_boxes = high_preds[key]
            boxes = (low_boxes, high_boxes)
            if low_boxes is not None and high_boxes is not None:
                tracker.update(boxes)
            else:
                tracker.update((np.empty((0, 5)), np.empty((0, 5))))
            pbar.update(1)

    json_data = tracker.finalize(min_length=min_length, min_travel=min_travel)

    return json_data



@patch('json.encoder.c_make_encoder', None)
def json_dump_round_float(some_object, out_path, num_digits=4):
    """Write a json file to disk with a specified level of precision.
    See: https://gist.github.com/Sukonnik-Illia/ed9b2bec1821cad437d1b8adb17406a3
    """
    # saving original method
    of = json.encoder._make_iterencode
    def inner(*args, **kwargs):
        args = list(args)
        # fifth argument is float formater which will we replace
        fmt_str = '{:.' + str(num_digits) + 'f}'
        args[4] = lambda o: fmt_str.format(o)
        return of(*args, **kwargs)
    
    with patch('json.encoder._make_iterencode', wraps=inner):
        return json.dump(some_object, open(out_path, 'w'), indent=2)
    

def filter_detection_size(inference, image_meter_width, width, max_length):

    outputs = []
    for batch in inference:
        print("batch")
        print(type(batch))
        print(batch.shape)

        pix2width = image_meter_width/width
        width = batch[..., 2]*pix2width
        wc = width < max_length
        print("wc")
        print(type(wc))
        print(wc.shape)
        bs = batch.shape[0]  # batches

        output = torch.zeros((bs, 0, 6), device=batch.device)
        print("wc")
        print(batch.shape)
        for xi, x in enumerate(batch):
            x = x[wc[xi]]  # confidence
            print(x.shape)
            output[xi, :, :] = x

        output = torch.tensor(output)
        print("output len", output.shape)
        outputs.append(output)
         
    print(len(outputs))
    return outputs

def non_max_suppression(
        prediction,
        image_meter_width,
        image_pixel_width,
        max_length=1.5,
        conf_thres=0.25,
        iou_thres=0.45,
        max_det=300
):
    """Non-Maximum Suppression (NMS) on inference results to reject overlapping detections

    NOTE: SIMPLIFIED FOR SINGLE CLASS DETECTION

    Returns:
         list of detections, on (n,6) tensor per image [xyxy, conf, cls]
    """

    # Checks
    assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0'
    assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0'
    if isinstance(prediction, (list, tuple)):  # YOLOv5 model in validation model, output = (inference_out, loss_out)
        prediction = prediction[0]  # select only inference output

    device = prediction.device
    mps = 'mps' in device.type  # Apple MPS
    if mps:  # MPS not fully supported yet, convert tensors to CPU before NMS
        prediction = prediction.cpu()
    bs = prediction.shape[0]  # batch size
    xc = prediction[..., 4] > conf_thres  # candidates

    # width filter
    pix2width = image_meter_width/image_pixel_width
    width = prediction[..., 2]*pix2width
    if max_length > 0:
        wc = width < max_length
    else:
        # If max_length is 0, ignore
        wc = width > max_length


    # Settings
    # min_wh = 2  # (pixels) minimum box width and height
    max_nms = 30000  # maximum number of boxes into torchvision.ops.nms()
    redundant = True  # require redundant detections
    merge = False  # use merge-NMS

    output = [torch.zeros((0, 6), device=prediction.device)] * bs
    for xi, x in enumerate(prediction):  # image index, image inference

            
        # Keep boxes that pass confidence threshold
        x = x[xc[xi] * wc[xi]]  # confidence

        # If none remain process next image
        if not x.shape[0]:
            continue
            
        # Compute conf
        x[:, 5:] *= x[:, 4:5]  # conf = obj_conf * cls_conf


        # Box/Mask
        box = xywh2xyxy(x[:, :4])  # center_x, center_y, width, height) to (x1, y1, x2, y2)
        mask = x[:, 6:]  # zero columns if no masks

        # Detections matrix nx6 (xyxy, conf, cls)
        conf, j = x[:, 5:6].max(1, keepdim=True)
        x = torch.cat((box, conf, j.float(), mask), 1)[conf.view(-1) > conf_thres]


        # Check shape
        n = x.shape[0]  # number of boxes
        if not n:  # no boxes
            continue
        x = x[x[:, 4].argsort(descending=True)[:max_nms]]  # sort by confidence and remove excess boxes

        # Batched NMS
        boxes  = x[:, :4]  # boxes (offset by class), scores
        scores = x[:, 4]
        i = torchvision.ops.nms(boxes, scores, iou_thres)  # NMS

        i = i[:max_det]  # limit detections
        if merge and (1 < n < 3E3):  # Merge NMS (boxes merged using weighted mean)
            # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
            iou = box_iou(boxes[i], boxes) > iou_thres  # iou matrix
            weights = iou * scores[None]  # box weights
            x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True)  # merged boxes
            if redundant:
                i = i[iou.sum(1) > 1]  # require redundancy

        output[xi] = x[i]
        if mps:
            output[xi] = output[xi].to(device)
        
        logging = False

    return output