def find_people_in_frame(inputFrame, detector=HOGDescriptor_getDefaultPeopleDetector):
hog = HOGDescriptor()
hog.setSVMDetector(detector())
image = resize(inputFrame, width=min(400, inputFrame.shape[1]))
orig = image.copy()
(rects, _) = hog.detectMultiScale(
image, winStride=(4, 4), padding=(8, 8), scale=1.05)
for (x, y, w, h) in rects:
rectangle(orig, (x, y), (x+w, y+h), (255, 0, 0), 2)
rects = array([[x, y, x+w, y+h] for (x, y, w, h) in rects])
merged_rects = nms(rects, probs=None, overlapThresh=0.65)
for (xA, yA, xB, yB) in merged_rects:
rectangle(image, (xA, yA), (xB, yB), (0, 255, 0), 2)
size_of_image = image.shape[0] * image.shape[1]
size_of_window = abs(xA - xB) * abs(yA - yB)
portion_occupied = (size_of_image / size_of_window) * 100
color = None
if portion_occupied >= 0.1:
color = (255, 0, 0)
elif portion_occupied >= 0.05:
color = (0, 0, 255)
else:
color = (0, 255, 0)
putText(
image, f'Threat Level: {round(abs(xA - xB) * abs(yA - yB) * 1 / 150)}', (xA, yA-10), FONT_HERSHEY_SIMPLEX, 0.9, color, 2)
return image
def hogDetection(frame, winStride, scale):
hog = cv2.HOGDescriptor()
hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
(rects, weights) = hog.detectMultiScale(frame, winStride=winStride, padding=(4,4)
, scale=scale)
# apply nms
rects = nms(rects, probs=None, overlapThresh=0.65)
return rects
def detect_people(frame, winStride, scale, dnn):
if dnn is False:
hog = cv2.HOGDescriptor()
hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
(rects, weights) = hog.detectMultiScale(frame,
winStride=winStride,
padding=(4, 4),
scale=scale,
hitThreshold=0.4)
rects = nms(rects, probs=None, overlapThresh=0.65)
else:
rects = []
prototxt = "../model/MobileNetSSD_deploy.prototxt"
caffeModel = "../model/MobileNetSSD_deploy.caffemodel"
net = cv2.dnn.readNetFromCaffe(prototxt, caffeModel)
rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
(h, w) = frame.shape[:2]
blob = cv2.dnn.blobFromImage(rgb, 0.007843, (w, h), 127.5)
net.setInput(blob)
detections = net.forward()
for i in np.arange(0, detections.shape[2]):
confidence = detections[0, 0, i, 2]
if confidence > 0.5:
idx = int(detections[0, 0, i, 1])
if CLASSES[idx] != "person":
continue
box = detections[0, 0, i, 3:7] * np.array(
[w, h, w, h]) # rescale detection box size to frame size
(startX, startY, endX, endY) = box.astype("int")
x = startX
y = startY
w = endX - startX
h = endY - startY
rects.append((x, y, w, h))
return rects
def run_detection_baseline(inputFolder, outputFolder):
hog = HOGDescriptor()
hog.setSVMDetector(HOGDescriptor_getDefaultPeopleDetector())
if not exists(outputFolder):
mkdir(outputFolder)
for imagePath in list_images(inputFolder):
image = imread(imagePath)
image = resize(image, width=min(400, image.shape[1]))
orig = image.copy()
(rects, _) = hog.detectMultiScale(
image, winStride=(4, 4), padding=(8, 8), scale=1.05)
for (x, y, w, h) in rects:
rectangle(orig, (x, y), (x+w, y+h), (0, 0, 255), 2)
rects = array([[x, y, x+w, y+h] for (x, y, w, h) in rects])
merged_rects = nms(rects, probs=None, overlapThresh=0.65)
for (xA, yA, xB, yB) in merged_rects:
rectangle(image, (xA, yA), (xB, yB), (0, 255, 0), 2)
filename = imagePath[imagePath.rfind('/') + 1:]
imwrite(join(outputFolder, filename), image)