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main_spatial.py
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main_spatial.py
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import cv2 as cv
import numpy as np
import time
from scipy_distance_tracker import ScipySpatialTracker
def draw_bounding_boxes(idxs, bboxes, classIDs, confidences, detections_bboxes):
if len(idxs) > 0:
for i in idxs.flatten():
(x, y) = (bboxes[i][0], bboxes[i][1])
(w, h) = (bboxes[i][2], bboxes[i][3])
detections_bboxes.append((x, y, x + w, y + h))
clr = [int(c) for c in bbox_colors[classIDs[i]]]
cv.rectangle(image, (x, y), (x + w, y + h), clr, 2)
cv.putText(image, "{}: {:.4f}".format(labels[classIDs[i]], confidences[i]),
(x, y - 5), cv.FONT_HERSHEY_SIMPLEX, 0.5, clr, 2)
def draw_id_label(objects, class_id, tracker_obj):
for (object_id, centroid) in objects.items():
text_label = "ID {}".format(object_id)
tracker_obj.number_of_elements = max(tracker_obj.number_of_elements, object_id)
clr = [int(c) for c in bbox_colors[class_id]]
cv.putText(image, text_label, (centroid[0] - 10, centroid[1] - 10), cv.FONT_HERSHEY_SIMPLEX, 0.5, clr, 2)
cv.circle(image, (centroid[0], centroid[1]), 4, (0, 255, 0), -1)
model_type = "yolov3"
if model_type == "yolov2":
yolomodel = {"config_path": "E:\models\yolov2\yolov2.cfg",
"model_weights_path": "E:\models\yolov2\yolov2.weights",
"coco_names": "E:\models\yolov2\coco.names",
"confidence_threshold": 0.5,
"threshold": 0.3
}
if model_type == "yolov2-tiny":
yolomodel = {"config_path": "E:\models\yolo-tiny\yolov2-tiny.cfg",
"model_weights_path": "E:\models\yolo-tiny\yolov2-tiny.weights",
"coco_names": "E:\models\yolo-tiny\coco.names",
"confidence_threshold": 0.5,
"threshold": 0.3
}
if model_type == "yolov3":
yolomodel = {"config_path": "E:\models\yolov3\yolov3.cfg",
"model_weights_path": "E:\models\yolov3\yolov3.weights",
"coco_names": "E:\models\yolov3\coco.names",
"confidence_threshold": 0.5,
"threshold": 0.3
}
number_of_pedestrians = 0
number_of_cars = 0
net = cv.dnn.readNetFromDarknet(yolomodel["config_path"], yolomodel["model_weights_path"])
net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU)
labels = open(yolomodel["coco_names"]).read().strip().split("\n")
np.random.seed(12345)
layer_names = net.getLayerNames()
layer_names = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
print(layer_names)
bbox_colors = np.random.randint(0, 255, size=(len(labels), 3))
maxLost = 5 # maximum number of object losts counted when the object is being tracked
pedestrian_tracker = ScipySpatialTracker(maxLost=maxLost)
car_tracker = ScipySpatialTracker(maxLost=maxLost)
video_src = "input/crossroads.mp4"
cap = cv.VideoCapture(video_src)
(H, W) = (None, None) # input image height and width for the network
writer = None
loop_counter = 0
while True:
loop_counter += 1
start_time = time.time()
ok, image = cap.read()
if not ok:
print("Cannot read the video feed.")
break
if W is None or H is None:
(H, W) = image.shape[:2]
blob = cv.dnn.blobFromImage(image, 1 / 255.0, (416, 416), swapRB=True, crop=False)
net.setInput(blob)
detections_layer = net.forward(layer_names) # detect objects using object detection model
pedestrian_detections_bbox = [] # pedestrian bounding box for detections
car_detections_bbox = [] # car bounding box for detections
pedestrian_boxes, pedestrian_confidences, pedestrian_classIDs = [], [], []
car_boxes, car_confidences, car_classIDs = [], [], []
for out in detections_layer:
for detection in out:
scores = detection[5:]
classID = np.argmax(scores)
confidence = scores[classID]
# we are not interested in other COCO classes
if classID > 3:
continue
if confidence > yolomodel['confidence_threshold']:
box = detection[0:4] * np.array([W, H, W, H])
(centerX, centerY, width, height) = box.astype("int")
x = int(centerX - (width / 2))
y = int(centerY - (height / 2))
if classID == 0: # pedestrians
pedestrian_boxes.append([x, y, int(width), int(height)])
pedestrian_confidences.append(float(confidence))
pedestrian_classIDs.append(classID)
if classID == 2: # cars
car_boxes.append([x, y, int(width), int(height)])
car_confidences.append(float(confidence))
car_classIDs.append(classID)
pedestrians_idxs = cv.dnn.NMSBoxes(pedestrian_boxes, pedestrian_confidences, yolomodel["confidence_threshold"],
yolomodel["threshold"])
cars_idxs = cv.dnn.NMSBoxes(car_boxes, car_confidences, yolomodel["confidence_threshold"], yolomodel["threshold"])
draw_bounding_boxes(pedestrians_idxs, pedestrian_boxes, pedestrian_classIDs, pedestrian_confidences,
pedestrian_detections_bbox)
draw_bounding_boxes(cars_idxs, car_boxes, car_classIDs, car_confidences, car_detections_bbox)
pedestrian_objects = pedestrian_tracker.update(pedestrian_detections_bbox) # update with newly detected objects
car_objects = car_tracker.update(car_detections_bbox) # update tracker with newly detected objects
draw_id_label(pedestrian_objects, 0, pedestrian_tracker)
draw_id_label(car_objects, 2, car_tracker)
pedestrian_clr = [int(c) for c in bbox_colors[0]]
car_clr = [int(c) for c in bbox_colors[2]]
cv.putText(image, "Peds: " + str(pedestrian_tracker.number_of_elements + 1), (30, 30),
cv.FONT_HERSHEY_SIMPLEX, 1, pedestrian_clr, 3)
cv.putText(image, "Cars: " + str(car_tracker.number_of_elements + 1), (30, 70),
cv.FONT_HERSHEY_SIMPLEX, 1, car_clr, 3)
cv.imshow("image", image)
if cv.waitKey(1) & 0xFF == ord('q'):
break
if writer is None:
fourcc = cv.VideoWriter_fourcc(*"MJPG")
writer = cv.VideoWriter("E:/Skopus/recordings/output/yolo/kg_nms_" + model_type + ".avi", fourcc, 20,
(W, H), True)
writer.write(image)
print("FPS: ", 1.0 / (time.time() - start_time))
writer.release()
cap.release()
cv.destroyWindow("image")