from Evaluation import GeoJsonReader
import csv
if __name__ == '__main__':
csv_output = open('narges_original_text.csv', 'wt');
csv_writer = csv.writer(csv_output)
count = 0
for i in range(1, 11):
result_file_name = './narges result/1920-' + str(i) + '.png_EdditedByPixels.txt'
result_obj = GeoJsonReader.load_json_file(result_file_name)
for feature in result_obj['features']:
text = feature['NameBeforeDictionary']
text.replace(',', '')
text.replace('\n', ' ')
csv_writer.writerow([text])
count += 1
proceed_sets = set()
score_writer = csv.DictWriter(scorefile, fieldnames=fieldnames2)
score_writer.writeheader()
rect_writer = csv.DictWriter(rect_precision_file, fieldnames=fieldnames)
rect_writer.writeheader()
# Read Ground Truth.
map_gt_list = [];
for m in range(1, 11):
map_name = ['1920-1.png', '1920-2.png', '1920-3.png', '1920-4.png', '1920-5.png',
'1920-6.png', '1920-7.png', '1920-8.png', '1920-9.png', '1920-10.png']
groundTruthFile = './GroundTruths/' + map_name[m - 1].split('.')[0] + '.geojson'
gt_obj = GeoJsonReader.load_json_file(groundTruthFile)
gt_list = GeoJsonReader.get_ground_truth_list(gt_obj, 1, 1)
map_gt_list.append(gt_list)
candidate_file = './narges result/candidates'
cf = open(candidate_file, 'r')
map_feature_list = []
map_result_obj_features = []
map_result_feature_count = []
map_result_rects = []
# Read result files.
count = 0
for i in range(1, MAP_NUM):
result_file_name = './narges result/1920-' + str(i) + '.png_EdditedByPixels.txt'
def evaluate_result(gt_obj, result_obj, group_para=0, scale_w=1, scale_h=1):
gt_list = GeoJsonReader.get_ground_truth_list(gt_obj, scale_w, scale_h)
result_rect_list = GeoJsonReader.get_result_rectangles(result_obj)
return ResultEvaluation.evaluation_simple(result_rect_list, gt_list, area_threshold=0.7, group_para=group_para)
gt_list = []
map_name = ['1920-1.png', '1920-2.png', '1920-3.png', '1920-4.png', '1920-5.png',
'1920-6.png', '1920-7.png', '1920-8.png', '1920-9.png', '1920-10.png']
found_gt_num = 0
correct_num = 0
discard_num = 0
strabo_num = 0
for m in range(1, 11):
groundTruthFile = './GroundTruths/' + map_name[m-1].split('.')[0] + '.geojson'
result_file = './rsh_rs/' + map_name[m-1] + 'ByPixels.txt'
result_obj = GeoJsonReader.load_json_file(result_file)
result_rect_list = GeoJsonReader.get_result_rectangles(result_obj)
gt_obj = GeoJsonReader.load_json_file(groundTruthFile)
gt_list = GeoJsonReader.get_ground_truth_list(gt_obj, 1, 1)
word_locations = map_words_locations[m]
for idw, rect in enumerate(word_locations):
word_list = map_aligned_list[m][idw]
discard_list = map_discard_list[m][idw]
result_text = ''
rect_idx = 0
for result_rect in result_rect_list:
if result_rect.is_the_same_rect(rect):
result_text = result_rect.text
resolutions[2] = float(500) / 1512
resolutions[3] = float(2000) / 1512
resolutions[4] = float(2500) / 1512
precisions = [ [[] for x in resolution_name] for y in range(1, 4) ]
recalls = [ [[] for x in resolution_name] for y in range(1, 4) ]
for r in range(1, 5):
for i in range(0, len(file_list)):
for u in range(1, 4):
groundTruthFile = './GroundTruths/' + file_list[i] + '.geojson'
resultFile = './Results/res' + str(r) + '/' + file_list[i] + '.pngByPixels.txt'
print("Ground Truth: %s\nResult: %s" % (groundTruthFile, resultFile))
print("Scaling: %f" % (resolutions[r]))
result_obj = GeoJsonReader.load_json_file(resultFile)
gt_obj = GeoJsonReader.load_json_file(groundTruthFile)
overlap_json, non_overlap_json, precision_list, recall_list, extracted_num_list, gt_num_list = \
evaluate_result(gt_obj, result_obj, group_para=u, scale_w=resolutions[r], scale_h=resolutions[r])
gt_num = gt_num_list[0]
extracted_num = extracted_num_list[0]
precision = precision_list[0]
recall = recall_list[0]
precisions[u - 1][r - 1].append(precision)
recalls[u - 1][r - 1].append(recall)
f_score = 0 if precision + recall == 0 else 2 * precision * recall / (precision + recall)