Kaggle competition: Global Wheat Detection
color = sns.color_palette()
sns.set_style('darkgrid')
 %matplotlib inline
%config InlineBackend.figure_format = 'retina'
pd.set_option('display.float_format', lambda x: '{:.3f}'.format(x))
pd.set_option('max_columns', None)
 print(os.listdir(".. /input/")) 
<load_from_csv>
def detect(save_img=False):
weights, imgsz = opt.weights,opt.img_size
source = '.. /input/global-wheat-detection/test/'
device = torch_utils.select_device(opt.device)
 half = False
model = torch.load(weights, map_location=device)['model'].to(device ).eval()
dataset = LoadImages(source, img_size=1024)
 t0 = time.time()
img = torch.zeros(( 1, 3, imgsz, imgsz), device=device)
 all_path=[]
all_bboxex =[]
all_score =[]
for path, img, im0s, vid_cap in dataset:
print(im0s.shape)
 img = torch.from_numpy(img ).to(device)
 img = img.half() if half else img.float()
img /= 255.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
 t1 = torch_utils.time_synchronized()
bboxes_2 = []
score_2 = []
if True:
pred = model(img, augment=opt.augment)[0]
pred = non_max_suppression(pred, 0.4, opt.iou_thres,merge=True, classes=None, agnostic=False)
 t2 = torch_utils.time_synchronized()
bboxes = []
score = []
for i, det in enumerate(pred):
p, s, im0 = path, '', im0s
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]
if det is not None and len(det):
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape ).round()
for c in det[:, -1].unique() :
n =(det[:, -1] == c ).sum()
for *xyxy, conf, cls in det:
if True:
xywh = torch.tensor(xyxy ).view(-1 ).numpy()
bboxes.append(xywh)
 score.append(conf)
 bboxes_2.append(bboxes)
 score_2.append(score)
 all_path.append(path)
 all_score.append(score_2)
 all_bboxex.append(bboxes_2)
 return all_path,all_score,all_bboxex
if __name__ == '__main__':
class opt:
weights = ".. /input/yolov5weight/yolov5_70.pt"
img_size = 1024
conf_thres = 0.1
iou_thres = 0.94
augment = False
device = '0'
classes=None
agnostic_nms = True
opt.img_size = check_img_size(opt.img_size)
 print(opt)
 with torch.no_grad() :
res = detect()
train = pd.read_csv('.. /input/train.csv')
 test = pd.read_csv('.. /input/test.csv')
 train.head() 
<categorify>
def run_wbf(boxes,scores, image_size=1024, iou_thr=0.41, skip_box_thr=0.4, weights=None):
labels0 = [np.ones(len(scores[idx])) for idx in range(len(scores)) ]
boxes, scores, labels = weighted_boxes_fusion(boxes, scores, labels0, weights=None, iou_thr=iou_thr, skip_box_thr=skip_box_thr)
 return boxes, scores, labels
dict_columns = ['belongs_to_collection', 'genres', 'production_companies',
'production_countries', 'spoken_languages', 'Keywords', 'cast', 'crew']
def text_to_dict(df):
for column in dict_columns:
df[column] = df[column].apply(lambda x: {} if pd.isna(x)else ast.literal_eval(x))
return df
train = text_to_dict(train)
 test = text_to_dict(test )<define_variables>
all_path,all_score,all_bboxex = res
for i, e in enumerate(train['belongs_to_collection'][:5]):
print(i, e )<count_values>
sys.path.insert(0, ".. /input/weightedboxesfusion")
 results =[]
def format_prediction_string(boxes, scores):
pred_strings = []
for j in zip(scores, boxes):
pred_strings.append("{0:.4f} {1} {2} {3} {4}".format(j[0], j[1][0], j[1][1], j[1][2], j[1][3]))
return " ".join(pred_strings)
 for row in range(len(all_path)) :
image_id = all_path[row].split("/")[-1].split(".")[0]
boxes = all_bboxex[row]
scores = all_score[row]
boxes, scores, labels = run_wbf(boxes,scores)
 boxes = boxes.astype(np.int32 ).clip(min=0, max=1023)
 boxes[:, 2] = boxes[:, 2] - boxes[:, 0]
boxes[:, 3] = boxes[:, 3] - boxes[:, 1]
result = {'image_id': image_id,'PredictionString': format_prediction_string(boxes, scores)}
results.append(result)
 test_df = pd.DataFrame(results, columns=['image_id', 'PredictionString'] )
<feature_engineering><EOS> 
test_df.to_csv('submission.csv', index=False)
 test_df.head(12 )