def evaluate_generator(self,
annotations,
comet_experiment=None,
iou_threshold=0.5,
max_detections=200):
""" Evaluate prediction model using a csv fit_generator
Args:
annotations (str): Path to csv label file, labels are in the format -> path/to/image.png,x1,y1,x2,y2,class_name
iou_threshold(float): IoU Threshold to count for a positive detection (defaults to 0.5)
max_detections (int): Maximum number of bounding box predictions
comet_experiment(object): A comet experiment class objects to track
Return:
mAP: Mean average precision of the evaluated data
"""
#Format args for CSV generator
classes_file = utilities.create_classes(annotations)
arg_list = utilities.format_args(annotations, classes_file, self.config)
args = parse_args(arg_list)
#create generator
validation_generator = CSVGenerator(
args.annotations,
args.classes,
image_min_side=args.image_min_side,
image_max_side=args.image_max_side,
config=args.config,
shuffle_groups=False,
)
average_precisions = evaluate(validation_generator,
self.prediction_model,
iou_threshold=iou_threshold,
score_threshold=args.score_threshold,
max_detections=max_detections,
save_path=args.save_path,
comet_experiment=comet_experiment)
# print evaluation
total_instances = []
precisions = []
for label, (average_precision, num_annotations) in average_precisions.items():
print('{:.0f} instances of class'.format(num_annotations),
validation_generator.label_to_name(label),
'with average precision: {:.4f}'.format(average_precision))
total_instances.append(num_annotations)
precisions.append(average_precision)
if sum(total_instances) == 0:
print('No test instances found.')
return
print('mAP using the weighted average of precisions among classes: {:.4f}'.format(
sum([a * b for a, b in zip(total_instances, precisions)]) /
sum(total_instances)))
mAP = sum(precisions) / sum(x > 0 for x in total_instances)
print('mAP: {:.4f}'.format(mAP))
return mAP
def detectAlphabets(imageToRecognize):
args = parse_args()
args.val_path = imageToRecognize
# os.environ["CUDA_VISIBLE_DEVICES"] = "0"
keras.backend.tensorflow_backend.set_session(get_session())
model = keras.models.load_model(os.path.join(dir, '../snapshots/resnet50_csv_wtext.h5'), custom_objects=custom_objects)
test_image_data_generator = keras.preprocessing.image.ImageDataGenerator()
#
# # create a generator for testing data
test_generator = CSVGenerator(
csv_data_file=args.annotations,
csv_class_file=args.classes,
image_data_generator=test_image_data_generator,
batch_size=args.batch_size
)
# index = 0
# load image
image = read_image_bgr(args.val_path)
# copy to draw on
draw = image.copy()
draw = cv2.cvtColor(draw, cv2.COLOR_BGR2RGB)
# preprocess image for network
image = preprocess_image(image)
image, scale = resize_image(image)
# process image
start = time.time()
_, _, detections = model.predict_on_batch(np.expand_dims(image, axis=0))
print("processing time: ", time.time() - start)
print('detections:', detections)
# compute predicted labels and scores
predicted_labels = np.argmax(detections[0, :, 4:], axis=1)
scores = detections[0, np.arange(detections.shape[1]), 4 + predicted_labels]
print("label=", predicted_labels)
# correct for image scale
scaled_detection = detections[0, :, :4] / scale
# visualize detections
recognized = {}
plt.figure(figsize=(15, 15))
plt.axis('off')
for idx, (label, score) in enumerate(zip(predicted_labels, scores)):
if score < 0.35:
continue
b = scaled_detection[idx, :4].astype(int)
cv2.rectangle(draw, (b[0], b[1]), (b[2], b[3]), (0, 0, 255), 1)
caption = test_generator.label_to_name(label)
if caption == "equal":
caption = "="
cv2.putText(draw, caption, (b[0], b[1] - 1), cv2.FONT_HERSHEY_PLAIN, 1, (255, 255, 255), 1)
recognized[caption] = b
print(caption + ", score=" + str(score))
plt.imshow(draw)
plt.show()
return recognized, draw
detections[:, :, 3] = np.minimum(image.shape[0], detections[:, :, 3])
# correct boxes for image scale
detections[0, :, :4] /= scale
# change to (x, y, w, h) (MS COCO standard)
detections[:, :, 2] -= detections[:, :, 0]
detections[:, :, 3] -= detections[:, :, 1]
# compute predicted labels and scores
for detection in detections[0, ...]:
label = np.argmax(detection[4:])
# append detections for each positively labeled class
if float(detection[4 + label]) > args.score_threshold:
image_result = {
'image_id' : test_generator.image_names[i],
'category_id' : test_generator.label_to_name(label),
'scores' : [float(det) for i,det in
enumerate(detection) if i >= 4],
'bbox' : (detection[:4]).tolist(),
}
# append detection to results
results.append(image_result)
# append image to list of processed images
image_ids.append(test_generator.image_names[i])
# print progress
print('{}/{}'.format(i, len(test_generator.image_names)), end='\r')
out_name = '{}_predict_results_{}.pickle'.format(os.path.splitext(args.test_path)[0],str(args.score_threshold).split(".")[-1])
pickle.dump(results,open(out_name,'wb'))
detections[0, :, :4] /= scale
# change to (x, y, w, h) (MS COCO standard)
detections[:, :, 2] -= detections[:, :, 0]
detections[:, :, 3] -= detections[:, :, 1]
# compute predicted labels and scores
for detection in detections[0, ...]:
label = int(detection[4])
# append detections for each positively labeled class
if float(detection[4 + label]) > args.score_threshold:
image_result = {
'image_id':
test_generator.image_names[i],
'category_id':
test_generator.label_to_name(label),
'scores':
[float(det) for i, det in enumerate(detection) if i >= 5],
'bbox': (detection[:4]).tolist(),
}
# append detection to results
results.append(image_result)
# append image to list of processed images
image_ids.append(test_generator.image_names[i])
# print progress
print('{}/{}'.format(i, len(test_generator.image_names)), end='\r')
out_name = '{}_predict_results_{}.pickle'.format(
os.path.splitext(args.test_path)[0],
str(args.score_threshold).split(".")[-1])
pickle.dump(results, open(out_name, 'wb'))
def main():
os.makedirs(RESULTS_DIR, exist_ok=True)
# os.environ["CUDA_VISIBLE_DEVICES"] = "0"
keras.backend.tensorflow_backend.set_session(get_session())
if SNAPSHOT_FILE:
snapshot_file = SNAPSHOT_FILE
else:
snapshots = os.listdir(SNAPSHOT_DIR)
if not snapshots:
raise Exception("There are no snapshots")
snapshot_file = os.path.join(SNAPSHOT_DIR,
sorted(snapshots, reverse=True)[0])
print("snapshot: {}".format(snapshot_file))
model = keras.models.load_model(
snapshot_file, custom_objects=custom_objects)
# print(model.summary())
# create image data generator object
val_image_data_generator = keras.preprocessing.image.ImageDataGenerator()
# create a generator for testing data
val_generator = CSVGenerator(
ANNOTATIONS,
CLASSES,
val_image_data_generator,
batch_size=BATCH_SIZE,
base_dir=BASE_DIR)
for image_index in range(val_generator.size()):
# load image
image = val_generator.load_image(image_index)
# copy to draw on
draw = image.copy()
draw = cv2.cvtColor(draw, cv2.COLOR_BGR2RGB)
# preprocess image for network
image = val_generator.preprocess_image(image)
image, scale = val_generator.resize_image(image)
annotations = val_generator.load_annotations(image_index)
# process image
start = time.time()
_, _, detections = model.predict_on_batch(
np.expand_dims(image, axis=0))
print("processing time: ", time.time() - start)
# compute predicted labels and scores
predicted_labels = np.argmax(detections[0, :, 4:], axis=1)
scores = detections[
0, np.arange(detections.shape[1]), 4 + predicted_labels]
# correct for image scale
detections[0, :, :4] /= scale
# visualize detections
detected = False
for idx, (label, score) in enumerate(zip(predicted_labels, scores)):
if score < 0.5:
continue
detected = True
if VISUALIZE_RESULTS:
b = detections[0, idx, :4].astype(int)
cv2.rectangle(draw, (b[0], b[1]), (b[2], b[3]), (0, 0, 255), 3)
caption = "{} {:.3f}".format(
val_generator.label_to_name(label), score)
cv2.putText(draw, caption, (b[0], b[1] - 10),
cv2.FONT_HERSHEY_PLAIN, 1.5, (0, 0, 0), 3)
cv2.putText(draw, caption, (b[0], b[1] - 10),
cv2.FONT_HERSHEY_PLAIN, 1.5, (255, 255, 255), 2)
if VISUALIZE_RESULTS and detected:
# visualize annotations
for annotation in annotations:
label = int(annotation[4])
b = annotation[:4].astype(int)
cv2.rectangle(draw, (b[0], b[1]), (b[2], b[3]), (0, 255, 0), 2)
caption = "{}".format(val_generator.label_to_name(label))
cv2.putText(draw, caption, (b[0], b[1] - 10),
cv2.FONT_HERSHEY_PLAIN, 1.5, (0, 0, 0), 3)
cv2.putText(draw, caption, (b[0], b[1] - 10),
cv2.FONT_HERSHEY_PLAIN, 1.5, (255, 255, 255), 2)
plt.figure(figsize=(15, 15))
plt.axis('off')
plt.imshow(draw)
plt.show()