Create predict.py

predict.py

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+import hydra
+import torch
+import argparse
+import time
+from pathlib import Path
+import math
+import cv2
+import torch
+import torch.backends.cudnn as cudnn
+from numpy import random
+from ultralytics.yolo.engine.predictor import BasePredictor
+from ultralytics.yolo.utils import DEFAULT_CONFIG, ROOT, ops
+from ultralytics.yolo.utils.checks import check_imgsz
+from ultralytics.yolo.utils.plotting import Annotator, colors, save_one_box
+
+import cv2
+from deep_sort_pytorch.utils.parser import get_config
+from deep_sort_pytorch.deep_sort import DeepSort
+from collections import deque
+import numpy as np
+palette = (2 ** 11 - 1, 2 ** 15 - 1, 2 ** 20 - 1)
+cars_deque = {}
+
+
+deepsort = None
+
+object_counter = {}
+
+speed_line_queue = {}
+def estimatespeed(Location1, Location2):
+    #Euclidean Distance Formula
+    d_pixel = math.sqrt(math.pow(Location2[0] - Location1[0], 2) + math.pow(Location2[1] - Location1[1], 2))
+    # defining thr pixels per meter
+    ppm = 8
+    d_meters = d_pixel/ppm
+    time_constant = 15*3.6
+    #distance = speed/time
+    speed = d_meters * time_constant
+
+    return int(speed)
+def init_tracker():
+    global deepsort
+    cfg_deep = get_config()
+    cfg_deep.merge_from_file("deep_sort_pytorch/configs/deep_sort.yaml")
+
+    deepsort= DeepSort(cfg_deep.DEEPSORT.REID_CKPT,
+                            max_dist=cfg_deep.DEEPSORT.MAX_DIST, min_confidence=cfg_deep.DEEPSORT.MIN_CONFIDENCE,
+                            nms_max_overlap=cfg_deep.DEEPSORT.NMS_MAX_OVERLAP, max_iou_distance=cfg_deep.DEEPSORT.MAX_IOU_DISTANCE,
+                            max_age=cfg_deep.DEEPSORT.MAX_AGE, n_init=cfg_deep.DEEPSORT.N_INIT, nn_budget=cfg_deep.DEEPSORT.NN_BUDGET,
+                            use_cuda=True)
+##########################################################################################
+def xyxy_to_xywh(*xyxy):
+    """" Calculates the relative bounding box from absolute pixel values. """
+    bbox_left = min([xyxy[0].item(), xyxy[2].item()])
+    bbox_top = min([xyxy[1].item(), xyxy[3].item()])
+    bbox_w = abs(xyxy[0].item() - xyxy[2].item())
+    bbox_h = abs(xyxy[1].item() - xyxy[3].item())
+    x_c = (bbox_left + bbox_w / 2)
+    y_c = (bbox_top + bbox_h / 2)
+    w = bbox_w
+    h = bbox_h
+    return x_c, y_c, w, h
+
+
+def compute_color_for_labels(label):
+    """
+    Simple function that adds fixed color depending on the class
+    """
+    if label == 0: #person
+        color = (85,45,255)
+    elif label == 2: # Car
+        color = (222,82,175)
+    elif label == 3:  # Motobike
+        color = (0, 204, 255)
+    elif label == 5:  # Bus
+        color = (0, 149, 255)
+    else:
+        color = [int((p * (label ** 2 - label + 1)) % 255) for p in palette]
+    return tuple(color)
+
+def draw_border(img, pt1, pt2, color, thickness, r, d):
+    x1,y1 = pt1
+    x2,y2 = pt2
+    # Top left
+    cv2.line(img, (x1 + r, y1), (x1 + r + d, y1), color, thickness)
+    cv2.line(img, (x1, y1 + r), (x1, y1 + r + d), color, thickness)
+    cv2.ellipse(img, (x1 + r, y1 + r), (r, r), 180, 0, 90, color, thickness)
+    # Top right
+    cv2.line(img, (x2 - r, y1), (x2 - r - d, y1), color, thickness)
+    cv2.line(img, (x2, y1 + r), (x2, y1 + r + d), color, thickness)
+    cv2.ellipse(img, (x2 - r, y1 + r), (r, r), 270, 0, 90, color, thickness)
+    # Bottom left
+    cv2.line(img, (x1 + r, y2), (x1 + r + d, y2), color, thickness)
+    cv2.line(img, (x1, y2 - r), (x1, y2 - r - d), color, thickness)
+    cv2.ellipse(img, (x1 + r, y2 - r), (r, r), 90, 0, 90, color, thickness)
+    # Bottom right
+    cv2.line(img, (x2 - r, y2), (x2 - r - d, y2), color, thickness)
+    cv2.line(img, (x2, y2 - r), (x2, y2 - r - d), color, thickness)
+    cv2.ellipse(img, (x2 - r, y2 - r), (r, r), 0, 0, 90, color, thickness)
+
+    cv2.rectangle(img, (x1 + r, y1), (x2 - r, y2), color, -1, cv2.LINE_AA)
+    cv2.rectangle(img, (x1, y1 + r), (x2, y2 - r - d), color, -1, cv2.LINE_AA)
+    
+    cv2.circle(img, (x1 +r, y1+r), 2, color, 12)
+    cv2.circle(img, (x2 -r, y1+r), 2, color, 12)
+    cv2.circle(img, (x1 +r, y2-r), 2, color, 12)
+    cv2.circle(img, (x2 -r, y2-r), 2, color, 12)
+    
+    return img
+
+def UI_box(x, img, color=None, label=None, line_thickness=None):
+    # Plots one bounding box on image img
+    tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1  # line/font thickness
+    color = color or [random.randint(0, 255) for _ in range(3)]
+    c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
+    cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
+    if label:
+        tf = max(tl - 1, 1)  # font thickness
+        t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
+
+        img = draw_border(img, (c1[0], c1[1] - t_size[1] -3), (c1[0] + t_size[0], c1[1]+3), color, 1, 8, 2)
+
+        cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
+
+
+def ccw(A,B,C):
+    return (C[1]-A[1]) * (B[0]-A[0]) > (B[1]-A[1]) * (C[0]-A[0])
+
+
+def draw_boxes(img, bbox, names,object_id, identities=None, offset=(0, 0)):
+    #cv2.line(img, line[0], line[1], (46,162,112), 3)
+    cv2.putText(img, f'Number of cars: {len(cars_deque)}', (11, 35), 0, 1, [0, 255, 0], thickness=2, lineType=cv2.LINE_AA)
+    height, width, _ = img.shape
+    # remove tracked point from buffer if object is lost
+    for key in list(cars_deque):
+      if key not in identities:
+        cars_deque.pop(key)
+    
+    for i, box in enumerate(bbox):
+        obj_name = names[object_id[i]]
+        if obj_name == 'car':
+            x1, y1, x2, y2 = [int(i) for i in box]
+            x1 += offset[0]
+            x2 += offset[0]
+            y1 += offset[1]
+            y2 += offset[1]
+
+            # code to find center of bottom edge
+            center = (int((x2+x1)/ 2), int((y2+y2)/2))
+
+            # get ID of object
+            id = int(identities[i]) if identities is not None else 0
+
+            # create new buffer for new object
+            if id not in cars_deque:  
+              cars_deque[id] = deque(maxlen= 64)
+              speed_line_queue[id] = []
+            color = compute_color_for_labels(object_id[i])
+        
+        
+            label = '{}{:d}'.format("", id) + ":"+ '%s' % (obj_name)
+        
+
+            # add center to buffer
+            cars_deque[id].appendleft(center)
+            if len(cars_deque[id]) >= 2:
+              object_speed = estimatespeed(cars_deque[id][1], cars_deque[id][0])
+              speed_line_queue[id].append(object_speed)
+              if obj_name not in object_counter:
+                        object_counter[obj_name] = 1
+        
+
+            try:
+                label = label + " " + str(sum(speed_line_queue[id])//len(speed_line_queue[id])) + "km/h"
+            except:
+                pass
+            UI_box(box, img, label=label, color=color, line_thickness=2)
+    
+    
+    return img
+
+
+class DetectionPredictor(BasePredictor):
+
+    def get_annotator(self, img):
+        return Annotator(img, line_width=self.args.line_thickness, example=str(self.model.names))
+
+    def preprocess(self, img):
+        img = torch.from_numpy(img).to(self.model.device)
+        img = img.half() if self.model.fp16 else img.float()  # uint8 to fp16/32
+        img /= 255  # 0 - 255 to 0.0 - 1.0
+        return img
+
+    def postprocess(self, preds, img, orig_img):
+        preds = ops.non_max_suppression(preds,
+                                        self.args.conf,
+                                        self.args.iou,
+                                        agnostic=self.args.agnostic_nms,
+                                        max_det=self.args.max_det)
+
+        for i, pred in enumerate(preds):
+            shape = orig_img[i].shape if self.webcam else orig_img.shape
+            pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], shape).round()
+
+        return preds
+
+    def write_results(self, idx, preds, batch):
+        p, im, im0 = batch
+        all_outputs = []
+        log_string = ""
+        if len(im.shape) == 3:
+            im = im[None]  # expand for batch dim
+        self.seen += 1
+        im0 = im0.copy()
+        if self.webcam:  # batch_size >= 1
+            log_string += f'{idx}: '
+            frame = self.dataset.count
+        else:
+            frame = getattr(self.dataset, 'frame', 0)
+
+        self.data_path = p
+        save_path = str(self.save_dir / p.name)  # im.jpg
+        self.txt_path = str(self.save_dir / 'labels' / p.stem) + ('' if self.dataset.mode == 'image' else f'_{frame}')
+        log_string += '%gx%g ' % im.shape[2:]  # print string
+        self.annotator = self.get_annotator(im0)
+
+        det = preds[idx]
+        all_outputs.append(det)
+        if len(det) == 0:
+            return log_string
+        for c in det[:, 5].unique():
+            n = (det[:, 5] == c).sum()  # detections per class
+            log_string += f"{n} {self.model.names[int(c)]}{'s' * (n > 1)}, "
+        # write
+        gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
+        xywh_bboxs = []
+        confs = []
+        oids = []
+        outputs = []
+        for *xyxy, conf, cls in reversed(det):
+            x_c, y_c, bbox_w, bbox_h = xyxy_to_xywh(*xyxy)
+            xywh_obj = [x_c, y_c, bbox_w, bbox_h]
+            xywh_bboxs.append(xywh_obj)
+            confs.append([conf.item()])
+            oids.append(int(cls))
+        xywhs = torch.Tensor(xywh_bboxs)
+        confss = torch.Tensor(confs)
+          
+        outputs = deepsort.update(xywhs, confss, oids, im0)
+        if len(outputs) > 0:
+            bbox_xyxy = outputs[:, :4]
+            identities = outputs[:, -2]
+            object_id = outputs[:, -1]
+            
+            draw_boxes(im0, bbox_xyxy, self.model.names, object_id,identities)
+
+        return log_string
+
+
+@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
+def predict(cfg):
+    init_tracker()
+    cfg.model = cfg.model or "yolov8n.pt"
+    cfg.imgsz = check_imgsz(cfg.imgsz, min_dim=2)  # check image size
+    cfg.source = cfg.source if cfg.source is not None else ROOT / "assets"
+    predictor = DetectionPredictor(cfg)
+    predictor()
+
+
+if __name__ == "__main__":
+    predict()