def load_yolo(modelfile="yolo.h5", confjson=None):
yolo = Detection.ObjectDetection() if confjson is None else CustomObjectDetection()
yolo.setModelTypeAsYOLOv3()
yolo.setModelPath(modelfile)
if confjson is not None:
yolo.setJsonPath(confjson)
yolo.loadModel()
return yolo
def detect_objects(video_src):
yolo = Detection.ObjectDetection()
yolo.setModelTypeAsYOLOv3()
yolo.setModelPath("./yolo.h5")
yolo.loadModel()
cam = cv.VideoCapture(video_src)
if not cam.isOpened():
print("could not open video_src " + str(video_src) + " !\n")
cam.set(cv.CAP_PROP_POS_AVI_RATIO, 1)
MHI_DURATION = cam.get(cv.CAP_PROP_POS_MSEC)
cam = cv.VideoCapture(video_src)
if not cam.isOpened():
print("could not open video_src " + str(video_src) + " !\n")
ret, img = cam.read()
if ret == False:
print("could not read from " + str(video_src) + " !\n")
i = 0
while cam.isOpened():
ret, frame = cam.read()
i += 1
if ret == False:
break
if i % 13 != 0: # 26 fps.
continue
img, preds = yolo.detectCustomObjectsFromImage(
input_image=img,
custom_objects=None,
input_type="array",
output_type="array",
minimum_percentage_probability=70,
display_percentage_probability=False,
display_object_name=True)
print(preds)
for pred in preds:
if pred['name'] not in objects:
objects.append(pred['name'])
def video_process(self, filename, no_of_frames):
progress_recorder = ProgressRecorder(self)
# media_url = settings.MEDIA_URL
# path_to_user_folder = media_url + "/video/20" + filename + ".mp4"
VID_PATH = "media/video/trial1.mp4"
MODEL_PATH = "video/yolo-tiny.h5"
CONTINUITY_THRESHOLD = 8 #For cutting out boxes
MIN_SECONDS = 1 # (seconds) Minimum duration of a moving object
INTERVAL_BW_DIVISIONS = 5 # (seconds) For distributing moving objects over a duration to reduce overlapping.
GAP_BW_DIVISIONS = 1.5 #(seconds)
FRAMES_PER_DETECTION = 3
fgbg = cv2.createBackgroundSubtractorKNN()
detector = Detection.ObjectDetection()
detector.setModelTypeAsTinyYOLOv3()
detector.setModelPath(MODEL_PATH)
detector.loadModel()
custom_objects = detector.CustomObjects(car=True,
motorcycle=True,
person=True,
bicycle=True,
bus=True,
truck=True)
vid = cv2.VideoCapture(VID_PATH)
fps = int(vid.get(cv2.CAP_PROP_FPS))
length = int(vid.get(cv2.CAP_PROP_FRAME_COUNT))
no_of_frames[0] = length
no_of_frames[1] = fps
frames = []
masks = []
ind = 0
start_time = tm.time()
all_conts = []
all_types = []
avg2 = None
while (vid.isOpened()):
# Background Extraction
if (avg2 != None): cv2.accumulateWeighted(frame, avg2, 0.01)
ret, frame = vid.read()
if (not ret): break
if (avg2): avg2 = np.float32(frame)
fgmask = fgbg.apply(frame)
# Running detection for every FRAMES_PER_DETECTION
if (ind % FRAMES_PER_DETECTION == 0):
frames.append(frame)
detected_image_array, detections = detector.detectCustomObjectsFromImage(
input_type="array",
output_type='array',
custom_objects=custom_objects,
input_image=frame,
minimum_percentage_probability=50)
types = []
conts = []
for eachObject in detections:
rect = eachObject["box_points"]
conts.append(
np.array([
rect[0], rect[1], rect[2] - rect[0], rect[3] - rect[1]
]))
types.append(eachObject["name"])
# print(ind//FRAMES_PER_DETECTION, end=' ', flush=True)
all_conts.append(np.array(conts))
all_types.append(types)
masks.append(fgmask)
# cv2.imshow('frame', detected_image_array)
# if cv2.waitKey(1) & 0xFF == ord("q"):
# break
ind += 1
# if ind==60:
# break
progress_recorder.set_progress(ind, length + length // 10,
f'On iteration {ind}')
masks = np.array(masks)
frames = np.array(frames)
# print("--- %s seconds ---" % (tm.time() - start_time))
vid.release()
# cv2.destroyAllWindows()
moving_objs = []
for curr_time, new_boxes in enumerate(all_conts): #iterating over frames
if len(new_boxes) != 0: #if not empty
new_assocs = [None] * len(
new_boxes
) #all new boxes initially are not associated with any moving_objs
obj_coords = np.array([
obj.last_coords() for obj in moving_objs
if obj.age(curr_time) < CONTINUITY_THRESHOLD
])
unexp_idx = -1 #index of unexpired obj in moving_objs
for obj_idx, obj in enumerate(moving_objs):
if obj.age(
curr_time
) < CONTINUITY_THRESHOLD: #checking only unexpired objects
unexp_idx += 1
nearest_new, dst1 = get_nearest(
obj.last_coords(), new_boxes) #nearest box to obj
nearest_obj, dst = get_nearest(
new_boxes[nearest_new],
obj_coords) #nearest obj to box
if nearest_obj == unexp_idx: #both closest to each-other
#associate
if (dst < 100):
new_assocs[nearest_new] = obj_idx
for new_idx, new_coords in enumerate(new_boxes):
new_assoc = new_assocs[new_idx]
new_box = box(new_coords, curr_time, all_types[curr_time][new_idx])
if new_assoc is not None:
#associate new box to moving_obj
moving_objs[new_assoc].add_box(new_box)
else:
#add a fresh, new moving_obj to moving_objs
new_moving_obj = moving_obj(new_box)
moving_objs.append(new_moving_obj)
# print("Done", end=' ')
#Removing objects that occur for a very small duration
MIN_FRAMES = MIN_SECONDS * (fps // 3)
moving_objs = [
obj for obj in moving_objs
if (obj.boxes[-1].time - obj.boxes[0].time) >= MIN_FRAMES
]
print(len(moving_objs))
for i, obj in enumerate(moving_objs):
boxes = obj.boxes
g = 0
mx = 0
otype = {}
for rect in boxes:
x, y, w, h = map(int, list(rect.coords))
area = w * h
if (area > mx):
mx = area
mx_i = g
# cv2.imwrite('./test/'+str(i)+'_'+str(g)+'.jpg', frames[rect.time, y:y+h, x:x+w])
if (rect.type not in otype): otype[rect.type] = 0
otype[rect.type] += 1
g += 1
moving_objs[i].type = max(otype, key=otype.get)
rect = boxes[mx_i]
x, y, w, h = map(int, list(rect.coords))
cv2.imwrite('media/result/' + str(i) + '.jpg',
frames[rect.time, y:y + h, x:x + w])
ret, thresh = cv2.threshold(masks[rect.time], 1, 255,
cv2.THRESH_BINARY)
# cv2.imwrite('./result/a'+str(i)+'.jpg', thresh[y:y+h, x:x+w])
moving_objs[i].img = frames[rect.time, y:y + h, x:x + w]
moving_objs[i].mask = thresh[y:y + h, x:x + w]
moving_objs[i].color = findColor(moving_objs[i].img,
moving_objs[i].mask)
temp = 'http://127.0.0.1:8000/media/result/' + str(i) + '.jpg'
moving_objs[i].imgname = temp
SECONDS_PER_3FRAME = FRAMES_PER_DETECTION / fps
with open('media/out.csv', 'w', newline='') as f:
writer = csv.writer(f)
for i, obj in enumerate(moving_objs):
writer.writerow([
i, obj.type, obj.color[0], obj.color[1], obj.color[2],
obj.boxes[0].time * SECONDS_PER_3FRAME,
obj.boxes[-1].time * SECONDS_PER_3FRAME, obj.imgname
])
progress_recorder.set_progress(ind + length // 10, length + length // 10,
"Exporting data for result ...")
tm.sleep(3)
modelpath = "./yolo.h5"
from imageai import Detection
import cv2
import os
import sys
import pkg_resources
haar_xml = pkg_resources.resource_filename(
'cv2', 'data/haarcascade_frontalface_default.xml')
haar_xml = 'C:\\ProgramData\\Anaconda3\\lib\\site-packages\\cv2\\data\\haarcascade_frontalface_default.xml'
faceCascade = cv2.CascadeClassifier(haar_xml)
print(faceCascade.empty())
os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'
yolo = Detection.ObjectDetection()
yolo.setModelTypeAsYOLOv3()
yolo.setModelPath(modelpath)
yolo.loadModel()
cam = cv2.VideoCapture(0) #0=front-cam, 1=external cam
cam.set(cv2.CAP_PROP_FRAME_WIDTH, 720)
cam.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)
while True:
ret, img = cam.read()
img2, preds = yolo.detectCustomObjectsFromImage(input_image=img,
custom_objects=yolo.CustomObjects(person=True), input_type="array",
output_type="array",
minimum_percentage_probability=80,
display_percentage_probability=True,
from imageai import Detection
from scipy.stats import itemfreq
import csv
execution_path = os.getcwd()
VID_PATH = './demos/trial1.mp4'
CONTINUITY_THRESHOLD = 8 #For cutting out boxes
MIN_SECONDS = 1 # (seconds) Minimum duration of a moving object
INTERVAL_BW_DIVISIONS = 5 # (seconds) For distributing moving objects over a duration to reduce overlapping.
GAP_BW_DIVISIONS = 1.5 #(seconds)
FRAMES_PER_DETECTION = 3
fgbg = cv2.createBackgroundSubtractorKNN()
detector = Detection.ObjectDetection()
detector.setModelTypeAsTinyYOLOv3()
detector.setModelPath("./yolo-tiny.h5")
detector.loadModel()
custom_objects = detector.CustomObjects(car=True,
motorcycle=True,
person=True,
bicycle=True,
bus=True,
truck=True)
# ## Detection on video
def videoAnalyze(video_url):
vid = cv2.VideoCapture(video_url)
from imageai import Detection
import cv2
import matplotlib.pyplot as plt
from defisheye import Defisheye
dtype = 'linear'
format = 'fullframe'
fov = 180
pfov = 120
model = Detection.ObjectDetection()
model.setModelTypeAsYOLOv3()
model.setModelPath('yolo.h5')
# model.setModelPath('resnet50_coco_best_v2.0.1.h5')
model.loadModel()
# custom = model.CustomObjects(person=True)
vid = "/home/mthk/Desktop/video-object-detection/video/NVR_ch17_main_20201212080008_20201212083025_2.mp4"
camera = cv2.VideoCapture(vid)
rect_points = []
drawing = False
def draw_rect_with_drag(event, x, y, flags, param):
global drawing, img, rect_points
if event == cv2.EVENT_LBUTTONDOWN:
drawing = True
rect_points = [(x, y)]
elif event == cv2.EVENT_LBUTTONUP: