first push

detect.py

@@ -0,0 +1,9 @@
+import cv2 as cv
+import numpy as n
+
+cap=cv.VideoCapture(0)
+
+while True:
+    success,img=cap.read()
+    cv.imshow("Video",img)
+cv2.waitKey(1)

dummy.py

@@ -0,0 +1,165 @@
+import numpy as np
+from ultralytics import YOLO
+import cv2
+import cvzone
+import math
+from sort import *
+
+
+
+# MongoDB connection URI with a default database (replace with your actual values)
+
+# def connectMongo() -> pymongo.database.Database:
+#     try:
+      
+
+#         try:
+            
+#             client = pymongo.MongoClient('mongodb+srv://INFINIX:INFINIX@cluster0.rubyoda.mongodb.net/?retryWrites=true&w=majority')
+#             db = client["INFINIX"]
+#             print(db)
+#             if db != None:
+#                 print("connected to db")
+#                 return db 
+#         except Exception as e:
+#             print(e)
+
+    
+#     except Exception as e:
+#         return "Error in Connecting to MongoDB" + str(e)
+    
+# db=connectMongo()
+
+# collection=db['BUS_DETS']
+
+# Create a collection to store entry count
+# entry_count_collection = db.entry_count
+
+cap = cv2.VideoCapture('TrialFootage.mp4')
+
+model = YOLO("../Yolo-Weights/yolov8n.pt")
+occupancy = 0
+coming = 0
+goin = 0
+ListPeople = []
+dict = {}
+width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+print(f"Video Resolution: {width}x{height}")
+classNames = ["person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train", "truck", "boat",
+              "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat",
+              "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella",
+              "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat",
+              "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup",
+              "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli",
+              "carrot", "hot dog", "pizza", "donut", "cake", "chair", "sofa", "pottedplant", "bed",
+              "diningtable", "toilet", "tvmonitor", "laptop", "mouse", "remote", "keyboard", "cell phone",
+              "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors",
+              "teddy bear", "hair drier", "toothbrush"
+              ]
+
+# Tracking
+
+tracker = Sort(max_age=20, min_hits=3, iou_threshold=0.3)
+
+yelloLine = [270, 0, 270, 600]
+
+RedLine = [173, 0, 173, 600]
+
+totalCountUp = []
+mask=cv2.imread('mask.jpg')
+
+while True:
+    
+
+    success, img = cap.read()
+    # imgRegion=cv2.bitwise_and(img,mask)
+
+    results = model(img, stream=True)
+
+    detections = np.empty((0, 5))
+
+    for r in results:
+        boxes = r.boxes
+        for box in boxes:
+            # Bounding Box
+            x1, y1, x2, y2 = box.xyxy[0]
+            x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
+            # cv2.rectangle(img,(x1,y1),(x2,y2),(255,0,255),3)
+            w, h = x2 - x1, y2 - y1
+
+            # Confidence
+            conf = math.ceil((box.conf[0] * 100)) / 100
+            # Class Name
+            cls = int(box.cls[0])
+            currentClass = classNames[cls]
+
+            if currentClass == "person" and conf > 0.3:
+                # cvzone.putTextRect(img, f'{currentClass} {conf}', (max(0, x1), max(35, y1)),
+                #                    scale=0.6, thickness=1, offset=3)
+                # cvzone.cornerRect(img, (x1, y1, w, h), l=9, rt=5)
+                currentArray = np.array([x1, y1, x2, y2, conf])
+                detections = np.vstack((detections, currentArray))
+
+    resultsTracker = tracker.update(detections)
+
+    
+    for result in resultsTracker:
+        x1, y1, x2, y2, id = result
+        x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
+        print(result)
+        w, h = x2 - x1, y2 - y1
+        cvzone.cornerRect(img, (x1, y1, w, h), l=9, rt=2, colorR=(255, 0, 255))
+        cvzone.putTextRect(img, f' {int(id)}', (max(0, x1), max(35, y1)),
+                           scale=2, thickness=3, offset=10)
+
+        cx, cy = x1 + w // 2, y1 + h // 2
+        cv2.circle(img, (cx, cy), 5, (255, 0, 255), cv2.FILLED)
+
+        if yelloLine[0] - 20 < cx < yelloLine[2] + 20:
+            if totalCountUp.count(id) == 0:
+                totalCountUp.append(id)
+                dict[id] = [False]
+                cv2.line(img, (yelloLine[0], yelloLine[1]), (yelloLine[2], yelloLine[3]), (0, 0, 255), 5)
+            elif totalCountUp.count(id) == 1:
+                if (dict[id].count(False) < 1):
+                    dict[id].append(False)
+                cv2.line(img, (yelloLine[0], yelloLine[1]), (yelloLine[2], yelloLine[3]), (0, 0, 255), 5)
+        if RedLine[0] - 20 < cx < RedLine[2] + 30:
+            if totalCountUp.count(id) == 0:
+                totalCountUp.append(id)
+                dict[id] = [True]
+                cv2.line(img, (RedLine[0], RedLine[1]), (RedLine[2], RedLine[3]), (0, 255, 200), 5)
+            elif totalCountUp.count(id) == 1:
+                if (dict[id].count(True) < 1):
+                    dict[id].append(True)
+                    cv2.line(img, (RedLine[0], RedLine[1]), (RedLine[2], RedLine[3]), (0, 255, 200), 5)
+
+    print(totalCountUp)
+    entry_count = 0
+
+    for i in dict.values():
+        if (len(i) == 2):
+            if i[0] == True and i[1] == False:
+                if entry_count > 0:
+                    entry_count -= 1
+            if i[0] == False and i[1] == True:
+                entry_count += 1
+        
+    
+    print('count is ', entry_count)
+    print(dict)
+    # # Update the MongoDB collection with the current count
+    # entry_count_collection.update_one({}, {"$set": {"count":entry_count}}, upsert=True)
+    # collection.update_one({"id": "your_document_id"}, {"$set": {"entry_count": entry_count}})
+    
+    cv2.putText(img, str(entry_count), (110, 245), cv2.FONT_HERSHEY_PLAIN, 5, (50, 50, 230), 7)
+    print('count is ', entry_count)
+    print(dict)
+    cv2.imshow("Image", img)
+    cv2.waitKey(1)
+    print(entry_count)
+    # collection.update_one({"id":"826587"},
+    #                {"$set": {
+    #                    "entry_count":entry_count,
+    #                   }})

sort.py

@@ -0,0 +1,342 @@
+"""
+    SORT: A Simple, Online and Realtime Tracker
+    Copyright (C) 2016-2020 Alex Bewley alex@bewley.ai
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+"""
+from __future__ import print_function
+
+import os
+import numpy as np
+import matplotlib
+
+matplotlib.use('TkAgg')
+import matplotlib.pyplot as plt
+import matplotlib.patches as patches
+from skimage import io
+
+import glob
+import time
+import argparse
+from filterpy.kalman import KalmanFilter
+
+np.random.seed(0)
+
+
+def linear_assignment(cost_matrix):
+    try:
+        import lap
+        _, x, y = lap.lapjv(cost_matrix, extend_cost=True)
+        return np.array([[y[i], i] for i in x if i >= 0])  #
+    except ImportError:
+        from scipy.optimize import linear_sum_assignment
+        x, y = linear_sum_assignment(cost_matrix)
+        return np.array(list(zip(x, y)))
+
+
+def iou_batch(bb_test, bb_gt):
+    """
+    From SORT: Computes IOU between two bboxes in the form [x1,y1,x2,y2]
+    """
+    bb_gt = np.expand_dims(bb_gt, 0)
+    bb_test = np.expand_dims(bb_test, 1)
+
+    xx1 = np.maximum(bb_test[..., 0], bb_gt[..., 0])
+    yy1 = np.maximum(bb_test[..., 1], bb_gt[..., 1])
+    xx2 = np.minimum(bb_test[..., 2], bb_gt[..., 2])
+    yy2 = np.minimum(bb_test[..., 3], bb_gt[..., 3])
+    w = np.maximum(0., xx2 - xx1)
+    h = np.maximum(0., yy2 - yy1)
+    wh = w * h
+    o = wh / ((bb_test[..., 2] - bb_test[..., 0]) * (bb_test[..., 3] - bb_test[..., 1])
+              + (bb_gt[..., 2] - bb_gt[..., 0]) * (bb_gt[..., 3] - bb_gt[..., 1]) - wh)
+    return (o)
+
+
+def convert_bbox_to_z(bbox):
+    """
+    Takes a bounding box in the form [x1,y1,x2,y2] and returns z in the form
+      [x,y,s,r] where x,y is the centre of the box and s is the scale/area and r is
+      the aspect ratio
+    """
+    w = bbox[2] - bbox[0]
+    h = bbox[3] - bbox[1]
+    x = bbox[0] + w / 2.
+    y = bbox[1] + h / 2.
+    s = w * h  # scale is just area
+    r = w / float(h)
+    return np.array([x, y, s, r]).reshape((4, 1))
+
+
+def convert_x_to_bbox(x, score=None):
+    """
+    Takes a bounding box in the centre form [x,y,s,r] and returns it in the form
+      [x1,y1,x2,y2] where x1,y1 is the top left and x2,y2 is the bottom right
+    """
+    w = np.sqrt(x[2] * x[3])
+    h = x[2] / w
+    if (score == None):
+        return np.array([x[0] - w / 2., x[1] - h / 2., x[0] + w / 2., x[1] + h / 2.]).reshape((1, 4))
+    else:
+        return np.array([x[0] - w / 2., x[1] - h / 2., x[0] + w / 2., x[1] + h / 2., score]).reshape((1, 5))
+
+
+class KalmanBoxTracker(object):
+    """
+    This class represents the internal state of individual tracked objects observed as bbox.
+    """
+    count = 0
+
+    def __init__(self, bbox):
+        """
+        Initialises a tracker using initial bounding box.
+        """
+        # define constant velocity model
+        self.kf = KalmanFilter(dim_x=7, dim_z=4)
+        self.kf.F = np.array(
+            [[1, 0, 0, 0, 1, 0, 0], [0, 1, 0, 0, 0, 1, 0], [0, 0, 1, 0, 0, 0, 1], [0, 0, 0, 1, 0, 0, 0],
+             [0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 1]])
+        self.kf.H = np.array(
+            [[1, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0]])
+
+        self.kf.R[2:, 2:] *= 10.
+        self.kf.P[4:, 4:] *= 1000.  # give high uncertainty to the unobservable initial velocities
+        self.kf.P *= 10.
+        self.kf.Q[-1, -1] *= 0.01
+        self.kf.Q[4:, 4:] *= 0.01
+
+        self.kf.x[:4] = convert_bbox_to_z(bbox)
+        self.time_since_update = 0
+        self.id = KalmanBoxTracker.count
+        KalmanBoxTracker.count += 1
+        self.history = []
+        self.hits = 0
+        self.hit_streak = 0
+        self.age = 0
+
+    def update(self, bbox):
+        """
+        Updates the state vector with observed bbox.
+        """
+        self.time_since_update = 0
+        self.history = []
+        self.hits += 1
+        self.hit_streak += 1
+        self.kf.update(convert_bbox_to_z(bbox))
+
+    def predict(self):
+        """
+        Advances the state vector and returns the predicted bounding box estimate.
+        """
+        if ((self.kf.x[6] + self.kf.x[2]) <= 0):
+            self.kf.x[6] *= 0.0
+        self.kf.predict()
+        self.age += 1
+        if (self.time_since_update > 0):
+            self.hit_streak = 0
+        self.time_since_update += 1
+        self.history.append(convert_x_to_bbox(self.kf.x))
+        return self.history[-1]
+
+    def get_state(self):
+        """
+        Returns the current bounding box estimate.
+        """
+        return convert_x_to_bbox(self.kf.x)
+
+
+def associate_detections_to_trackers(detections, trackers, iou_threshold=0.3):
+    """
+    Assigns detections to tracked object (both represented as bounding boxes)
+
+    Returns 3 lists of matches, unmatched_detections and unmatched_trackers
+    """
+    if (len(trackers) == 0):
+        return np.empty((0, 2), dtype=int), np.arange(len(detections)), np.empty((0, 5), dtype=int)
+
+    iou_matrix = iou_batch(detections, trackers)
+
+    if min(iou_matrix.shape) > 0:
+        a = (iou_matrix > iou_threshold).astype(np.int32)
+        if a.sum(1).max() == 1 and a.sum(0).max() == 1:
+            matched_indices = np.stack(np.where(a), axis=1)
+        else:
+            matched_indices = linear_assignment(-iou_matrix)
+    else:
+        matched_indices = np.empty(shape=(0, 2))
+
+    unmatched_detections = []
+    for d, det in enumerate(detections):
+        if (d not in matched_indices[:, 0]):
+            unmatched_detections.append(d)
+    unmatched_trackers = []
+    for t, trk in enumerate(trackers):
+        if (t not in matched_indices[:, 1]):
+            unmatched_trackers.append(t)
+
+    # filter out matched with low IOU
+    matches = []
+    for m in matched_indices:
+        if (iou_matrix[m[0], m[1]] < iou_threshold):
+            unmatched_detections.append(m[0])
+            unmatched_trackers.append(m[1])
+        else:
+            matches.append(m.reshape(1, 2))
+    if (len(matches) == 0):
+        matches = np.empty((0, 2), dtype=int)
+    else:
+        matches = np.concatenate(matches, axis=0)
+
+    return matches, np.array(unmatched_detections), np.array(unmatched_trackers)
+
+
+class Sort(object):
+    def __init__(self, max_age=1, min_hits=3, iou_threshold=0.3):
+        """
+        Sets key parameters for SORT
+        """
+        self.max_age = max_age
+        self.min_hits = min_hits
+        self.iou_threshold = iou_threshold
+        self.trackers = []
+        self.frame_count = 0
+
+    def update(self, dets=np.empty((0, 5))):
+        """
+        Params:
+          dets - a numpy array of detections in the format [[x1,y1,x2,y2,score],[x1,y1,x2,y2,score],...]
+        Requires: this method must be called once for each frame even with empty detections (use np.empty((0, 5)) for frames without detections).
+        Returns the a similar array, where the last column is the object ID.
+
+        NOTE: The number of objects returned may differ from the number of detections provided.
+        """
+        self.frame_count += 1
+        # get predicted locations from existing trackers.
+        trks = np.zeros((len(self.trackers), 5))
+        to_del = []
+        ret = []
+        for t, trk in enumerate(trks):
+            pos = self.trackers[t].predict()[0]
+            trk[:] = [pos[0], pos[1], pos[2], pos[3], 0]
+            if np.any(np.isnan(pos)):
+                to_del.append(t)
+        trks = np.ma.compress_rows(np.ma.masked_invalid(trks))
+        for t in reversed(to_del):
+            self.trackers.pop(t)
+        matched, unmatched_dets, unmatched_trks = associate_detections_to_trackers(dets, trks, self.iou_threshold)
+
+        # update matched trackers with assigned detections
+        for m in matched:
+            self.trackers[m[1]].update(dets[m[0], :])
+
+        # create and initialise new trackers for unmatched detections
+        for i in unmatched_dets:
+            trk = KalmanBoxTracker(dets[i, :])
+            self.trackers.append(trk)
+        i = len(self.trackers)
+        for trk in reversed(self.trackers):
+            d = trk.get_state()[0]
+            if (trk.time_since_update < 1) and (trk.hit_streak >= self.min_hits or self.frame_count <= self.min_hits):
+                ret.append(np.concatenate((d, [trk.id + 1])).reshape(1, -1))  # +1 as MOT benchmark requires positive
+            i -= 1
+            # remove dead tracklet
+            if (trk.time_since_update > self.max_age):
+                self.trackers.pop(i)
+        if (len(ret) > 0):
+            return np.concatenate(ret)
+        return np.empty((0, 5))
+
+
+def parse_args():
+    """Parse input arguments."""
+    parser = argparse.ArgumentParser(description='SORT demo')
+    parser.add_argument('--display', dest='display', help='Display online tracker output (slow) [False]',
+                        action='store_true')
+    parser.add_argument("--seq_path", help="Path to detections.", type=str, default='data')
+    parser.add_argument("--phase", help="Subdirectory in seq_path.", type=str, default='train')
+    parser.add_argument("--max_age",
+                        help="Maximum number of frames to keep alive a track without associated detections.",
+                        type=int, default=1)
+    parser.add_argument("--min_hits",
+                        help="Minimum number of associated detections before track is initialised.",
+                        type=int, default=3)
+    parser.add_argument("--iou_threshold", help="Minimum IOU for match.", type=float, default=0.3)
+    args = parser.parse_args()
+    return args
+
+
+if __name__ == '__main__':
+    # all train
+    args = parse_args()
+    display = args.display
+    phase = args.phase
+    total_time = 0.0
+    total_frames = 0
+    colours = np.random.rand(32, 3)  # used only for display
+    if (display):
+        if not os.path.exists('mot_benchmark'):
+            print(
+                '\n\tERROR: mot_benchmark link not found!\n\n    Create a symbolic link to the MOT benchmark\n    (https://motchallenge.net/data/2D_MOT_2015/#download). E.g.:\n\n    $ ln -s /path/to/MOT2015_challenge/2DMOT2015 mot_benchmark\n\n')
+            exit()
+        plt.ion()
+        fig = plt.figure()
+        ax1 = fig.add_subplot(111, aspect='equal')
+
+    if not os.path.exists('output'):
+        os.makedirs('output')
+    pattern = os.path.join(args.seq_path, phase, '*', 'det', 'det.txt')
+    for seq_dets_fn in glob.glob(pattern):
+        mot_tracker = Sort(max_age=args.max_age,
+                           min_hits=args.min_hits,
+                           iou_threshold=args.iou_threshold)  # create instance of the SORT tracker
+        seq_dets = np.loadtxt(seq_dets_fn, delimiter=',')
+        seq = seq_dets_fn[pattern.find('*'):].split(os.path.sep)[0]
+
+        with open(os.path.join('output', '%s.txt' % (seq)), 'w') as out_file:
+            print("Processing %s." % (seq))
+            for frame in range(int(seq_dets[:, 0].max())):
+                frame += 1  # detection and frame numbers begin at 1
+                dets = seq_dets[seq_dets[:, 0] == frame, 2:7]
+                dets[:, 2:4] += dets[:, 0:2]  # convert to [x1,y1,w,h] to [x1,y1,x2,y2]
+                total_frames += 1
+
+                if (display):
+                    fn = os.path.join('mot_benchmark', phase, seq, 'img1', '%06d.jpg' % (frame))
+                    im = io.imread(fn)
+                    ax1.imshow(im)
+                    plt.title(seq + ' Tracked Targets')
+
+                start_time = time.time()
+                trackers = mot_tracker.update(dets)
+                cycle_time = time.time() - start_time
+                total_time += cycle_time
+
+                for d in trackers:
+                    print('%d,%d,%.2f,%.2f,%.2f,%.2f,1,-1,-1,-1' % (frame, d[4], d[0], d[1], d[2] - d[0], d[3] - d[1]),
+                          file=out_file)
+                    if (display):
+                        d = d.astype(np.int32)
+                        ax1.add_patch(patches.Rectangle((d[0], d[1]), d[2] - d[0], d[3] - d[1], fill=False, lw=3,
+                                                        ec=colours[d[4] % 32, :]))
+
+                if (display):
+                    fig.canvas.flush_events()
+                    plt.draw()
+                    ax1.cla()
+
+    print("Total Tracking took: %.3f seconds for %d frames or %.1f FPS" % (
+    total_time, total_frames, total_frames / total_time))
+
+    if (display):
+        print("Note: to get real runtime results run without the option: --display")