def main(args):
img = misc.imread(args[1])
predictions = detect_number(img)
if predictions == None or predictions == []:
print("NONE")
sys.exit(0)
best_prediction = find_best_prediction(predictions)
labels, bboxes, probs = best_prediction
for i in range(len(labels)):
draw_bounding_box(img, bboxes[i], RED)
if "/" in args[1]:
st = args[1].index("/")
else:
st = 0
dt = args[1].index(".")
result_name = args[1][st+1:dt]
misc.imsave("output/detected_" + result_name + ".png", img)
out_file = open("output/result_" + result_name + ".txt", "w")
out_file.write(''.join(labels))
out_file.write("\n" + str(list(np.round(probs, 2))))
print("Detected licence plate number: ", ''.join(labels))
Ta.append(T_v)
sa.append(speed_g[frame_itt + delta_fr])
angles_objects = []
boxes_main = []
time_start_itt = time.time()
boxes_out = nn.get_features(img1)
tt_nn.append(time_tressing(time_start_itt, '\t\tneural network'))
t0 = time.time()
if debug:
out = img1.copy()
for box in boxes_out:
cl_name = box[0]
pt1 = box[1]
pt2 = box[2]
if cl_name in vehicles_classes:
out = draw.draw_bounding_box(out, cl_name, draw.red, pt1, pt2,
3)
else:
out = draw.draw_bounding_box(out, cl_name, draw.blue, pt1, pt2,
3)
tt_draw_rec.append(time_tressing(t0, '\t\tbounding box drawing'))
t0 = time.time()
for box in boxes_out:
cl_name = box[0]
if cl_name in vehicles_classes:
angles_objects.append(
np.arctan2(-pai_in_frame[1] + box[2][1],
-pai_in_frame[0] + box[1][0]))
angles_objects.append(
np.arctan2(-pai_in_frame[1] + box[2][1],
-pai_in_frame[0] + box[2][0]))
boxes_main.append(box)
class_ids.append(class_id)
confidences.append(float(confidence))
boxes.append([x, y, w, h])
# apply non-max suppression
indices = cv2.dnn.NMSBoxes(boxes, confidences, conf_threshold, nms_threshold)
# go through the detections remaining
# after nms and draw bounding box
boxes_out = []
for ind in indices:
i_ = ind[0]
box = boxes[i_]
x = box[0]
y = box[1]
w = box[2]
h = box[3]
img1 = draw.draw_bounding_box(img1, None, COLORS[class_ids[i_]], confidences[i_], round(x), round(y), round(x+w), round(y+h))
boxes_out.append([[round(x), round(y)], [round(x+w), round(y+w)]])
# print(classes[class_ids[i_]], 'was drawed border')
t0 = time.time()
pts1_tem = []
pts2_tem = []
for i in range(len(pts1)):
pt1 = pts1[i]
pt2 = pts2[i]
o = True
for box in boxes_out:
box_pt1 = box[0]
box_pt2 = box[1]
if box_pt1[0] <= pt1[0] <= box_pt2[0] and box_pt1[1] <= pt1[1] <= box_pt2[1]:
o = False
break
frameWidth = image.shape[1]
frameHeight = image.shape[0]
t_y = time.time()
output_yolo = nn.get_features(image)
times_yolo.append(time.time() - t_y)
t_op = time.time()
output_opnps = op.getPose(image, True)
pp, ll = op.getPose(image)
times_openpose.append(time.time() - t_op)
yolo_result = image.copy()
for box in output_yolo:
name = box[0]
pt1 = box[1]
pt2 = box[2]
color = box[3]
yolo_result = draw.draw_bounding_box(yolo_result, name, color, pt1,
pt2)
yolo_result = op.drawPose(yolo_result, pp, ll, 3, 1)
cv2.imwrite('output/cars&humans/{}_yolo.png'.format(itt), yolo_result)
probMap = output_opnps[0, 0, :, :]
probMap = cv2.resize(probMap, (frameWidth, frameHeight))
minM = np.min(probMap)
probMap -= minM
maxM = np.max(probMap)
k = 255 / maxM
probMap *= 255
probMap = np.uint8(probMap)
pb = np.zeros((frameHeight, frameWidth, 3), np.uint8)
# pb[:, :, 0] = probMap #blue
pb[:, :, 1] = probMap #green
# pb[:, :, 2] = probMap #red
image[:, :, 1] = 0