def drawing(self, flag=0):
random.seed(3) # deterministic bbox colors
# video = self.set_saved_video(self.cap, self.output_video, (self.width, self.height))
if flag:
self.draw()
else:
while self.cap.isOpened():
frame_resized = self.frame_queue.get()
detections = self.detections_queue.get()
# -------------------------------------------
# face_detections = self.face_detections_queue.get()
# -------------------------------------------
fps = self.fps_queue.get()
if frame_resized is not None:
image = darknet.draw_boxes(detections, frame_resized,
self.class_colors)
# -------------------------------------------
# face_image = self.frame_queue_face.get()
# face_image = darknet.draw_faces(face_detections, face_image, (252, 240, 2))
# -------------------------------------------
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# if self.output_video is not None:
# video.write(image)
self.drew_images.put(image)
# -------------------------------------------
# self.face_image_queue.put(face_image)
# cv2.imshow('face recognizer', face_image)
# -------------------------------------------
if cv2.waitKey(fps) == 27:
break
# video.release()
cv2.destroyAllWindows()
def drawing(frame_queue, detections_queue, fps_queue):
random.seed(3) # deterministic bbox colors
video = set_saved_video(cap, args.out_filename, (width, height))
while cap.isOpened():
# ret, orignal_frame = cap.read()
frame_resized = frame_queue.get()
orignal_frame = orig_frame_queue.get()
detections = detections_queue.get()
# fps = fps_queue.get()
if frame_resized is not None:
# print('rfr', frame_width, frame_height)
image = darknet.draw_boxes(detections, frame_resized,
orignal_frame, class_colors)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# print(image.shape)
# image = cv2.resize(image, (1056, 720))
if args.out_filename is not None:
video.write(image)
if not args.dont_show:
cv2.imshow('Inference', image)
if cv2.waitKey(1) == 27:
break
cap.release()
video.release()
cv2.destroyAllWindows()
def drawing(frame_queue, detections_queue, fps_queue, curruntPointList, buffer_queue):
random.seed(3) # deterministic bbox colors
video = set_saved_video(cap, args.out_filename, (width, height))
while cap.isOpened():
frame_resized = frame_queue.get()
detections = detections_queue.get()
fps = fps_queue.get()
if frame_resized is not None:
## loop every box:
print("\n\n\n\n\n>>>my data process begin here:")
curruntPointList.clear()
for detection in detections:
point = POINT(detection[2][0], detection[2][1],
detection[2][2] * detection[2][3])
curruntPointList.append(point)
# print("\n" + str(detection[2][0]))
## loop end & point processing
pointProcessing(curruntPointList, buffer_queue)
###########################
image = darknet.draw_boxes(detections, frame_resized, class_colors)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
if args.out_filename is not None:
video.write(image)
if not args.dont_show:
cv2.imshow('Inference', image)
if cv2.waitKey(fps) == 27:
break
cap.release()
video.release()
cv2.destroyAllWindows()
def get_frame(frame_resized, detections):
if frame_resized is not None:
frame = darknet.draw_boxes(detections, frame_resized, class_colors)
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
ret, buffer = cv2.imencode('.jpg', frame)
frame = buffer.tobytes()
return frame
def drawbox(img, bboxes):
rdict, image, image_resized, predictor = Detector(img, bboxes)
types = []
if len(rdict["vests"]) > 0:
x11, y11, x21, y21 = rdict["vests"]
x11, y11, x21, y21 = box_float2int(x11, y11, x21, y21)
types.append(["SP1003"])
if len(rdict["nomask"]) > 0:
x12, y12, x22, y22 = rdict["nomask"]
x12, y12, x22, y22 = box_float2int(x12, y12, x22, y22)
types.append(["SP1001"])
# if len(rdict["mask"]) > 0 :
# x13,y13,x23,y23 = rdict["mask"]
# x13 = int(x13)
# y13 = int(y13)
# x23 = int(x23)
# y23 = int(y23)
# cv2.rectangle(image, (x13, y13), (x23, y23), (0, 0, 255), 2)
# types.append(["SP1009"])
image = darknet.draw_boxes(predictor, image_resized, class_colors)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
return image, types
def image_detection(image_path, network, class_names, class_colors, thresh):
# Darknet doesn't accept numpy images.
# Create one with image we reuse for each detect
width = darknet.network_width(network)
height = darknet.network_height(network)
darknet_image = darknet.make_image(width, height, 3)
image = cv2.imread(image_path)
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image_resized = cv2.resize(image_rgb, (width, height),
interpolation=cv2.INTER_LINEAR)
darknet.copy_image_from_bytes(darknet_image, image_resized.tobytes())
detections = darknet.detect_image(network,
class_names,
darknet_image,
thresh=thresh)
new_dets = []
for det in detections:
new_x1 = det[2][0] * image.shape[1] / width
new_y1 = det[2][1] * image.shape[0] / height
new_x2 = det[2][2] * image.shape[1] / width
new_y2 = det[2][3] * image.shape[0] / height
new_det = (new_x1, new_y1, new_x2, new_y2)
new_dets.append((det[0], det[1], new_det))
darknet.free_image(darknet_image)
image = darknet.draw_boxes(new_dets, image, class_colors)
return cv2.cvtColor(image, cv2.COLOR_BGR2RGB), new_dets
def drawing(frame_queue, detections_queue, fps_queue):
random.seed(3) # deterministic bbox colors
video = set_saved_video(cap, args.out_filename, (width, height))
while cap.isOpened():
try:
frame_resized = frame_queue.get(timeout=1)
detections = detections_queue.get(timeout=1)
fps = fps_queue.get()
if frame_resized is not None:
# detect and draw lines
bgr = cv2.cvtColor(frame_resized, cv2.COLOR_RGB2BGR)
res = imageProcessing(bgr)
frame_resized = cv2.cvtColor(res, cv2.COLOR_BGR2RGB)
image = darknet.draw_boxes(detections, frame_resized,
class_colors, width, height)
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
if args.out_filename is not None:
video.write(image)
if not args.dont_show:
cv2.namedWindow('Inference', cv2.WINDOW_NORMAL)
cv2.imshow('Inference', image)
if cv2.waitKey(1) == 27:
break
except:
cap.release()
video.release()
cv2.destroyAllWindows()
return
def drawing(frame_queue, detections_queue, fps_queue):
random.seed(3) # deterministic bbox colors
original_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
original_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
video = set_saved_video(cap, args.out_filename,
(original_width, original_height))
nth_frame = 0
while cap.isOpened():
print("processing frame", nth_frame)
frame = frame_queue.get()
detections = detections_queue.get()
fps = fps_queue.get()
if frame is not None:
image = darknet.draw_boxes(detections, frame.copy(), class_colors)
#image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
if args.out_filename is not None:
video.write(image)
if args.output_prefix is not None and len(detections) != 0:
cv2.imwrite(
args.output_prefix + str(nth_frame) + "_inference.jpg",
image)
cv2.imwrite(args.output_prefix + str(nth_frame) + ".jpg",
frame)
nth_frame += 1
if not args.dont_show:
cv2.imshow('Inference', image)
if cv2.waitKey(fps) == 27:
break
cap.release()
video.release()
cv2.destroyAllWindows()
def batch_detection(network,
images,
class_names,
class_colors,
thresh=0.25,
hier_thresh=.5,
nms=.45,
batch_size=4):
image_height, image_width, _ = check_batch_shape(images, batch_size)
darknet_images = prepare_batch(images, network)
batch_detections = darknet.network_predict_batch(network, darknet_images,
batch_size, image_width,
image_height, thresh,
hier_thresh, None, 0, 0)
batch_predictions = []
for idx in range(batch_size):
num = batch_detections[idx].num
detections = batch_detections[idx].dets
if nms:
darknet.do_nms_obj(detections, num, len(class_names), nms)
predictions = darknet.remove_negatives(detections, class_names, num)
images[idx] = darknet.draw_boxes(predictions, images[idx],
class_colors)
batch_predictions.append(predictions)
darknet.free_batch_detections(batch_detections, batch_size)
return images, batch_predictions
def drawing(frame_queue, detections_queue, fps_queue):
random.seed(3) # deterministic bbox colors
video = set_saved_video(cap, args.out_filename, (width, height))
while cap.isOpened():
frame_resized = frame_queue.get()
detections = detections_queue.get()
fps = fps_queue.get()
if frame_resized is not None:
image = darknet.draw_boxes(detections, frame_resized, class_colors)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
#預設剪中間背景當人臉
face = image[160:390, 200:430]
#如果有人臉座標就取代預設背景
if detections:
for label, confidence, bbox in detections:
left, top, right, bottom = darknet.bbox2points(bbox)
face = image[top:bottom, left:right]
#if args.out_filename is not None:
#video.write(image)
if not args.dont_show:
cv2.imshow('Inference', image)
cv2.imshow('face', face)
if cv2.waitKey(fps) == 27:
break
cap.release()
video.release()
cv2.destroyAllWindows()
def image_detection(image_path, network, class_names, class_colors, thresh):
# Darknet doesn't accept numpy images.
# Create one with image we reuse for each detect
width = darknet.network_width(network)
height = darknet.network_height(network)
darknet_image = darknet.make_image(width, height, 3)
image = cv2.imread(image_path)
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image_resized = cv2.resize(image_rgb, (width, height),
interpolation=cv2.INTER_LINEAR)
darknet.copy_image_from_bytes(darknet_image, image_resized.tobytes())
detections = darknet.detect_image(network, class_names, darknet_image, thresh=thresh)
image = darknet.draw_boxes(detections, image_resized, class_colors)
return cv2.cvtColor(image, cv2.COLOR_BGR2RGB), detections
if class_names == "fresh":
fresh = 0
if class_names == "ripe":
ripe = 0
if class_names == "raw":
raw = 0
if class_names == "flowering":
flowering = 0
if class_names == "alternaria":
alternaria = 0
if class_names == "cedar":
cedar = 0
if class_names == "fire-blight":
fire_blight = 0
if class_names == "leaf-roller":
leaf_roller = 0
if class_names == "fungal":
fungal = 0
def image_detection(image_path, network, class_names, class_colors, thresh):
# Darknet doesn't accept numpy images.
# Create one with image we reuse for each detect
width = darknet.network_width(network)
height = darknet.network_height(network)
darknet_image = darknet.make_image(width, height, 3)
image = cv2.imread(image_path)
original_width = image.shape[1]
original_height = image.shape[0]
x_scale = original_width / width
y_scale = original_height / height
image_rgb = cv2.cvtColor(image.copy(), cv2.COLOR_BGR2RGB)
image_resized = cv2.resize(image_rgb, (width, height),
interpolation=cv2.INTER_LINEAR)
darknet.copy_image_from_bytes(darknet_image, image_resized.tobytes())
detections = darknet.detect_image(network, class_names, darknet_image, thresh=thresh)
if len(detections) == 0:
return image, detections
temp_detections = list()
for i in range(len(detections)):
x_detection = detections[i][2][0] * x_scale
y_detection = detections[i][2][1] * y_scale
w_detection = detections[i][2][2] * x_scale
h_detection = detections[i][2][3] * y_scale
temp_detections.append([detections[i][0], detections[i][1], (x_detection, y_detection, w_detection, h_detection)])
detections = temp_detections
image = darknet.draw_boxes(detections, image, class_colors)
return image, detections
def drawing(frame_queue, detections_queue, fps_queue):
random.seed(3) # deterministic bbox colors
video = set_saved_video(cap, args.out_filename,
(darknet_width, darknet_height))
while cap.isOpened():
frame = frame_queue.get()
detections = detections_queue.get()
fps = fps_queue.get()
detections_adjusted = []
if frame is not None:
for label, confidence, bbox in detections:
bbox_adjusted = convert2original(frame, bbox)
detections_adjusted.append(
(str(label), confidence, bbox_adjusted))
image = darknet.draw_boxes(detections_adjusted, frame,
class_colors)
if not args.dont_show:
cv2.imshow('Inference', image)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
if args.out_filename is not None:
video.write(image)
if cv2.waitKey(fps) == 27:
break
cap.release()
video.release()
cv2.destroyAllWindows()
def draw_detections(self,
detections,
frame,
convert_to_rgb=True,
classes=[]):
""" Draw the detections detected on the frame.
Args:
detections (list): All the detections in this format [(class, conf, [x, y, w, h]), ...].
frame (numpy.ndarray): The frame to draw the detections on.
convert_to_rgb (bool, optional): Whether to convert the frame to RGB. Defaults to True.
classes (list, optional): Limit the detections to these class names. Defaults to [].
Returns:
numpy.ndarray: The given frame with detections (BBs) on it.
"""
if classes != []:
for i, det in enumerate(detections):
class_name = det[0]
if class_name not in classes:
del detections[i]
image = darknet.draw_boxes(detections, frame, self.class_colors)
if convert_to_rgb:
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
return image
def callback(self, msg):
begin = time.clock()
frame = self.bridge.imgmsg_to_cv2(msg, "bgr8")
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
#cv2.imshow("frame_rgb", frame_rgb)
frame_resized = cv2.resize(frame_rgb, (self.width, self.height),
interpolation=cv2.INTER_LINEAR)
#cv2.imshow("frame_resized", frame_resized)
img_for_detect = darknet.make_image(self.width, self.height, 3)
darknet.copy_image_from_bytes(img_for_detect, frame_resized.tobytes())
#print(img_for_detect.__dict__)
detections = darknet.detect_image(self.network,
self.class_names,
img_for_detect,
thresh=self.thresh)
#print(detections)
image = darknet.draw_boxes(detections, frame_resized,
self.class_colors)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
end = time.clock()
print(end - begin)
cv2.imshow('Inference', image)
cv2.imshow("Image Window", frame)
cv2.waitKey(3)
def drawing(self, imgQue, detQue, fpsQue, msgQue):
random.seed(3) # deterministic bbox colors
self.window = cv2.namedWindow("Inference", cv2.WINDOW_NORMAL)
cv2.moveWindow("Inference", 200, 2000)
cv2.resizeWindow("Inference", self.height, self.width)
if self.SAVEVID:
fourcc = cv2.VideoWriter_fourcc(*"XVID")
vid = cv2.VideoWriter('output.avi', fourcc, 20.0,
(self.height, self.width))
while self.RUNNING:
frame = imgQue.get()
dets = detQue.get()
fps = fpsQue.get()
if frame is not None:
#print(dets)
image = darknet.draw_boxes(dets, frame, self.classColors)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = cv2.resize(image, (self.height, self.width))
#if self.args.display:
if self.SAVEVID:
vid.write(image)
cv2.imshow('Inference', image)
if cv2.waitKey(fps) == 27:
print("shutting down")
msgQue.put(False)
#vid.release()
self.shutdown()
vid.release()
cv2.destroyAllWindows()
print("Drawing completely done")
def drawing(frame_queue, detections_queue, fps_queue):
"""
drawing bbox on the image and writing results video file or show video image.
"""
# so we could release it if a signal is given
global video
# deterministic bbox colors
random.seed(3)
# results video file
filename = args.out_filename
# each time will open a new out file
if args.out_filename:
filename_split = args.out_filename.rsplit(".", 1)
index = 0
while 1:
# save file: name_<index>.mp4
filename = filename_split[0] + '_' + \
str(index) + '.' + filename_split[1]
# file not exists
if not os.path.isfile(filename):
break
# trying next index
index += 1
# result video obj
video = set_saved_video(
cap, filename, (args.capture_frame_width, args.capture_frame_height))
while cap.isOpened():
frame_resized = frame_queue.get()
detections = detections_queue.get()
fps = fps_queue.get()
if frame_resized is not None:
# draw detection bounding boxs on image
image = darknet.draw_boxes(detections, frame_resized, class_colors)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = cv2.resize(
image, (args.capture_frame_width, args.capture_frame_height),
interpolation=cv2.INTER_LINEAR)
# writing video image
if args.out_filename is not None:
video.write(image)
# show video image
if not args.dont_show:
cv2.imshow('Inference', image)
# Esc key to stop GUI
if cv2.waitKey(fps) == 27:
break
# Closes video file or capturing device
cap.release()
# Closes video write file
video.release()
# destroys all of the opened HighGUI windows
cv2.destroyAllWindows()
if args.out_filename:
print("\nOut file: {}".format(filename))
def image_detection(self,
image_bgr,
network,
class_names,
class_colors,
thresh=0.25):
# 判断输入图像是否为3通道
if len(image_bgr.shape) == 2:
image_bgr = np.stack([image_bgr] * 3, axis=-1)
# 获取原始图像大小
orig_h, orig_w = image_bgr.shape[:2]
width = darknet.network_width(network)
height = darknet.network_height(network)
darknet_image = darknet.make_image(width, height, 3)
# image = cv2.imread(image_path)
image_rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)
image_resized = cv2.resize(image_rgb, (width, height),
interpolation=cv2.INTER_LINEAR)
darknet.copy_image_from_bytes(darknet_image, image_resized.tobytes())
detections = darknet.detect_image(network,
class_names,
darknet_image,
thresh=thresh)
darknet.free_image(darknet_image)
'''注意：这里原始代码依旧是608*608，而不是原图大小，因此我们需要转换'''
new_detections = []
for detection in detections:
pred_label, pred_conf, (x, y, w, h) = detection
new_x = x / width * orig_w
new_y = y / height * orig_h
new_w = w / width * orig_w
new_h = h / height * orig_h
# 可以约束一下
(x1, y1, x2, y2) = self.bbox2point((new_x, new_y, new_w, new_h))
x1 = x1 if x1 > 0 else 0
x2 = x2 if x2 < orig_w else orig_w
y1 = y1 if y1 > 0 else 0
y2 = y2 if y2 < orig_h else orig_h
(new_x, new_y, new_w, new_h) = self.point2bbox((x1, y1, x2, y2))
new_detections.append(
(pred_label, pred_conf, (new_x, new_y, new_w, new_h)))
image = darknet.draw_boxes(new_detections, image_rgb, class_colors)
return cv2.cvtColor(image, cv2.COLOR_RGB2BGR), new_detections
def pipeline(img):
# image data transform
# img - cv image
# im - yolo image
im, image = dn.array_to_image(img)
dn.rgbgr_image(im)
tic = time.time()
result = detect2(net, meta, im)
toc = time.time()
print(toc - tic, result)
img_final = dn.draw_boxes(img, result)
return img_final
def clickAndRemove(event, x, y, flags, param):
global detections, img, class_colors, orig_det
image = img.copy()
#clicking on a detection with the left button means remove it because it's wrong
if event == cv2.EVENT_LBUTTONDOWN:
new_det = []
for d in detections:
if x >= d[2][0] - d[2][2] / 2. and x <= d[2][
0] + d[2][2] / 2. and y >= d[2][
1] - d[2][3] / 2. and y <= d[2][1] + d[2][3] / 2.:
print(x, y)
else:
new_det.append(d)
detections = new_det
image = darknet.draw_boxes(detections, image, class_colors)
cv2.imshow('image', image)
#a click with the right button will restore the original detections of the net (in case of user mistake)
if event == cv2.EVENT_RBUTTONDOWN:
detections = copy.deepcopy(orig_det)
image = darknet.draw_boxes(detections, image, class_colors)
cv2.imshow('image', image)
def image_detection(image_path, network, class_names, class_colors, thresh):
# Darknet doesn't accept numpy images.
# Create one with image we reuse for each detect
width = darknet.network_width(network)
height = darknet.network_height(network)
darknet_image = darknet.make_image(width, height, 3)
image = cv2.imread(image_path)
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image_resized = cv2.resize(image_rgb, (width, height),
interpolation=cv2.INTER_LINEAR)
darknet.copy_image_from_bytes(darknet_image, image_resized.tobytes())
detections = darknet.detect_image(network, class_names, darknet_image, thresh=thresh)
darknet.free_image(darknet_image)
image = darknet.draw_boxes(detections, image_resized, class_colors)
return cv2.cvtColor(image, cv2.COLOR_BGR2RGB), detections
def image_detection(image, network, class_names, class_colors, thresh):
width = darknet.network_width(network)
height = darknet.network_height(network)
darknet_image = darknet.make_image(width, height, 3)
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image_resized = cv2.resize(image_rgb, (width, height),
interpolation=cv2.INTER_LINEAR)
darknet.copy_image_from_bytes(darknet_image, image_resized.tobytes())
detections = darknet.detect_image(network,
class_names,
darknet_image,
thresh=thresh)
darknet.free_image(darknet_image)
image = darknet.draw_boxes(detections, image_resized, class_colors)
return cv2.cvtColor(image, cv2.COLOR_BGR2RGB), detections
def detect_image(image=cv2.imread('data/dog.jpg')):
darknet_image = darknet.make_image(resize_width, resize_height, 3)
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image_resized = cv2.resize(image_rgb, (resize_width, resize_height),
interpolation=cv2.INTER_LINEAR)
darknet.copy_image_from_bytes(darknet_image, image_resized.tobytes())
detections = darknet.detect_image(network,
class_names,
darknet_image,
thresh=thresh)
darknet.free_image(darknet_image)
image = darknet.draw_boxes(detections, image_resized, class_colors)
image = image_resized
return cv2.cvtColor(image, cv2.COLOR_RGB2BGR), detections
def drawing(self):
random.seed(3) # deterministic bbox colors
#video = set_saved_video(cap, args.out_filename, (width, height))
while self.cap.isOpened():
frame_resized = self.frame_queue.get()
detections = self.detections_queue.get()
fps = self.fps_queue.get()
if frame_resized is not None:
image = darknet.draw_boxes(detections, frame_resized, self.class_colors)
#image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
#if args.out_filename is not None:
#video.write(image)
#if not args.dont_show:
#cv2.imshow('Inference', image)
self.YOLO_image_queue.put(image) #把RGB圖片存起來
if cv2.waitKey(fps) == 27:
break
self.cap.release()
def image_detection_lime(image, network, class_names, class_colors, thresh):
# Basically same as image_detection, but we get the image as a numpy array
#print("entrou image detection LIME")
width = darknet.network_width(network)
height = darknet.network_height(network)
darknet_image = darknet.make_image(width, height, 3)
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image_resized = cv2.resize(image_rgb, (width, height),
interpolation=cv2.INTER_LINEAR)
darknet.copy_image_from_bytes(darknet_image, image_resized.tobytes())
detections, detection_prob_list = darknet.detect_image_lime(network,
class_names,
darknet_image,
thresh=thresh)
darknet.free_image(darknet_image)
image = darknet.draw_boxes(detections, image_resized, class_colors)
return cv2.cvtColor(image, cv2.COLOR_BGR2RGB), detections
def drawing(frame_queue, detections_queue, fps_queue):
random.seed(3) # deterministic bbox colors
video = set_saved_video(cap, args.out_filename, (width, height))
while cap.isOpened():
frame_resized = frame_queue.get()
detections = detections_queue.get()
fps = fps_queue.get()
if frame_resized is not None:
image = darknet.draw_boxes(detections, frame_resized, class_colors)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
if args.out_filename is not None:
video.write(image)
if not args.dont_show:
cv2.imshow('Inference', image)
if cv2.waitKey(fps) == 27:
break
cap.release()
video.release()
cv2.destroyAllWindows()
def image_detection(image_path, network, class_names, class_colors, thresh):
# Darknet doesn't accept numpy images.
# Create one with image we reuse for each detect
width = darknet.network_width(network)
height = darknet.network_height(network)
darknet_image = darknet.make_image(width, height, 3)
image = cv2.imread(image_path)
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image_resized = cv2.resize(image_rgb, (width, height),
interpolation=cv2.INTER_LINEAR)
darknet.copy_image_from_bytes(darkmask: 99% (left_x: 43 top_y: 28 width: 40 height: 39)
mask: 100% (le< /home/fadi/다운로드/Yolo_mark/x64/Release/data/train.txt >ft_x: 134 top_y: 24 width: 32 height: 34)
mask: 99% (left_x: 205 top_y: 20 width: 35 height: 41)net_image, image_resized.tobytes())
detections = darknet.detect_image(network, class_names, darknet_image, thresh=thresh)
darknet.free_image(darknet_image)
image = darknet.draw_boxes(detections, image_resized, class_colors)
return cv2.cvtColor(image, cv2.COLOR_BGR2RGB), detections
def image_detection(image_path, network, class_names, class_colors, thresh):
# Darknet doesn't accept numpy images.
# Create one with image we reuse for each detect
width = darknet.network_width(network)
height = darknet.network_height(network)
darknet_image = darknet.make_image(width, height, 3)
image = cv2.imread(image_path)
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image_resized = cv2.resize(image_rgb, (width, height),
interpolation=cv2.INTER_LINEAR)
darknet.copy_image_from_bytes(darknet_image, image_resized.tobytes())
detections = darknet.detect_image(network,
class_names,
darknet_image,
thresh=thresh)
# Setup BCM Mode
GPIO.setmode(GPIO.BCM)
# Setup pin number
pinGreen = 18 #Green led
pinRed = 24 #Red led
# Setup the pin as output direction
valueLow = GPIO.LOW
valueHigh = GPIO.HIGH
GPIO.setup(pinGreen, GPIO.OUT)
GPIO.setup(pinRed, GPIO.OUT)
if (detections[0][0] == "with_mask"):
GPIO.output(pinGreen, valueHigh)
GPIO.output(pinRed, valueLow)
else:
GPIO.output(pinRed, valueHigh)
GPIO.output(pinGreen, valueLow)
# Clean it up
GPIO.cleanup()
darknet.free_image(darknet_image)
image = darknet.draw_boxes(detections, image_resized, class_colors)
return cv2.cvtColor(image, cv2.COLOR_BGR2RGB), detections
def __image_detection(self, image, network, class_names, class_colors, thresh):
width = darknet.network_width(network)
self.__logger.info('Network width calculated')
height = darknet.network_height(network)
self.__logger.info('Network hight calculated')
darknet_image = darknet.make_image(width, height, 3)
self.__logger.info('Darknet image produced')
image_rgb = image
image_resized = cv2.resize(image_rgb, (width, height),
interpolation=cv2.INTER_LINEAR)
self.__logger.info('Image resized')
darknet.copy_image_from_bytes(darknet_image, image_resized.tobytes())
self.__logger.info('Image copyed')
detections = darknet.detect_image(network, class_names, darknet_image, thresh=thresh)
self.__logger.info('Detection completed')
darknet.free_image(darknet_image)
self.__logger.info('Image freed')
image = darknet.draw_boxes(detections, image_resized, class_colors)
self.__logger.info('Boxes drawn')
return cv2.cvtColor(image, cv2.COLOR_BGR2RGB), detections
def _image_detection(self, image, thresh):
# Darknet doesn't accept numpy images.
# Create one with image we reuse for each detect
width = darknet.network_width(self._network)
height = darknet.network_height(self._network)
logger.debug('Darknet network width=%s height=%s', width, height)
darknet_image = darknet.make_image(width, height, 3)
logger.debug('Generating RGB resized image for Darknet')
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image_resized = cv2.resize(image_rgb, (width, height),
interpolation=cv2.INTER_LINEAR)
logger.debug('Copying image to darknet')
darknet.copy_image_from_bytes(darknet_image, image_resized.tobytes())
logger.debug('Running darknet.detect_image()')
with suppress_stdout_stderr(suppress=not self.debug):
detections = darknet.detect_image(
self._network, self._names, darknet_image, thresh=thresh
)
darknet.free_image(darknet_image)
logger.info('Darknet result: %s', detections)
image = darknet.draw_boxes(detections, image_resized, self._colors)
return cv2.cvtColor(image, cv2.COLOR_BGR2RGB), detections
