def predict_time(img, THRESHOLD=0.2):
boxes, probs = yolo_detector.predict(img, THRESHOLD)
image, scaled_boxes = draw_scaled_boxes(
img, boxes, probs,
["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", ":"])
detected = []
for prob in probs:
detected.append(classes[np.argmax(prob)])
final = []
for i in range(len(scaled_boxes)):
dict = {}
dict['x'] = scaled_boxes[i][0]
dict['w'] = scaled_boxes[i][1]
dict['y'] = scaled_boxes[i][2]
dict['h'] = scaled_boxes[i][3]
dict['class'] = detected[i]
final.append(dict)
final = sorted(final, key=lambda x: x['x'])
detected = [sub['class'] for sub in final]
detected = ''.join(detected)
#print('Detected: ', detected)
return detected
def get_digits_boxes_img(image):
im_size = np.shape(image)
boxes, probs = yolo.predict(image, float(threshold))
labels = np.argmax(probs, axis=1) if len(probs) > 0 else []
probs = np.max(probs, axis=1) if len(probs) > 0 else []
# 4. save detection result
image = draw_scaled_boxes(image, boxes, probs, config['model']['labels'])
centers = []
for count in range(len(boxes)):
box = boxes[count]
x = (((box[0] + box[2] / 2) / im_size[0]))
y = (((box[1] + box[3] / 2) / im_size[1]))
centers.append([x, y])
centers = np.array(centers)
jersey_numbers = []
if len(centers) > 1:
cluster.fit_predict(centers)
cluster_labels = cluster.labels_
clusters = np.unique(cluster_labels)
conf_score = []
for c_id in clusters:
g_centers = centers[list(cluster_labels == c_id)]
g_labels = labels[list(cluster_labels == c_id)]
g_probs = probs[list(cluster_labels == c_id)]
center_x = [center[0] for center in g_centers]
g_labels = g_labels[np.argsort(center_x)]
number = int((''.join(str(label) for label in g_labels)))
jersey_numbers.append(number)
conf_score.append(np.mean(g_probs))
else:
jersey_numbers = list(labels)
conf_score = [np.mean(probs)]
# print("{}-boxes are detected".format(len(boxes)))
# print("jersey numbers detected are {}".format(jersey_numbers))
return jersey_numbers, conf_score
config['model']['labels'],
is_only_detect=config['train']['is_only_detect'])
n_true_positives = 0
n_truth = 0
n_pred = 0
for i in range(len(annotations)):
img_path = annotations.fname(i)
img_fname = os.path.basename(img_path)
image = cv2.imread(img_path)
true_boxes = annotations.boxes(i)
true_labels = annotations.code_labels(i)
boxes, probs = yolo.predict(image, float(args.threshold))
labels = np.argmax(probs, axis=1) if len(probs) > 0 else []
# 4. save detection result
image = draw_scaled_boxes(image, boxes, probs,
config['model']['labels'])
output_path = os.path.join(write_dname, os.path.split(img_fname)[-1])
cv2.imwrite(output_path, image)
print("{}-boxes are detected. {} saved.".format(
len(boxes), output_path))
n_true_positives += count_true_positives(boxes, true_boxes, labels,
true_labels)
n_truth += len(true_boxes)
n_pred += len(boxes)
print(calc_score(n_true_positives, n_truth, n_pred))
# match boxes coordinate to original image
if crop_imgs.index(best_crop_img) == 0:
pass
elif crop_imgs.index(best_crop_img) == 1:
best_boxes[:, 0] = best_boxes[:, 0] + int(img.shape[1] * 0.2)
best_boxes[:, 2] = best_boxes[:, 2] + int(img.shape[1] * 0.2)
elif crop_imgs.index(best_crop_img) == 2:
best_boxes[:, 0] = best_boxes[:, 0] + int(img.shape[1] * 0.2)
best_boxes[:, 1] = best_boxes[:, 1] + int(img.shape[0] * 0.15)
best_boxes[:, 2] = best_boxes[:, 2] + int(img.shape[1] * 0.2)
best_boxes[:, 3] = best_boxes[:, 3] + int(img.shape[0] * 0.15)
# detection result
image = draw_scaled_boxes(
img, best_boxes, best_probs,
["10", "1", "2", "3", "4", "5", "6", "7", "8", "9"])
print("{}-boxes are detected.".format(len(best_boxes)))
# plt.imshow(image)
# plt.show()
# append to result list
result_dict = {"bbox": [], "label": [], "score": []}
try:
result_dict["bbox"] = bbox_format(best_boxes).tolist()
result_dict["label"] = np.argmax(np.array(best_probs), axis=1).tolist()
result_dict["score"] = np.amax(np.array(best_probs), axis=1).tolist()
except:
pass
result_list.append(result_dict)
print(result_dict)
print("pause playback")
time.sleep(0.5)
postion = []
cap = cv2.VideoCapture(0)
Connect_to_token()
creat_list()
while True:
_, frame = cap.read()
#startar detektering
boxes, probs = yolo.predict(frame, config["model"]["DEFAULT_THRESHOLD"])
labels = np.argmax(probs, axis=1) if len(probs) > 0 else []
#sparar detekterings resultat
frame = draw_scaled_boxes(frame, boxes, probs, config['model']['labels'])
label_list = config['model']['labels']
cv2.imshow("imageo", frame)
cv2.waitKey(1)
if 0 in labels: #om en tummeup hittas
try:
Add_to_list()
except:
input(
" No active device found ... press ENTER when you are back on track"
)
if 1 in labels: #om en hand hittas
try:
Change_song_puse_play()
def get_digits_boxes(img, show_img=False, save_img=False):
img_path = img_path.replace('\\', '/')
# img_path = img_path.replace('\','/')
image = cv2.imread(img_path)
im_size = np.shape(image)
boxes, probs = yolo.predict(image, float(threshold))
labels = np.argmax(probs, axis=1) if len(probs) > 0 else []
probs = np.max(probs, axis=1) if len(probs) > 0 else []
# 4. save detection result
image = draw_scaled_boxes(image, boxes, probs, config['model']['labels'])
centers = []
for count in range(len(boxes)):
box = boxes[count]
x = (((box[0] + box[2] / 2) / im_size[0]))
y = (((box[1] + box[3] / 2) / im_size[1]))
centers.append([x, y])
centers = np.array(centers)
jersey_numbers = []
if len(centers) > 1:
cluster.fit_predict(centers)
cluster_labels = cluster.labels_
clusters = np.unique(cluster_labels)
conf_score = []
for c_id in clusters:
g_centers = centers[list(cluster_labels == c_id)]
g_labels = labels[list(cluster_labels == c_id)]
g_probs = probs[list(cluster_labels == c_id)]
center_x = [center[0] for center in g_centers]
g_labels = g_labels[np.argsort(center_x)]
number = int((''.join(str(label) for label in g_labels)))
jersey_numbers.append(number)
conf_score.append(np.mean(g_probs))
else:
jersey_numbers = labels
conf_score = np.mean(probs)
numbers_string = ''
for number in jersey_numbers:
numbers_string = numbers_string + str(number) + ' '
cv2.putText(image, 'Jersey Number: %s' % numbers_string, (25, 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
# labels=labels[np.argsort(center_x)]
# number = int((''.join(str(label) for label in labels)))
if save_img:
output_path = os.path.join(
ntpath.dirname(img_path) + '/' +
(img_path.split("/")[-1]).split('.')[0] + '_out.jpg')
cv2.imwrite(output_path, image)
if show_img:
cv2.imshow("jersey number detected image", image)
cv2.waitKey(5000)
cv2.destroyAllWindows()
print("{}-boxes are detected".format(len(boxes)))
print("jersey numbers detected are {}".format(jersey_numbers))
return jersey_numbers, conf_score, labels, boxes
annotations = parse_annotation(config['train']['valid_annot_folder'],
config['train']['valid_image_folder'],
config['model']['labels'],
is_only_detect=config['train']['is_only_detect'])
n_true_positives = 0
n_truth = 0
n_pred = 0
for i in range(len(annotations)):
img_path = annotations.fname(i)
img_fname = os.path.basename(img_path)
image = cv2.imread(img_path)
true_boxes = annotations.boxes(i)
true_labels = annotations.code_labels(i)
boxes, probs = yolo.predict(image, float(args.threshold))
labels = np.argmax(probs, axis=1) if len(probs) > 0 else []
# 4. save detection result
image = draw_scaled_boxes(image, boxes, probs, config['model']['labels'])
output_path = os.path.join(write_dname, os.path.split(img_fname)[-1])
cv2.imwrite(output_path, image)
print("{}-boxes are detected. {} saved.".format(len(boxes), output_path))
n_true_positives += count_true_positives(boxes, true_boxes, labels, true_labels)
n_truth += len(true_boxes)
n_pred += len(boxes)
print(calc_score(n_true_positives, n_truth, n_pred))
#n_truth = 0
#n_pred = 0
#for i in range(len(annotations)):
for filename in os.listdir(args.path):
#img_path = annotations.fname(i)
img_path = os.path.join(args.path, filename)
#img_fname = os.path.basename(img_path)
img_fname = filename
image = cv2.imread(img_path)
#true_boxes = annotations.boxes(i)
#true_labels = annotations.code_labels(i)
boxes, probs = yolo.predict(image, float(args.threshold))
# 4. save detection result
image = draw_scaled_boxes(image, boxes, probs, labels)
output_path = os.path.join(write_dname, os.path.split(img_fname)[-1])
label_list = config['model']['labels']
right_label = np.argmax(probs, axis=1) if len(probs) > 0 else []
#cv2.imwrite(output_path, image)
print("{}-boxes are detected. {} saved.".format(
len(boxes), output_path))
if len(probs) > 0:
#create_ann(filename,image,boxes,right_label,label_list)
cv2.imwrite(output_path, image)
#n_true_positives += count_true_positives(boxes, true_boxes, labels, true_labels)
#n_truth += len(true_boxes)
#n_pred += len(boxes)
#print(calc_score(n_true_positives, n_truth, n_pred))
lst.sort()
json_dic = []
for i in range(13068):
file_name = str(i+1) + ".png"
print(file_name)
image_path = os.path.join(images_dir, file_name)
img = cv2.imread(image_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
boxes, probs, confidences = yolo_detector.predict(img, THRESHOLD)
# 4. save detection result
image, dic = draw_scaled_boxes(img,
boxes,
probs,
["1", "2", "3", "4", "5", "6", "7", "8", "9", "10"],
confidences)
print("{}-boxes are detected.".format(len(boxes)))
print(dic)
json_dic.append(dic)
# plt.imshow(image)
# plt.show()
# data = json.dumps(json_dic)
with open('data.json', 'w') as fp:
json.dump(json_dic, fp)
# %%