개정판 d8e1223c
issue #1366: add save option
Change-Id: I4c1edc9b5132fa8e83383406bc0bffea5d0aaa40
| DTI_PID/WebServer/app/recognition/index.py | ||
|---|---|---|
| 61 | 61 |
|
| 62 | 62 |
boxes = test_doftech_all_images.get_symbol(imgs, data['name'], root_path=data_path, trained_model1=os.path.join(data_path, 'checkpoint', data['name'] + "_only_params.pth"), \ |
| 63 | 63 |
trained_model2=os.path.dirname(os.path.realpath( |
| 64 |
__file__)) + '\\..\\..\\symbol_recognition\\MODEL\\doftech_all_class_only_params_opc.pth') |
|
| 64 |
__file__)) + '\\..\\..\\symbol_recognition\\MODEL\\doftech_all_class_only_params_opc.pth', save=True)
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|
| 65 | 65 |
|
| 66 | 66 |
return jsonify({'symbol_box': boxes[0]})
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| 67 | 67 |
|
| DTI_PID/WebServer/symbol_recognition/test_doftech_all_images.py | ||
|---|---|---|
| 47 | 47 |
# x, y, width, height |
| 48 | 48 |
return [self.class_info, self.start_h, self.start_w, self.end_h - self.start_h, self.end_w - self.start_w, self.iou] |
| 49 | 49 |
|
| 50 |
def get_symbol(imgs, name=None, root_path=None, trained_model1=None, trained_model2=None): |
|
| 50 |
def get_symbol(imgs, name=None, root_path=None, trained_model1=None, trained_model2=None, save=False):
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|
| 51 | 51 |
global colors, DOFTECH_CLASSES |
| 52 | 52 |
|
| 53 | 53 |
parser = argparse.ArgumentParser("You Only Look Once: Unified, Real-Time Object Detection")
|
| ... | ... | |
| 101 | 101 |
|
| 102 | 102 |
print("=========> "+ img_name)
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| 103 | 103 |
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| 104 |
save_dir = os.path.dirname(os.path.realpath(__file__)) + '\save/' |
|
| 105 |
if not os.path.isdir(save_dir): |
|
| 106 |
os.mkdir(save_dir) |
|
| 107 |
|
|
| 108 |
save_dir = os.path.dirname(os.path.realpath(__file__)) + '\save/' + img_name + '/' |
|
| 109 |
if not os.path.isdir(save_dir): |
|
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os.mkdir(save_dir) |
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os.mkdir(save_dir+"a/") |
|
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os.mkdir(save_dir+"b/") |
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else: |
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shutil.rmtree(save_dir) |
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os.mkdir(save_dir) |
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os.mkdir(save_dir+"a/") |
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| 117 |
os.mkdir(save_dir+"b/") |
|
| 118 |
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| 119 |
text_file = open(save_dir+'test_result.txt', mode='wt', encoding='utf-8') |
|
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text_file.write(img_name+" -> Detection Reesult\n") |
|
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text_file.write("================================\n")
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if save: |
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try: |
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save_dir = os.path.dirname(os.path.realpath(__file__)) + '\save/' |
|
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save_dir_tile = save_dir + save_dir + "a/" |
|
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if not os.path.isdir(save_dir): |
|
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os.mkdir(save_dir) |
|
| 110 |
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save_dir = os.path.dirname(os.path.realpath(__file__)) + '\save/' + img_name + '/' |
|
| 112 |
if not os.path.isdir(save_dir): |
|
| 113 |
os.mkdir(save_dir) |
|
| 114 |
os.mkdir(save_dir+"a/") |
|
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#os.mkdir(save_dir+"b/") |
|
| 116 |
else: |
|
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shutil.rmtree(save_dir) |
|
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os.mkdir(save_dir) |
|
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os.mkdir(save_dir+"a/") |
|
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#os.mkdir(save_dir+"b/") |
|
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except: |
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print('save directory error')
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save = False |
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save_dir = None |
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save_dir_tile = None |
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| 126 |
|
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if save: |
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text_file = open(save_dir+'test_result.txt', mode='wt', encoding='utf-8') |
|
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text_file.write(img_name+" -> Detection Reesult\n") |
|
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text_file.write("================================\n")
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|
| 122 | 131 |
|
| 123 | 132 |
# Text File 만들기 |
| 124 | 133 |
|
| ... | ... | |
| 128 | 137 |
print("=========> SMALL OBJECT DETECTION")
|
| 129 | 138 |
for idx_small in range(len(small_object_patch_list)): |
| 130 | 139 |
patchs = small_object_patch_list[idx_small] |
| 131 |
total_symbole_list.append(detection_object(patchs, model1, save_dir+"a/", opc=False, opt=opt))
|
|
| 140 |
total_symbole_list.append(detection_object(patchs, model1, opc=False, opt=opt, save_root=save_dir_tile))
|
|
| 132 | 141 |
# ---------------------------------------------- |
| 133 | 142 |
# Large Object Detection (OPC etc.) |
| 134 | 143 |
# ---------------------------------------------- |
| ... | ... | |
| 140 | 149 |
t_image = img_list[idx_img].copy() |
| 141 | 150 |
count_result = merge_fn(t_image, total_symbole_list, save_dir) |
| 142 | 151 |
|
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for idx, value in enumerate(count_result): |
|
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text_file.write(DOFTECH_CLASSES[idx]+ " : "+ str(value) + "\n") |
|
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if save: |
|
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for idx, value in enumerate(count_result): |
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text_file.write(DOFTECH_CLASSES[idx]+ " : "+ str(value) + "\n") |
|
| 145 | 155 |
|
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text_file.close() |
|
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text_file.close()
|
|
| 147 | 157 |
|
| 148 | 158 |
res = [] |
| 149 | 159 |
for symbol in total_symbole_list[0]:# + total_symbole_list[1]: |
| ... | ... | |
| 155 | 165 |
return total_symbole_lists |
| 156 | 166 |
|
| 157 | 167 |
|
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def detection_object(patchs, model, save_root, opc, opt):
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|
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def detection_object(patchs, model, opc, opt, save_root=None):
|
|
| 159 | 169 |
global DOFTECH_CLASSES |
| 160 | 170 |
|
| 161 | 171 |
symbol_list = [] |
| ... | ... | |
| 225 | 235 |
(xmin, ymin + text_size[1] + 4), cv2.FONT_HERSHEY_PLAIN, 1, |
| 226 | 236 |
(255, 255, 255), 1) |
| 227 | 237 |
# -----------------------fill king image-------------------------------------- |
| 228 |
cv2.imwrite(save_root+"{}_prediction.png".format(idx), output_image)
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if save_root: |
|
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cv2.imwrite(save_root+"{}_prediction.png".format(idx), output_image)
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|
| 229 | 240 |
pil_image.setObjectNum(len(predictions)) |
| 230 | 241 |
return symbol_list |
| 231 | 242 |
|
| 232 |
def merge_fn(total_img, total_symbole_list, save_root): |
|
| 243 |
def merge_fn(total_img, total_symbole_list, save_root=None):
|
|
| 233 | 244 |
""" total_img:path -> img file """ |
| 234 | 245 |
global DOFTECH_CLASSES |
| 235 | 246 |
|
| ... | ... | |
| 258 | 269 |
for index, name in enumerate(DOFTECH_CLASSES): |
| 259 | 270 |
if name == total_symbole_list[idx_out][idx].class_info : |
| 260 | 271 |
count_result[index] = count_result[index]+1 |
| 261 |
cv2.imwrite(save_root+"entire.png", temp_king_img) |
|
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if save_root: |
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cv2.imwrite(save_root+"entire.png", temp_king_img) |
|
| 262 | 274 |
|
| 263 | 275 |
return count_result |
| 264 | 276 |
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내보내기 Unified diff
