def findObject():
f = open('/home/ubuntu/object_find.txt', 'r')
object_name = f.readlines()
print('object_name[0]: ' + object_name[0])
f.close()
#tmp_file = request.files['abc']
#tmp_file.save('/home/ubuntu/tmp.jpg')
get_frame('extra/yolo.jpg', False)
result = detect_image('/home/ubuntu/IOT_WEB_SERVER/static/extra/yolo.jpg')
print("result : " + str(result))
str_result = ''
for i in result:
str_result = str_result + translateEtoK.get(i['class'], '') + ' '
print(str_result)
if object_name[0] in str_result:
print("물건을 찾은 경우입니다 ###########")
str_result = "전방에 " + str(object_name[0]) + " 찾았습니다"
else:
print("물건을 찾지 못한 경우입니다 ################")
print(str(object_name[0]) + " " + str_result)
str_result = "0" # 찾는 물품이 없을경우 0을 전달함으로써 아틱이 식별할 수 있게끔 해줌
return Response(str_result, status=200, mimetype='text/plain')
def detect_img(yolo, length, image, path):
for i in range(length):
try:
img = Image.open(path[i])
except:
print('Open Error! Try again!')
continue
else:
r_image = yolo.detect_image(img, image[i])
# cv2.imwrite("images/"+image[i], np.asarray(r_image)[..., ::-1])
yolo.yolo.close_session()
def detect_img(yolo, images_path):
for image_path in images_path:
try:
image = Image.open(image_path)
except:
print('Open Error! Try again!')
else:
r_image = yolo.detect_image(image)
result = np.asarray(r_image)
cv2.namedWindow("result", cv2.WINDOW_NORMAL)
cv2.imshow("result", result)
cv2.waitKey(0)
cv2.destroyAllWindows()
def image():
get_frame('extra/yolo.jpg', False)
result = detect_image('/home/ubuntu/IOT_WEB_SERVER/static/extra/yolo.jpg')
if not result:
str_result = '인식된 주요 물체가 없습니다'
else:
print("result" + str(result))
str_result = ''
for i in result:
str_result = str_result + translateEtoK.get(i['class'], '') + ' '
print(str_result)
str_result = removeDuplicates(str_result)
str_result = str_result + '있습니다'
print(str_result)
return Response(str_result, status=200, mimetype='text/plain')
def detect_img(yolo, images_path):
accum_time = 0
curr_fps = 0
fps = "FPS: ??"
prev_time = timer()
stop_flag = False
while True:
for image_path in images_path:
try:
image = Image.open(image_path)
except:
print('Open Error! Try again!')
else:
image = yolo.detect_image(image)
result = np.asarray(image)
curr_time = timer()
exec_time = curr_time - prev_time
prev_time = curr_time
accum_time = accum_time + exec_time
curr_fps = curr_fps + 1
if accum_time > 1:
accum_time = accum_time - 1
fps = "FPS: " + str(curr_fps)
curr_fps = 0
cv2.putText(result,
text=fps,
org=(3, 15),
fontFace=cv2.FONT_HERSHEY_SIMPLEX,
fontScale=0.50,
color=(255, 0, 0),
thickness=2)
cv2.namedWindow("result", cv2.WINDOW_NORMAL)
cv2.imshow("result", result)
k = cv2.waitKey(33)
if k == 27: # Esc key to stop
stop_flag = True
break
elif k == -1: # normally -1 returned,so don't print it
continue
else:
print(k) # else print its value
if (stop_flag is True):
break
cv2.destroyAllWindows()
def main(yolo):
start = time.time()
#Definition of the parameters
max_cosine_distance = 0.5 #余弦距离的控制阈值
nn_budget = None
nms_max_overlap = 0.3 #非极大抑制的阈值
counter = []
#deep_sort
# model_filename = 'model_data/market1501.pb'
model_filename = 'model_data/mars-small128.pb'
encoder = gdet.create_box_encoder(model_filename, batch_size=1)
metric = nn_matching.NearestNeighborDistanceMetric("cosine",
max_cosine_distance,
nn_budget)
tracker = Tracker(metric)
video_capture = cv2.VideoCapture(args["input"])
fps = 0.0
while True:
ret, frame = video_capture.read() # frame shape 640*480*3
if ret != True:
break
t1 = time.time()
boxs, class_names = yolo.detect_image(frame)
features = encoder(frame, boxs)
detections = [
Detection(bbox, 1.0, feature)
for bbox, feature in zip(boxs, features)
]
# Run non-maxima suppression.
# boxes = np.array([d.tlwh for d in detections])
# scores = np.array([d.confidence for d in detections])
# indices = preprocessing.non_max_suppression(boxes, nms_max_overlap, scores)
# detections = [detections[i] for i in indices]
# detections = detections[:]
# Call the tracker
tracker.predict()
tracker.update(detections)
i = int(0)
indexIDs = []
# for det in detections:
# bbox = det.to_tlbr()
# cv2.rectangle(frame,(int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])),(255,255,255), 2)
# print(tracker.tracks)
for track in tracker.tracks:
if not track.is_confirmed() or track.time_since_update > 1:
continue
#boxes.append([track[0], track[1], track[2], track[3]])
indexIDs.append(int(track.track_id))
counter.append(int(track.track_id))
bbox = track.to_tlbr()
color = [int(c) for c in COLORS[indexIDs[i] % len(COLORS)]]
cv2.rectangle(frame, (int(bbox[0]), int(bbox[1])),
(int(bbox[2]), int(bbox[3])), (color), 3)
cv2.putText(frame, str(track.track_id),
(int(bbox[0]), int(bbox[1] - 50)), 0, 5e-3 * 150,
(color), 2)
if len(class_names) > 0:
class_name = class_names[0]
cv2.putText(frame, str(class_names[0]),
(int(bbox[0]), int(bbox[1] - 20)), 0, 5e-3 * 150,
(color), 2)
i += 1
#bbox_center_point(x,y)
center = (int(
((bbox[0]) + (bbox[2])) / 2), int(((bbox[1]) + (bbox[3])) / 2))
#track_id[center]
pts[track.track_id].append(center)
thickness = 5
#center point
cv2.circle(frame, (center), 1, color, thickness)
#draw motion path
for j in range(1, len(pts[track.track_id])):
if pts[track.track_id][j - 1] is None or pts[
track.track_id][j] is None:
continue
thickness = int(np.sqrt(64 / float(j + 1)) * 2)
cv2.line(frame, (pts[track.track_id][j - 1]),
(pts[track.track_id][j]), (color), thickness)
#cv2.putText(frame, str(class_names[j]),(int(bbox[0]), int(bbox[1] -20)),0, 5e-3 * 150, (255,255,255),2)
count = len(set(counter))
fps = (fps + (1. / (time.time() - t1))) / 2
cv2.putText(frame, "Total Object Counter: " + str(count),
(int(20), int(120)), 0, 5e-3 * 200, (0, 255, 0), 2)
cv2.putText(frame, "Current Object Counter: " + str(i),
(int(20), int(80)), 0, 5e-3 * 200, (0, 255, 0), 2)
cv2.putText(frame, "FPS: %f" % (fps), (int(20), int(40)), 0,
5e-3 * 200, (0, 255, 0), 3)
cv2.namedWindow("YOLOv4_Deep_SORT", 0)
cv2.resizeWindow('YOLOv4_Deep_SORT', 1024, 768)
cv2.imshow('YOLOv4_Deep_SORT', frame)
# Press Q to stop!
if cv2.waitKey(1) & 0xFF == ord('q'):
break
print(" ")
print("[Finish]")
end = time.time()
if len(pts[track.track_id]) != None:
print(args["input"][43:57] + ": " + str(count) + " " +
str(class_name) + ' Found')
else:
print("[No Found]")
video_capture.release()
cv2.destroyAllWindows()
#!/usr/bin/env python
# -*- coding:utf-8 -*-
# author : kly time:2019/4/14
import yolo
import os
from PIL import Image
import glob
if __name__ == '__main__':
yolo = yolo.YOLO()
path = "D:\work//new\Test\INA-T//*.jpg"
outdir = "D:\work//new\Test\out"
for jpgfile in glob.glob(path):
img = Image.open(jpgfile)
num = jpgfile[-10:-4]
print(num)
img = yolo.detect_image(img, num)
#img.save(os.path.join(outdir, os.path.basename(jpgfile)))
print('ok')
yolo.close_session()
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
# My kingdom for a good redistributable image drawing library.
for i in range(thickness):
draw.rectangle([left + i, top + i, right - i, bottom - i],
outline=self.colors[c])
draw.rectangle(
[tuple(text_origin),
tuple(text_origin + label_size)],
fill=self.colors[c])
draw.text(text_origin, label, fill=(0, 0, 0), font=font)
del draw
end = timer()
print(end - start)
return image
def close_session(self):
self.sess.close()
if __name__ == "__main__":
path = "d:/pics/test.jpg"
fra = cv2.imread(path)
img = Image.fromarray(fra)
image = yolo.detect_image(img)
cv2.imwrite("d:/pics/result.jpg", image)
import yolo
from PIL import Image
if __name__ == '__main__':
image_path = "./1.jpg"
image = Image.open(image_path)
yolo = yolo.YOLO()
r_image = yolo.detect_image(image=image)
r_image.show()