Kaggle competition: Global Wheat Detection
<SOS> metric: custom metric Kaggle data source: global-wheat-detection<concatenate>
import numpy as np
import pandas as pd
import os
from tqdm.auto import tqdm
import shutil as sh
x_train = np.append(x_train, [np.fliplr(x)for x in x_train], axis=0)
 y_train = np.append(y_train, [np.fliplr(x)for x in y_train], axis=0)
 print(x_train.shape)
 print(y_valid.shape )<choose_model_class>
!cp -r.. /input/yolov5train/* .
input_layer = Input(( img_size_target, img_size_target, 1))
output_layer = build_model(input_layer, 16,0.5)
 model = Model(input_layer, output_layer)
 model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["acc"] )<train_model>
!pip install --no-deps '.. /input/weightedboxesfusion/' > /dev/null
early_stopping = EarlyStopping(monitor='val_acc', mode = 'max',patience=20, verbose=1)
 model_checkpoint = ModelCheckpoint("./unet_best1.model",monitor='val_acc',
mode = 'max', save_best_only=True, verbose=1)
 reduce_lr = ReduceLROnPlateau(monitor='val_acc', mode = 'max',factor=0.5, patience=6, min_lr=0.001, verbose=1)
 epochs = 200
batch_size = 32
history = model.fit(x_train, y_train,
validation_data=[x_valid, y_valid],
epochs=epochs,
batch_size=batch_size,
callbacks=[early_stopping, model_checkpoint, reduce_lr],
verbose=1 )<load_pretrained>
def convertTrainLabel() :
df = pd.read_csv('.. /input/global-wheat-detection/train.csv')
 bboxs = np.stack(df['bbox'].apply(lambda x: np.fromstring(x[1:-1], sep=',')))
 for i, column in enumerate(['x', 'y', 'w', 'h']):
df[column] = bboxs[:,i]
df.drop(columns=['bbox'], inplace=True)
 df['x_center'] = df['x'] + df['w']/2
df['y_center'] = df['y'] + df['h']/2
df['classes'] = 0
df = df[['image_id','x', 'y', 'w', 'h','x_center','y_center','classes']]
index = list(set(df.image_id))
source = 'train'
if True:
for fold in [0]:
val_index = index[len(index)*fold//5:len(index)*(fold+1)//5]
for name,mini in tqdm(df.groupby('image_id')) :
if name in val_index:
path2save = 'val2017/'
else:
path2save = 'train2017/'
if not os.path.exists('convertor/fold{}/labels/'.format(fold)+path2save):
os.makedirs('convertor/fold{}/labels/'.format(fold)+path2save)
 with open('convertor/fold{}/labels/'.format(fold)+path2save+name+".txt", 'w+')as f:
row = mini[['classes','x_center','y_center','w','h']].astype(float ).values
row = row/1024
row = row.astype(str)
 for j in range(len(row)) :
text = ' '.join(row[j])
 f.write(text)
 f.write("
")
 if not os.path.exists('convertor/fold{}/images/{}'.format(fold,path2save)) :
os.makedirs('convertor/fold{}/images/{}'.format(fold,path2save))
sh.copy(".. /input/global-wheat-detection/{}/{}.jpg".format(source,name),'convertor/fold{}/images/{}/{}.jpg'.format(fold,path2save,name))
model = load_model("./unet_best1.model" )<predict_on_test>
def run_wbf(boxes, scores, image_size=1023, iou_thr=0.5, skip_box_thr=0.7, weights=None):
labels = [np.zeros(score.shape[0])for score in scores]
boxes = [box/(image_size)for box in boxes]
boxes, scores, labels = weighted_boxes_fusion(boxes, scores, labels, weights=None, iou_thr=iou_thr, skip_box_thr=skip_box_thr)
 boxes = boxes*(image_size)
 return boxes, scores, labels
def TTAImage(image, index):
image1 = image.copy()
if index==0:
rotated_image = cv2.rotate(image1, cv2.ROTATE_90_CLOCKWISE)
 return rotated_image
elif index==1:
rotated_image2 = cv2.rotate(image1, cv2.ROTATE_90_CLOCKWISE)
 rotated_image2 = cv2.rotate(rotated_image2, cv2.ROTATE_90_CLOCKWISE)
 return rotated_image2
elif index==2:
rotated_image3 = cv2.rotate(image1, cv2.ROTATE_90_CLOCKWISE)
 rotated_image3 = cv2.rotate(rotated_image3, cv2.ROTATE_90_CLOCKWISE)
 rotated_image3 = cv2.rotate(rotated_image3, cv2.ROTATE_90_CLOCKWISE)
 return rotated_image3
elif index == 3:
return image1
def rotBoxes90(boxes, im_w, im_h):
ret_boxes =[]
for box in boxes:
x1, y1, x2, y2 = box
x1, y1, x2, y2 = x1-im_w//2, im_h//2 - y1, x2-im_w//2, im_h//2 - y2
x1, y1, x2, y2 = y1, -x1, y2, -x2
x1, y1, x2, y2 = int(x1+im_w//2), int(im_h//2 - y1), int(x2+im_w//2), int(im_h//2 - y2)
 x1a, y1a, x2a, y2a = min(x1, x2), min(y1, y2), max(x1, x2), max(y1, y2)
 ret_boxes.append([x1a, y1a, x2a, y2a])
 return np.array(ret_boxes)
 def detect1Image(im0, imgsz, model, device, conf_thres, iou_thres):
img = letterbox(im0, new_shape=imgsz)[0]
img = img[:, :, ::-1].transpose(2, 0, 1)
 img = np.ascontiguousarray(img)
 img = torch.from_numpy(img ).to(device)
 img = img.float()
img /= 255.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
 pred = model(img, augment=False)[0]
pred = non_max_suppression(pred, conf_thres, iou_thres)
 boxes = []
scores = []
for i, det in enumerate(pred):
if det is not None and len(det):
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape ).round()
for *xyxy, conf, cls in det:
boxes.append([int(xyxy[0]), int(xyxy[1]), int(xyxy[2]), int(xyxy[3])])
 scores.append(conf)
 return np.array(boxes), np.array(scores )
def predict_result(model,x_test,img_size_target):
preds_test = model.predict(x_test ).reshape(-1, img_size_target, img_size_target)
 preds_test += np.array([ np.fliplr(a)for a in model.predict(np.array([np.fliplr(x)for x in x_test])).reshape(-1, img_size_target, img_size_target)])
 return preds_test/2.0<predict_on_test>
def makePseudolabel() :
source = '.. /input/global-wheat-detection/test/'
weights = '.. /input/yolosub/gwd/yolo5x_panet_1.pt'
imgsz = 1024
conf_thres = 0.5
iou_thres = 0.6
is_TTA = True
imagenames = os.listdir(source)
 device = torch.device('cuda')if torch.cuda.is_available() else torch.device('cpu')
 model = torch.load(weights, map_location=device)['model'].float()
model.to(device ).eval()
dataset = LoadImages(source, img_size=imgsz)
 path2save = 'train2017/'
if not os.path.exists('convertor/fold0/labels/'+path2save):
os.makedirs('convertor/fold0/labels/'+path2save)
 if not os.path.exists('convertor/fold0/images/{}'.format(path2save)) :
os.makedirs('convertor/fold0/images/{}'.format(path2save))
for name in imagenames:
image_id = name.split('.')[0]
im01 = cv2.imread('%s/%s.jpg'%(source,image_id))
if im01.shape[0]!=1024 or im01.shape[1]!=1024:
continue
assert im01 is not None, 'Image Not Found '
im_w, im_h = im01.shape[:2]
if is_TTA:
enboxes = []
enscores = []
for i in range(4):
im0 = TTAImage(im01, i)
 boxes, scores = detect1Image(im0, imgsz, model, device, conf_thres, iou_thres)
 for _ in range(3-i):
boxes = rotBoxes90(boxes, im_w, im_h)
 enboxes.append(boxes)
 enscores.append(scores)
 boxes, scores, labels = run_wbf(enboxes, enscores, image_size = im_w, iou_thr=0.6, skip_box_thr=0.43)
 boxes = boxes.astype(np.int32 ).clip(min=0, max=im_w)
 else:
boxes, scores = detect1Image(im01, imgsz, model, device, conf_thres, iou_thres)
 boxes[:, 2] = boxes[:, 2] - boxes[:, 0]
boxes[:, 3] = boxes[:, 3] - boxes[:, 1]
boxes = boxes[scores >= 0.05].astype(np.int32)
 scores = scores[scores >=float(0.05)]
lineo = ''
for box in boxes:
x1, y1, w, h = box
xc, yc, w, h =(x1+w/2)/1024,(y1+h/2)/1024, w/1024, h/1024
lineo += '0 %f %f %f %f
'%(xc, yc, w, h)
 fileo = open('convertor/fold0/labels/'+path2save+image_id+".txt", 'w+')
 fileo.write(lineo)
 fileo.close()
sh.copy(".. /input/global-wheat-detection/test/{}.jpg".format(image_id),'convertor/fold0/images/{}/{}.jpg'.format(path2save,image_id))
preds_valid = predict_result(model,x_valid,img_size_target )<define_search_space>
if len(os.listdir('.. /input/global-wheat-detection/test/')) <11:
pass
else:
!python train.py --img 1024 --batch 4 --epochs 10 --data.. /input/configyolo5/wheat0.yaml --cfg.. /input/yolosub/gwd/yolo5_PANET.yaml --weights.. /input/yolosub/gwd/yolo5x_panet_1.pt
!rm -rf convertor
def filter_image(img):
if img.sum() < 100:
return np.zeros(img.shape)
 else:
return img
thresholds = np.linspace(0.3, 0.7, 31)
 ious = np.array([iou_metric(y_valid.reshape(( -1, img_size_target, img_size_target)) , [filter_image(img)for img in preds_valid > threshold])for threshold in tqdm_notebook(thresholds)])
<categorify>
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 )
def rle_encode(im):
pixels = im.flatten(order = 'F')
 pixels = np.concatenate([[0], pixels, [0]])
 runs = np.where(pixels[1:] != pixels[:-1])[0] + 1
runs[1::2] -= runs[::2]
return ' '.join(str(x)for x in runs )<drop_column>
def detect() :
source = '.. /input/global-wheat-detection/test/'
weights = '.. /input/yolosub/gwd/yolo5x_panet_1.pt'
if not os.path.exists(weights):
weights = '.. /input/yolosub/gwd/yolo5x_panet_1.pt'
imgsz = 1024
conf_thres = 0.5
iou_thres = 0.6
is_TTA = True
imagenames = os.listdir(source)
 device = torch.device('cuda')if torch.cuda.is_available() else torch.device('cpu')
 model = torch.load(weights, map_location=device)['model'].float()
model.to(device ).eval()
dataset = LoadImages(source, img_size=imgsz)
 results = []
fig, ax = plt.subplots(5, 2, figsize=(30, 70))
count = 0
for name in imagenames:
image_id = name.split('.')[0]
im01 = cv2.imread('%s/%s.jpg'%(source,image_id))
assert im01 is not None, 'Image Not Found '
im_w, im_h = im01.shape[:2]
if is_TTA:
enboxes = []
enscores = []
for i in range(4):
im0 = TTAImage(im01, i)
 boxes, scores = detect1Image(im0, imgsz, model, device, conf_thres, iou_thres)
 for _ in range(3-i):
boxes = rotBoxes90(boxes, im_w, im_h)
 if 1:
enboxes.append(boxes)
 enscores.append(scores)
 boxes, scores = detect1Image(im01, imgsz, model, device, conf_thres, iou_thres)
 enboxes.append(boxes)
 enscores.append(scores)
 boxes, scores, labels = run_wbf(enboxes, enscores, image_size = im_w, iou_thr=0.6, skip_box_thr=0.5)
 boxes = boxes.astype(np.int32 ).clip(min=0, max=im_w)
 else:
boxes, scores = detect1Image(im01, imgsz, model, device, conf_thres, iou_thres)
 boxes[:, 2] = boxes[:, 2] - boxes[:, 0]
boxes[:, 3] = boxes[:, 3] - boxes[:, 1]
boxes = boxes[scores >= 0.05].astype(np.int32)
 scores = scores[scores >=float(0.05)]
if count<10:
for box, score in zip(boxes,scores):
cv2.rectangle(im0, 
(box[0], box[1]), 
(box[2]+box[0], box[3]+box[1]), 
(220, 0, 0), 2)
 cv2.putText(im0, '%.2f'%(score),(box[0], box[1]), cv2.FONT_HERSHEY_SIMPLEX ,
0.5,(255,255,255), 2, cv2.LINE_AA)
 ax[count%5][count//5].imshow(im0)
 count+=1
result = {
'image_id': image_id,
'PredictionString': format_prediction_string(boxes, scores)
 }
results.append(result)
 return results
<predict_on_test><EOS> 
results = detect()
test_df = pd.DataFrame(results, columns=['image_id', 'PredictionString'])
 test_df.to_csv('submission.csv', index=False)
 test_df.head()