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synced 2025-09-26 20:31:14 +08:00
cleaned up the file
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QuintenMM
@@ -1,9 +1,11 @@
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import cv2
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import datetime
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import imutils
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import numpy as np
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from collections import defaultdict
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from centroidtracker import CentroidTracker
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import pandas as pd
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import imutils
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protopath = "MobileNetSSD_deploy.prototxt"
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modelpath = "MobileNetSSD_deploy.caffemodel"
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@@ -15,141 +17,170 @@ detector = cv2.dnn.readNetFromCaffe(prototxt=protopath, caffeModel=modelpath)
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CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
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"bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
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"dog", "horse", "motorbike", "person", "pottedplant", "sheep",
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"sofa", "train", "tvmonitor"]
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"bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
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"dog", "horse", "motorbike", "person", "pottedplant", "sheep",
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"sofa", "train", "tvmonitor"]
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# maxDisappeared, time wait when object moves out of frame
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tracker = CentroidTracker(maxDisappeared=700, maxDistance=220)
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tracker = CentroidTracker(maxDisappeared=500, maxDistance=220)
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def non_max_suppression_fast(boxes, overlapThresh):
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try:
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if len(boxes) == 0:
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return []
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'"Cobine boundingboxes that overlap into one bbox"'
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try:
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if len(boxes) == 0:
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return []
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if boxes.dtype.kind == "i":
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boxes = boxes.astype("float")
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if boxes.dtype.kind == "i":
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boxes = boxes.astype("float")
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pick = []
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pick = []
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x1 = boxes[:, 0]
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y1 = boxes[:, 1]
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x2 = boxes[:, 2]
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y2 = boxes[:, 3]
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x1 = boxes[:, 0]
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y1 = boxes[:, 1]
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x2 = boxes[:, 2]
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y2 = boxes[:, 3]
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area = (x2 - x1 + 1) * (y2 - y1 + 1)
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idxs = np.argsort(y2)
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area = (x2 - x1 + 1) * (y2 - y1 + 1)
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idxs = np.argsort(y2)
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while len(idxs) > 0:
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last = len(idxs) - 1
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i = idxs[last]
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pick.append(i)
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while len(idxs) > 0:
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last = len(idxs) - 1
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i = idxs[last]
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pick.append(i)
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xx1 = np.maximum(x1[i], x1[idxs[:last]])
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yy1 = np.maximum(y1[i], y1[idxs[:last]])
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xx2 = np.minimum(x2[i], x2[idxs[:last]])
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yy2 = np.minimum(y2[i], y2[idxs[:last]])
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xx1 = np.maximum(x1[i], x1[idxs[:last]])
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yy1 = np.maximum(y1[i], y1[idxs[:last]])
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xx2 = np.minimum(x2[i], x2[idxs[:last]])
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yy2 = np.minimum(y2[i], y2[idxs[:last]])
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w = np.maximum(0, xx2 - xx1 + 1)
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h = np.maximum(0, yy2 - yy1 + 1)
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w = np.maximum(0, xx2 - xx1 + 1)
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h = np.maximum(0, yy2 - yy1 + 1)
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overlap = (w * h) / area[idxs[:last]]
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overlap = (w * h) / area[idxs[:last]]
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idxs = np.delete(idxs, np.concatenate(([last],
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np.where(overlap > overlapThresh)[0])))
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idxs = np.delete(idxs, np.concatenate(([last],
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np.where(overlap > overlapThresh)[0])))
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return boxes[pick].astype("int")
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except Exception as e:
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print("Exception occurred in non_max_suppression : {}".format(e))
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return boxes[pick].astype("int")
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except Exception as e:
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print("Exception occurred in non_max_suppression : {}".format(e))
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def convert_to_2d(Xcenter, y2):
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'" Convert the coordinates from a 3d playing field to a 2d playing field"'
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pts_src = np.array([[257, 262], [370, 225], [492, 190], [294, 324], [474, 272], [620, 213], [727, 383], [799, 259]])
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# Take points from the frame as reference and give the same point coordinates on the picture for a transformation
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pts_dst = np.array([[110, 145], [349, 145], [588, 145], [110, 500], [349, 500], [588, 500], [349, 855], [588, 855]])
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# calculate matrix H
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h, status = cv2.findHomography(pts_src, pts_dst)
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def main():
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cap = cv2.VideoCapture('1639943552_6-967003_camera1_200-200-400-400_24_769.mp4')
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# provide a point you wish to map from image 1 to image 2
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a = np.array([[Xcenter, y2]], dtype='float32')
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a = np.array([a])
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fps_start_time = datetime.datetime.now()
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fps = 0
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total_frames = 0
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centroid_dict = defaultdict(list)
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object_id_list = []
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# finally, get the mapping
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pointsOut = cv2.perspectiveTransform(a, h)
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pointsOut = pointsOut.astype(int)
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return pointsOut
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while True:
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ret, frame = cap.read()
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frame = imutils.resize(frame, width=600)
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total_frames = total_frames + 1
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(height, width) = frame.shape[:2]
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def main(video="1639943552_6-967003_camera1_200-200-400-400_24_769.mp4"):
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'"Read the frames, recognise humans and track them. The coordinates of the bottom of the bbox are saved for transormation and plotting"'
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cap = cv2.VideoCapture(video)
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blob = cv2.dnn.blobFromImage(frame, 0.007843, (width, height), 127.5)
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detector.setInput(blob)
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person_detections = detector.forward()
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paddel_2d = cv2.imread('media/paddelfield.jpeg')
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rects = []
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for i in np.arange(0, person_detections.shape[2]):
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confidence = person_detections[0, 0, i, 2]
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if confidence > 0.5:
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idx = int(person_detections[0, 0, i, 1])
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fps_start_time = datetime.datetime.now()
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fps = 0
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total_frames = 0
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centroid_dict = defaultdict(list)
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object_id_list = []
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if CLASSES[idx] != "person":
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continue
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while True:
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ret, frame = cap.read()
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frame = imutils.resize(frame, width=800)
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# frame = frame[540:1080, 700:1920]
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total_frames = total_frames + 1
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(height, width) = frame.shape[:2]
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person_box = person_detections[0, 0, i, 3:7] * np.array([width, height, width, height])
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(startX, startY, endX, endY) = person_box.astype("int")
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rects.append(person_box)
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blob = cv2.dnn.blobFromImage(frame, 0.007843, (width, height), 127.5)
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detector.setInput(blob)
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person_detections = detector.forward()
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boundingboxes = np.array(rects)
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boundingboxes = boundingboxes.astype(int)
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rects = non_max_suppression_fast(boundingboxes, 0.4)
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objects = tracker.update(rects)
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rects = []
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for i in np.arange(0, person_detections.shape[2]):
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confidence = person_detections[0, 0, i, 2]
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if confidence > 0.5:
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idx = int(person_detections[0, 0, i, 1])
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for (objectId, bbox) in objects.items():
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x1, y1, x2, y2 = bbox
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x1 = int(x1)
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y1 = int(y1)
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x2 = int(x2)
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y2 = int(y2)
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if CLASSES[idx] != "person":
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continue
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xCenter = int((x1 + x2) / 2)
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yCenter = int((y1 + y2) / 2)
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person_box = person_detections[0, 0, i, 3:7] * np.array([width, height, width, height])
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(startX, startY, endX, endY) = person_box.astype("int")
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rects.append(person_box)
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cv2.circle(frame, (xCenter, y2), 5, (0, 255, 0), -1)
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boundingboxes = np.array(rects)
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boundingboxes = boundingboxes.astype(int)
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rects = non_max_suppression_fast(boundingboxes, 0.4)
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objects = tracker.update(rects)
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centroid_dict[objectId].append((xCenter, y2))
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for (objectId, bbox) in objects.items():
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x1, y1, x2, y2 = bbox
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x1 = int(x1)
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y1 = int(y1)
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x2 = int(x2)
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y2 = int(y2)
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if objectId not in object_id_list:
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object_id_list.append(objectId)
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start_pt = (xCenter, y2)
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end_pt = (xCenter, y2)
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cv2.line(frame, start_pt, end_pt, (0, 255, 0), 2)
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else:
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L = len(centroid_dict[objectId])
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for pt in range(len(centroid_dict[objectId])):
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if not pt + 1 == L:
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start_pt = (centroid_dict[objectId][pt][0], centroid_dict[objectId][pt][1])
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end_pt = (centroid_dict[objectId][pt + 1][0], centroid_dict[objectId][pt + 1][1])
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cv2.line(frame, start_pt, end_pt, (0, 255, 0), 1)
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xCenter = int((x1 + x2) / 2)
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yCenter = int((y1 + y2) / 2)
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cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 0, 255), 2)
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text = "ID: {}".format(objectId)
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cv2.putText(frame, text, (x1, y1-5), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 1)
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cv2.circle(frame, (xCenter, y2), 5, (0, 255, 0), -1)
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fps_end_time = datetime.datetime.now()
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time_diff = fps_end_time - fps_start_time
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if time_diff.seconds == 0:
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fps = 0.0
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else:
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fps = (total_frames / time_diff.seconds)
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fps_text = "FPS: {:.2f}".format(fps)
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cv2.putText(frame, fps_text, (5, 30), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 1)
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# saving the converted cords
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pointsout = convert_to_2d(xCenter, y2)
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for tuple in pointsout:
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for points in tuple:
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pd.DataFrame({'x': [points[0]], 'y': [points[1]]}, index=[objectId]).to_csv('cords.csv', mode='a',
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header=False)
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cv2.imshow("Application", frame)
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# cv2.VideoWriter_fourcc("new_vid.mp4", cv2.VideoWriter_fourcc(*'mp4v'), 20, (width, height))
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key = cv2.waitKey(1)
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if key == ord('q'):
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break
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cap.release()
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cv2.destroyAllWindows()
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centroid_dict[objectId].append((xCenter, y2))
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if objectId not in object_id_list:
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object_id_list.append(objectId)
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start_pt = (xCenter, y2)
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end_pt = (xCenter, y2)
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cv2.line(frame, start_pt, end_pt, (0, 255, 0), 2)
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else:
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L = len(centroid_dict[objectId])
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for pt in range(len(centroid_dict[objectId])):
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if not pt + 1 == L:
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start_pt = (centroid_dict[objectId][pt][0], centroid_dict[objectId][pt][1])
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end_pt = (centroid_dict[objectId][pt + 1][0], centroid_dict[objectId][pt + 1][1])
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cv2.line(frame, start_pt, end_pt, (0, 255, 0), 1)
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cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 0, 255), 2)
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text = "ID: {}".format(objectId)
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cv2.putText(frame, text, (x1, y1 - 5), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 1)
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fps_end_time = datetime.datetime.now()
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time_diff = fps_end_time - fps_start_time
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if time_diff.seconds == 0:
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fps = 0.0
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else:
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fps = (total_frames / time_diff.seconds)
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fps_text = "FPS: {:.2f}".format(fps)
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cv2.putText(frame, fps_text, (5, 30), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 1)
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# cv2.imshow("Application", frame)
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key = cv2.waitKey(1)
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if key == ord('q'):
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break
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cap.release()
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cv2.destroyAllWindows()
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main()
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