def display(preds, imgs, imshow=True, imwrite=False, img_id=1):
for i in range(len(imgs)):
if len(preds[i]['rois']) == 0:
continue
imgs[i] = imgs[i].copy()
imgs[i] = cv2.cvtColor(imgs[i], cv2.COLOR_BGR2RGB)
for j in range(len(preds[i]['rois'])):
x1, y1, x2, y2 = preds[i]['rois'][j].astype(np.int)
obj = obj_list[preds[i]['class_ids'][j]]
score = float(preds[i]['scores'][j])
plot_one_box(imgs[i], [x1, y1, x2, y2],
label=obj,
score=score,
color=color_list[get_index_label(obj, obj_list)])
if imshow:
cv2.imshow('img', imgs[i])
cv2.waitKey(0)
if imwrite:
str1 = 'test/' + str(img_id) + '.jpg'
cv2.imwrite(str1, imgs[i])
def display(preds, imgs, imshow=True, imwrite=False):
for i in range(len(imgs)):
if len(preds[i]['rois']) == 0:
continue
imgs[i] = imgs[i].copy()
for j in range(len(preds[i]['rois'])):
x1, y1, x2, y2 = preds[i]['rois'][j].astype(np.int)
obj = obj_list[preds[i]['class_ids'][j]]
score = float(preds[i]['scores'][j])
plot_one_box(imgs[i], [x1, y1, x2, y2],
label=obj,
score=score,
color=color_list[get_index_label(obj, obj_list)])
if imshow:
cv2.imshow('img', imgs[i])
cv2.waitKey(0)
if imwrite:
# cv2.imwrite(f'test/img_inferred_d{compound_coef}_this_repo_{i}.jpg', imgs[i])
cv2.imwrite(
f'test_result/d0_epoch71_1031/img_inferred_d{compound_coef}_this_repo_{i}.jpg',
imgs[i])
def display(preds, imgs, imshow=True, imwrite=False, write_dir=None):
# for i in range(len(imgs)):
for i, (name, img) in enumerate(imgs.items()):
if len(preds[i]['rois']) == 0:
continue
for j in range(len(preds[i]['rois'])):
x1, y1, x2, y2 = preds[i]['rois'][j].astype(np.int)
obj = obj_list[preds[i]['class_ids'][j]]
score = float(preds[i]['scores'][j])
plot_one_box(
img, #imgs[i],
[x1, y1, x2, y2],
label=obj,
score=score,
color=color_list[get_index_label(obj, obj_list)])
if imshow:
cv2.imshow('img', imgs[i])
cv2.waitKey(0)
if imwrite:
os.makedirs(write_dir, exist_ok=True)
write_path = os.path.join(write_dir, f'{name}.jpg')
cv2.imwrite(write_path, img)
def display(preds, imgs, imshow=True, imwrite=False):
for i in range(len(imgs)):
if len(preds[i]['rois']) == 0:
continue
for j in range(len(preds[i]['rois'])):
x1, y1, x2, y2 = preds[i]['rois'][j].astype(np.int)
obj = obj_list[preds[i]['class_ids'][j]]
score = float(preds[i]['scores'][j])
plot_one_box(imgs[i], [x1, y1, x2, y2],
label=obj,
score=score,
color=color_list[get_index_label(obj, obj_list)])
if imshow:
cv2.imshow('img', imgs[i])
cv2.waitKey(0)
if imwrite:
img = img_path.split('/')[-1].split('.')[0]
# pdb.set_trace()
img_name = f'{img}_img_inferred_d{compound_coef}_this_repo_{i}.jpg'
img_save_path = os.path.join(img_save_folder, img_name)
print(img_save_path)
cv2.imwrite(img_save_path, imgs[i])
def display(preds, imgs, imshow=True, imwrite=False):
for i in range(len(imgs)):
if len(preds[i]['rois']) == 0:
continue
for j in range(len(preds[i]['rois'])):
x1, y1, x2, y2 = preds[i]['rois'][j].astype(np.int)
obj = obj_list[preds[i]['class_ids'][j]]
score = float(preds[i]['scores'][j])
plot_one_box(imgs[i], [x1, y1, x2, y2],
label=obj,
score=score,
color=color_list[get_index_label(obj, obj_list)])
if imshow:
#----------以下在Colab會crash, 必須改用plt-------------
#cv2.imshow('img', imgs[i])
#cv2.waitKey(0)
#------------------------------------------------------
plt.imshow(imgs[i])
plt.show()
if imwrite:
cv2.imwrite(
f'test/img_inferred_d{compound_coef}_this_repo_{i}.jpg',
imgs[i])
def display(self, preds, imgs, imshow=True, imwrite=False):
scores = []
labels = []
bboxes = []
for i in range(len(imgs)):
if len(preds[i]['rois']) == 0:
continue
for j in range(len(preds[i]['rois'])):
x1, y1, x2, y2 = preds[i]['rois'][j].astype(np.int)
obj = self.system_dict["params"]["obj_list"][preds[i]
['class_ids'][j]]
score = float(preds[i]['scores'][j])
if (score > self.system_dict["params"]["threshold"]):
scores.append(score)
labels.append(obj)
bboxes.append([x1, y1, x2, y2])
plot_one_box(imgs[i], [x1, y1, x2, y2],
label=obj,
score=score,
color=self.system_dict["local"]["color_list"]
[get_index_label(
obj,
self.system_dict["params"]["obj_list"])])
if imshow:
cv2.imshow('img', imgs[i])
cv2.waitKey(0)
if imwrite:
cv2.imwrite('output.jpg', imgs[i])
return scores, labels, bboxes
def display(cur_frame, preds, imgs, imshow=True, imwrite=False):
for i in range(len(imgs)):
if len(preds[i]['rois']) == 0:
continue
imgs[i] = imgs[i].copy()
for j in range(len(preds[i]['rois'])):
x1, y1, x2, y2 = preds[i]['rois'][j].astype(np.int)
obj = obj_list[preds[i]['class_ids'][j]]
score = float(preds[i]['scores'][j])
plot_one_box(imgs[i], [x1, y1, x2, y2],
label=obj,
score=score,
color=color_list[get_index_label(obj, obj_list)])
if imshow:
cv2.imshow('img', imgs[i])
#cv2.waitKey(0)
if imwrite:
if not os.path.exists(img_path):
os.makedirs(img_path)
cv2.imwrite(
f'{img_path}/img_inferred_d{compound_coef}_this_repo_{cur_frame}.jpg',
imgs[i])
def display(preds, imgs, imshow=True, showtime=0, imwrite=False):
for i, img_name in zip(range(len(imgs)), img_names):
# if len(preds[i]['rois']) == 0: # if model dosen't detect object, not show image
# continue
imgs[i] = imgs[i].copy()
for j in range(len(preds[i]['rois'])):
x1, y1, x2, y2 = preds[i]['rois'][j].astype(np.int)
obj = obj_list[preds[i]['class_ids'][j]]
score = float(preds[i]['scores'][j])
plot_one_box(imgs[i], [x1, y1, x2, y2],
label=obj,
score=score,
color=color_list[get_index_label(obj, obj_list)])
print(obj)
if imwrite:
img = cv2.cvtColor(imgs[i], cv2.COLOR_BGR2RGB)
cv2.imwrite(f'test/img_inferred_d{compound_coef}_{img_name}', img)
if imshow:
img = cv2.cvtColor(imgs[i], cv2.COLOR_BGR2RGB)
cv2.namedWindow(f'{img_name}', cv2.WINDOW_NORMAL)
cv2.imshow(f'{img_name}', img)
cv2.waitKey(showtime)
cv2.destroyAllWindows()
def display(preds,
imgs,
compound_coef,
obj_list=None,
imshow=True,
imwrite=False,
debug=False):
if obj_list is None:
obj_list = ['person']
color_list = standard_to_bgr(STANDARD_COLORS)
for i in range(len(imgs)):
if len(preds[i]['rois']) == 0:
if debug:
cv2.imshow('img', imgs[i])
cv2.waitKey(0)
continue
imgs[i] = imgs[i].copy()
for j in range(len(preds[i]['rois'])):
x1, y1, x2, y2 = preds[i]['rois'][j].astype(np.int)
obj = obj_list[preds[i]['class_ids'][j]]
score = float(preds[i]['scores'][j])
plot_one_box(imgs[i], [x1, y1, x2, y2],
label=obj,
score=score,
color=color_list[get_index_label(obj, obj_list)])
if imshow:
cv2.imshow('img', imgs[i])
cv2.waitKey(0)
if imwrite:
os.system("mkdir -p ./assets/predictions")
cv2.imwrite(
f'./assets/predictions/img_inferred_d{compound_coef}_this_repo_{i}.jpg',
imgs[i])
if imwrite:
image_folder = './assets/predictions'
image_files = [
image_folder + '/' + img for img in os.listdir(image_folder)
if img.endswith(".jpg")
]
clip = moviepy.video.io.ImageSequenceClip.ImageSequenceClip(
image_files, fps=1)
clip.write_videofile('./assets/predictions_testset.mp4')
def display(preds, imgs, imwrite=True):
for i in range(len(imgs)):
if len(preds[i]['rois']) == 0:
continue
for j in range(len(preds[i]['rois'])):
x1, y1, x2, y2 = preds[i]['rois'][j].astype(np.int)
obj = obj_list[preds[i]['class_ids'][j]]
score = float(preds[i]['scores'][j])
plot_one_box(imgs[i], [x1, y1, x2, y2],
label=obj,
score=score,
color=color_list[get_index_label(obj, obj_list)])
if imwrite:
cv2.imwrite(
f'test/img_inferred_d{compound_coef}_this_repo_{i}.jpg',
imgs[i])
def display(preds, imgs, imshow=True, imwrite=False):
for i, img_name in zip(range(len(imgs)), img_names):
imgs[i] = imgs[i].copy()
for j in range(len(preds[i]['rois'])):
x1, y1, x2, y2 = preds[i]['rois'][j].astype(np.int)
obj = obj_list[preds[i]['class_ids'][j]]
score = float(preds[i]['scores'][j])
plot_one_box(imgs[i], [x1, y1, x2, y2], label=obj, score=score,
color=color_list[get_index_label(obj, obj_list)])
if imshow:
img = cv2.cvtColor(imgs[i], cv2.COLOR_BGR2RGB)
cv2.imshow('video', img)
cv2.waitKey(1)
if imwrite:
cv2.imwrite(f'test/img_inferred_d{compound_coef}_{img_name}', imgs[i])
def __save_image(self, preds, imgs, imwrite=True):
color_list = standard_to_bgr(STANDARD_COLORS)
for i in range(len(imgs)):
if len(preds[i]['rois']) == 0:
continue
imgs[i] = imgs[i].copy()
for j in range(len(preds[i]['rois'])):
x1, y1, x2, y2 = preds[i]['rois'][j].astype(np.int)
obj = self.obj_list[preds[i]['class_ids'][j]]
score = float(preds[i]['scores'][j])
plot_one_box(imgs[i], [x1, y1, x2, y2],
label=obj,
score=score,
color=color_list[get_index_label(
obj, self.obj_list)])
if imwrite:
cv2.imwrite(
f'test/img_inferred_d{self.compound_coef}_this_repo_{i}.jpg',
imgs[i])
def display(preds, imgs, imshow=True, imwrite=False):
for i in range(len(imgs)):
if len(preds[i]['rois']) == 0:
continue
for j in range(len(preds[i]['rois'])):
x1, y1, x2, y2 = preds[i]['rois'][j].astype(np.int)
obj = obj_list[preds[i]['class_ids'][j]]
score = float(preds[i]['scores'][j])
plot_one_box(imgs[i], [x1, y1, x2, y2],
label=obj,
score=score,
color=color_list[get_index_label(obj, obj_list)])
if imshow:
cv2.imshow('img', imgs[i])
cv2.waitKey(0)
if imwrite:
cv2.imwrite(f'test/out.jpg', imgs[i])
def display(preds, imgs, imshow=True, imwrite=False):
x = []
y = []
result_1 = []
result_2 = []
for i in range(len(imgs)):
if len(preds[i]['rois']) == 0:
continue
for j in range(len(preds[i]['rois'])):
x1, y1, x2, y2 = preds[i]['rois'][j].astype(np.int)
obj = obj_list[preds[i]['class_ids'][j]]
score = float(preds[i]['scores'][j])
x, y = plot_one_box(imgs[i], [x1, y1, x2, y2],
label=obj,
score=score,
color=color_list[get_index_label(
obj, obj_list)])
result_1.append(x)
result_2.append(y)
return result_1, result_2
def display(out_1, out_2, imgs, imshow=True, showtime=0, imwrite=False):
# if len(preds[i]['rois']) == 0: # if model dosen't detect object, not show image
# continue
for img, out_1 in zip(imgs, out_1):
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
for i in range(len(out_1['rois'])):
ox1, oy1, ox2, oy2 = out_1['rois'][i].astype(np.int)
obj_1 = obj_list_1[out_1['class_ids'][i]]
score = float(out_1['scores'][i])
color = color_list[get_index_label(obj_1, obj_list_1)]
plot_one_box(img, [ox1, oy1, ox2, oy2],
label=obj_1,
score=score,
color=color)
print(obj_1)
print(f'obj {i}:', ox1, oy1, ox2, oy2)
for j in range(len(out_2[i]['rois'])):
dx1, dy1, dx2, dy2 = out_2[i]['rois'][j].astype(np.int)
obj_2 = obj_list_2[out_2[i]['class_ids'][j]].split('_')[-1]
score = float(out_2[i]['scores'][j])
plot_one_box(img, [dx1 + ox1, dy1 + oy1, dx2 + ox1, dy2 + oy1],
label=obj_2,
score=score,
color=color)
print(obj_2)
print('damage :', dx1, dy1, dx2, dy2)
print('change :', dx1 + ox1, dy1 + oy1, dx2 + ox1, dy2 + oy1)
if imshow:
# print(f'{img_name}')
cv2.namedWindow('__', cv2.WINDOW_NORMAL)
cv2.resizeWindow('__', 1500, 1000)
cv2.imshow('__', img)
# cv2.waitKey(0)
key = cv2.waitKey(showtime)
if key == ord('p'):
cv2.waitKey(-1)
def display(names, preds, imgs, imshow=True, imwrite=False, save_bbox=True):
for i in range(len(imgs)):
if len(preds[i]['rois']) == 0:
continue
f = open('test/' + names[i] + '.txt', "w")
for j in range(len(preds[i]['rois'])):
x1, y1, x2, y2 = preds[i]['rois'][j].astype(np.int)
obj = obj_list[preds[i]['class_ids'][j]]
score = float(preds[i]['scores'][j])
plot_one_box(imgs[i], [x1, y1, x2, y2],
label=obj,
score=score,
color=color_list[get_index_label(obj, obj_list)])
if save_bbox and obj == 'cell phone':
f.write(
str(67) + ' ' + str(score) + ' ' + str(x1) + ' ' +
str(y1) + ' ' + str(x2) + ' ' + str(y2) + '\n')
if imshow:
cv2.imshow(names[i], imgs[i])
cv2.waitKey(0)
if imwrite:
cv2.imwrite('test/' + names[i] + '.jpg', imgs[i])
def display(preds, imgs, imshow=True, imwrite=True):
for i in range(len(imgs)):
if len(preds[i]['rois']) == 0:
continue
imgs[i] = imgs[i].copy()
for j in range(len(preds[i]['rois'])):
x1, y1, x2, y2 = preds[i]['rois'][j].astype(np.int)
obj = obj_list[preds[i]['class_ids'][j]]
score = float(preds[i]['scores'][j])
plot_one_box(imgs[i], [x1, y1, x2, y2], label=obj, score=score,
color=color_list[get_index_label(obj, obj_list)])
if imshow:
imgs[i] = cv2.cvtColor(imgs[i], cv2.COLOR_BGR2RGB)
cv2.imshow('img', imgs[i])
cv2.waitKey(0)
if imwrite:
cv2.imwrite(f'test/d{compound_coef}_{haha}', imgs[i])
print('nmsl')
def display(preds, imgs, imshow=True, imwrite=False):
for i in range(len(imgs)):
if len(preds[i]['rois']) == 0:
continue
imgs[i] = imgs[i].copy()
for j in range(len(preds[i]['rois'])):
x1, y1, x2, y2 = preds[i]['rois'][j].astype(np.int)
obj = obj_list[preds[i]['class_ids'][j]]
score = float(preds[i]['scores'][j])
list_image_id.append(img_path.split('/')[-1])
list_class.append(obj)
list_score.append(score)
list_xmin.append(x1)
list_ymin.append(y1)
list_xmax.append(x2)
list_ymax.append(y2)
plot_one_box(imgs[i], [x1, y1, x2, y2],
label=obj,
score=score,
color=color_list[get_index_label(obj, obj_list)])
def display(preds, imgs, imshow=True, imwrite=False):
for i in range(len(imgs)):
if len(preds[i]['rois']) == 0:
continue
for j in range(len(preds[i]['rois'])):
x1, y1, x2, y2 = preds[i]['rois'][j].astype(np.int)
obj = obj_list[preds[i]['class_ids'][j]]
score = float(preds[i]['scores'][j])
print(x1, y1, x2, y2, obj, score)
plot_one_box(imgs[i], [x1, y1, x2, y2],
label=obj,
score=score,
color=color_list[get_index_label(obj, obj_list)])
if imshow:
cv2.imshow('img', imgs[i])
cv2.waitKey(0)
if imwrite:
predicated_path = 'demo_jpg/{0}_infer.jpg'.format(
config.dataset_name)
cv2.imwrite(predicated_path, imgs[i])
print("write predicated result in:{0}".format(predicated_path))
def display(preds, imgs, imshow=True, imwrite=False, start=0):
for i in range(len(imgs)):
if len(preds[i]['rois']) == 0:
continue
for j in range(len(preds[i]['rois'])):
x1, y1, x2, y2 = preds[i]['rois'][j].astype(np.int)
obj = obj_list[preds[i]['class_ids'][j]]
score = float(preds[i]['scores'][j])
if obj in obj_interest:
plot_one_box(imgs[i], [x1, y1, x2, y2],
label=obj,
score=score,
color=color_list[get_index_label(obj, obj_list)])
if imshow:
cv2.imshow('img', imgs[i])
cv2.waitKey(0)
if imwrite:
cv2.imwrite(
img_root + 'Result' +
f'/img_inferred_d{compound_coef}_this_repo_{i+start}.jpg',
imgs[i])
anchors, regression, classification,
regressBoxes, clipBoxes,
threshold, iou_threshold)
preds = invert_affine(framed_metas, out)
if(savefig):
imgs = ori_imgs;
i = 0;
for j in range(len(preds[i]['rois'])):
x1, y1, x2, y2 = preds[i]['rois'][j].astype(np.int)
obj = obj_list[preds[i]['class_ids'][j]]
score = float(preds[i]['scores'][j])
if(score >= threshold and obj in classes):
plot_one_box(imgs[i], [x1, y1, x2, y2], label=obj,score=score,color=color_list[get_index_label(obj, obj_list)])
cv2.imwrite(output_name, imgs[i])
if(write_monk_format):
print("Saving Annotations to monk format");
df = preds_to_monk_format(img_path, preds, classes, class_names=obj_list, thresh=thresh);
out_file_name = output_name.split(".")[0] + ".csv";
df.to_csv(out_file_name, index=False);
if(write_coco_format):
print("Saving Annotations to coco format (individual files)");
iou_threshold)
preds = invert_affine(framed_metas, out)
imgs = ori_imgs
i = 0
if (savefig):
for j in range(len(preds[i]['rois'])):
x1, y1, x2, y2 = preds[i]['rois'][j].astype(np.int)
obj = obj_list[preds[i]['class_ids'][j]]
score = float(preds[i]['scores'][j])
if (score >= threshold and obj in classes):
plot_one_box(imgs[i], [x1, y1, x2, y2],
label=obj,
score=score,
color=color_list[get_index_label(
obj, obj_list)])
cv2.imwrite(output_name, imgs[i])
if (write_monk_format):
print("Saving Annotations to monk format")
df = preds_to_monk_format(img_path,
preds,
classes,
class_names=obj_list,
thresh=thresh)
out_file_name = output_name.split(".")[0] + ".csv"
df.to_csv(out_file_name, index=False)
if (write_coco_format):
print("Saving Annotations to coco format (individual files)")
def box(preds, imgs, color_list, obj_list, imshow=False, imwrite=False):
for i in range(len(imgs)):
if len(preds[i]['rois']) == 0:
continue
imgs[i] = imgs[i].copy()
for j in range(len(preds[i]['rois'])):
x1, y1, x2, y2 = preds[i]['rois'][j].astype(np.int)
obj = obj_list[preds[i]['class_ids'][j]]
score = float(preds[i]['scores'][j])
plot_one_box(imgs[i], [x1, y1, x2, y2], label=obj,score=score,color=color_list[get_index_label(obj, obj_list)])
return imgs[0]
