def detect_image(self, image_id, image):
self.confidence = 0.01
f = open("./input/detection-results/" + image_id + ".txt", "w")
image_shape = np.array(np.shape(image)[0:2])
# 加灰条的方式， resize 到 320x320
crop_img, x_offset, y_offset = letterbox_image(
image, [self.model_image_size[0], self.model_image_size[1]])
photo = np.array(crop_img, dtype=np.float64)
# 图片预处理，归一化
photo = preprocess_input(
np.reshape(photo, [
1, self.model_image_size[0], self.model_image_size[1],
self.model_image_size[2]
]))
preds = self.m2det.predict(photo)
# 将预测结果进行解码
results = self.bbox_util.detection_out(
preds, self.prior, confidence_threshold=self.confidence)
if len(results[0]) <= 0:
return image
# 筛选出其中得分高于confidence的框
det_label = results[0][:, 0]
det_conf = results[0][:, 1]
det_xmin, det_ymin, det_xmax, det_ymax = results[0][:, 2], results[
0][:, 3], results[0][:, 4], results[0][:, 5]
top_indices = [
i for i, conf in enumerate(det_conf) if conf >= self.confidence
]
top_conf = det_conf[top_indices]
top_label_indices = det_label[top_indices].tolist()
top_xmin, top_ymin, top_xmax, top_ymax = np.expand_dims(
det_xmin[top_indices],
-1), np.expand_dims(det_ymin[top_indices], -1), np.expand_dims(
det_xmax[top_indices],
-1), np.expand_dims(det_ymax[top_indices], -1)
# 去掉灰条
boxes = m2det_correct_boxes(
top_ymin, top_xmin, top_ymax, top_xmax,
np.array([self.model_image_size[0], self.model_image_size[1]]),
image_shape)
for i, c in enumerate(top_label_indices):
predicted_class = self.class_names[int(c) - 1]
score = str(top_conf[i])
top, left, bottom, right = boxes[i]
f.write("%s %s %s %s %s %s\n" %
(predicted_class, score[:6], str(int(left)), str(
int(top)), str(int(right)), str(int(bottom))))
f.close()
return
def detect_image(self, image):
image_shape = np.array(np.shape(image)[0:2])
crop_img,x_offset,y_offset = letterbox_image(image, [self.model_image_size[0],self.model_image_size[1]])
photo = np.array(crop_img,dtype = np.float64)
# 图片预处理，归一化
photo = preprocess_input(np.reshape(photo,[1,self.model_image_size[0],self.model_image_size[1],self.model_image_size[2]]))
preds = self.m2det.predict(photo)
# 将预测结果进行解码
results = self.bbox_util.detection_out(preds,self.prior,confidence_threshold=self.confidence)
if len(results[0])<=0:
return image
# 筛选出其中得分高于confidence的框
det_label = results[0][:, 0]
det_conf = results[0][:, 1]
det_xmin, det_ymin, det_xmax, det_ymax = results[0][:, 2], results[0][:, 3], results[0][:, 4], results[0][:, 5]
top_indices = [i for i, conf in enumerate(det_conf) if conf >= self.confidence]
top_conf = det_conf[top_indices]
top_label_indices = det_label[top_indices].tolist()
top_xmin, top_ymin, top_xmax, top_ymax = np.expand_dims(det_xmin[top_indices],-1),np.expand_dims(det_ymin[top_indices],-1),np.expand_dims(det_xmax[top_indices],-1),np.expand_dims(det_ymax[top_indices],-1)
# 去掉灰条
boxes = m2det_correct_boxes(top_ymin,top_xmin,top_ymax,top_xmax,np.array([self.model_image_size[0],self.model_image_size[1]]),image_shape)
font = ImageFont.truetype(font='model_data/simhei.ttf',size=np.floor(3e-2 * np.shape(image)[1] + 0.5).astype('int32'))
thickness = (np.shape(image)[0] + np.shape(image)[1]) // self.model_image_size[0]
for i, c in enumerate(top_label_indices):
predicted_class = self.class_names[int(c-1)]
score = top_conf[i]
top, left, bottom, right = boxes[i]
top = top - 5
left = left - 5
bottom = bottom + 5
right = right + 5
top = max(0, np.floor(top + 0.5).astype('int32'))
left = max(0, np.floor(left + 0.5).astype('int32'))
bottom = min(np.shape(image)[0], np.floor(bottom + 0.5).astype('int32'))
right = min(np.shape(image)[1], np.floor(right + 0.5).astype('int32'))
# 画框框
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
label = label.encode('utf-8')
print(label)
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
for i in range(thickness):
draw.rectangle(
[left + i, top + i, right - i, bottom - i],
outline=self.colors[int(c-1)])
draw.rectangle(
[tuple(text_origin), tuple(text_origin + label_size)],
fill=self.colors[int(c-1)])
draw.text(text_origin, str(label,'UTF-8'), fill=(0, 0, 0), font=font)
del draw
return image
def get_FPS(self, image, test_interval):
image_shape = np.array(np.shape(image)[0:2])
#---------------------------------------------------------#
# 给图像增加灰条，实现不失真的resize
# 也可以直接resize进行识别
#---------------------------------------------------------#
if self.letterbox_image:
crop_img = np.array(
letterbox_image(
image,
(self.model_image_size[1], self.model_image_size[0])))
else:
crop_img = image.convert('RGB')
crop_img = crop_img.resize(
(self.model_image_size[1], self.model_image_size[0]),
Image.BICUBIC)
photo = np.array(crop_img, dtype=np.float64)
photo = preprocess_input(
np.reshape(photo, [
1, self.model_image_size[0], self.model_image_size[1],
self.model_image_size[2]
]))
preds = self.m2det.predict(photo)
results = self.bbox_util.detection_out(
preds, self.prior, confidence_threshold=self.confidence)
if len(results[0]) > 0:
det_label = results[0][:, 0]
det_conf = results[0][:, 1]
det_xmin, det_ymin, det_xmax, det_ymax = results[0][:, 2], results[
0][:, 3], results[0][:, 4], results[0][:, 5]
top_indices = [
i for i, conf in enumerate(det_conf) if conf >= self.confidence
]
top_conf = det_conf[top_indices]
top_label_indices = det_label[top_indices].tolist()
top_xmin, top_ymin, top_xmax, top_ymax = np.expand_dims(
det_xmin[top_indices],
-1), np.expand_dims(det_ymin[top_indices], -1), np.expand_dims(
det_xmax[top_indices],
-1), np.expand_dims(det_ymax[top_indices], -1)
#-----------------------------------------------------------#
# 去掉灰条部分
#-----------------------------------------------------------#
if self.letterbox_image:
boxes = m2det_correct_boxes(
top_ymin, top_xmin, top_ymax, top_xmax,
np.array(
[self.model_image_size[0], self.model_image_size[1]]),
image_shape)
else:
top_xmin = top_xmin * image_shape[1]
top_ymin = top_ymin * image_shape[0]
top_xmax = top_xmax * image_shape[1]
top_ymax = top_ymax * image_shape[0]
boxes = np.concatenate(
[top_ymin, top_xmin, top_ymax, top_xmax], axis=-1)
t1 = time.time()
for _ in range(test_interval):
preds = self.m2det.predict(photo)
results = self.bbox_util.detection_out(
preds, self.prior, confidence_threshold=self.confidence)
if len(results[0]) > 0:
det_label = results[0][:, 0]
det_conf = results[0][:, 1]
det_xmin, det_ymin, det_xmax, det_ymax = results[
0][:, 2], results[0][:, 3], results[0][:, 4], results[0][:,
5]
top_indices = [
i for i, conf in enumerate(det_conf)
if conf >= self.confidence
]
top_conf = det_conf[top_indices]
top_label_indices = det_label[top_indices].tolist()
top_xmin, top_ymin, top_xmax, top_ymax = np.expand_dims(
det_xmin[top_indices], -1), np.expand_dims(
det_ymin[top_indices], -1), np.expand_dims(
det_xmax[top_indices],
-1), np.expand_dims(det_ymax[top_indices], -1)
#-----------------------------------------------------------#
# 去掉灰条部分
#-----------------------------------------------------------#
if self.letterbox_image:
boxes = m2det_correct_boxes(
top_ymin, top_xmin, top_ymax, top_xmax,
np.array([
self.model_image_size[0], self.model_image_size[1]
]), image_shape)
else:
top_xmin = top_xmin * image_shape[1]
top_ymin = top_ymin * image_shape[0]
top_xmax = top_xmax * image_shape[1]
top_ymax = top_ymax * image_shape[0]
boxes = np.concatenate(
[top_ymin, top_xmin, top_ymax, top_xmax], axis=-1)
t2 = time.time()
tact_time = (t2 - t1) / test_interval
return tact_time
def detect_image(self, image_id, image):
self.confidence = 0.01
f = open("./input/detection-results/" + image_id + ".txt", "w")
image_shape = np.array(np.shape(image)[0:2])
#---------------------------------------------------------#
# 给图像增加灰条，实现不失真的resize
# 也可以直接resize进行识别
#---------------------------------------------------------#
if self.letterbox_image:
crop_img = np.array(
letterbox_image(
image,
(self.model_image_size[1], self.model_image_size[0])))
else:
crop_img = image.convert('RGB')
crop_img = crop_img.resize(
(self.model_image_size[1], self.model_image_size[0]),
Image.BICUBIC)
photo = np.array(crop_img, dtype=np.float64)
#-----------------------------------------------------------#
# 图片预处理，归一化。
#-----------------------------------------------------------#
photo = preprocess_input(
np.reshape(photo, [
1, self.model_image_size[0], self.model_image_size[1],
self.model_image_size[2]
]))
preds = self.m2det.predict(photo)
#-----------------------------------------------------------#
# 将预测结果进行解码
#-----------------------------------------------------------#
results = self.bbox_util.detection_out(
preds, self.prior, confidence_threshold=self.confidence)
#--------------------------------------#
# 如果没有检测到物体，则返回原图
#--------------------------------------#
if len(results[0]) <= 0:
return image
#-----------------------------------------------------------#
# 筛选出其中得分高于confidence的框
#-----------------------------------------------------------#
det_label = results[0][:, 0]
det_conf = results[0][:, 1]
det_xmin, det_ymin, det_xmax, det_ymax = results[0][:, 2], results[
0][:, 3], results[0][:, 4], results[0][:, 5]
top_indices = [
i for i, conf in enumerate(det_conf) if conf >= self.confidence
]
top_conf = det_conf[top_indices]
top_label_indices = det_label[top_indices].tolist()
top_xmin, top_ymin, top_xmax, top_ymax = np.expand_dims(
det_xmin[top_indices],
-1), np.expand_dims(det_ymin[top_indices], -1), np.expand_dims(
det_xmax[top_indices],
-1), np.expand_dims(det_ymax[top_indices], -1)
#-----------------------------------------------------------#
# 去掉灰条部分
#-----------------------------------------------------------#
if self.letterbox_image:
boxes = m2det_correct_boxes(
top_ymin, top_xmin, top_ymax, top_xmax,
np.array([self.model_image_size[0], self.model_image_size[1]]),
image_shape)
else:
top_xmin = top_xmin * image_shape[1]
top_ymin = top_ymin * image_shape[0]
top_xmax = top_xmax * image_shape[1]
top_ymax = top_ymax * image_shape[0]
boxes = np.concatenate([top_ymin, top_xmin, top_ymax, top_xmax],
axis=-1)
for i, c in enumerate(top_label_indices):
predicted_class = self.class_names[int(c) - 1]
score = str(top_conf[i])
top, left, bottom, right = boxes[i]
f.write("%s %s %s %s %s %s\n" %
(predicted_class, score[:6], str(int(left)), str(
int(top)), str(int(right)), str(int(bottom))))
f.close()
return