import numpy as np
import cv2
import os
import json
from tqdm import tqdm
from glob import glob
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras import layers, models, optimizers
from custom_layers import yolov4_neck, yolov4_head, nms
from utils import load_weights, get_detection_data, draw_bbox, voc_ap, draw_plot_func, read_txt_to_list
from config import yolo_config
from loss import yolo_loss
class Yolov4(object):
def __init__(self,
weight_path=None,
class_name_path='coco_classes.txt',
config=yolo_config,
):
assert config['img_size'][0] == config['img_size'][1], 'not support yet'
assert config['img_size'][0] % config['strides'][-1] == 0, 'must be a multiple of last stride'
self.class_names = [line.strip() for line in open(class_name_path).readlines()]
self.img_size = yolo_config['img_size']
self.num_classes = len(self.class_names)
self.weight_path = weight_path
self.anchors = np.array(yolo_config['anchors']).reshape((3, 3, 2))
self.xyscale = yolo_config['xyscale']
self.strides = yolo_config['strides']
self.output_sizes = [self.img_size[0] // s for s in self.strides]
self.class_color = {name: list(np.random.random(size=3)*255) for name in self.class_names}
# Training
self.max_boxes = yolo_config['max_boxes']
self.iou_loss_thresh = yolo_config['iou_loss_thresh']
self.config = yolo_config
assert self.num_classes > 0, 'no classes detected!'
tf.keras.backend.clear_session()
if yolo_config['num_gpu'] > 1:
mirrored_strategy = tf.distribute.MirroredStrategy()
with mirrored_strategy.scope():
self.build_model(load_pretrained=True if self.weight_path else False)
else:
self.build_model(load_pretrained=True if self.weight_path else False)
def build_model(self, load_pretrained=True):
# core yolo model
input_layer = layers.Input(self.img_size)
yolov4_output = yolov4_neck(input_layer, self.num_classes)
self.yolo_model = models.Model(input_layer, yolov4_output)
# Build training model
y_true = [
layers.Input(name='input_2', shape=(52, 52, 3, (self.num_classes + 5))), # label small boxes
layers.Input(name='input_3', shape=(26, 26, 3, (self.num_classes + 5))), # label medium boxes
layers.Input(name='input_4', shape=(13, 13, 3, (self.num_classes + 5))), # label large boxes
layers.Input(name='input_5', shape=(self.max_boxes, 4)), # true bboxes
]
loss_list = tf.keras.layers.Lambda(yolo_loss, name='yolo_loss',
arguments={'num_classes': self.num_classes,
'iou_loss_thresh': self.iou_loss_thresh,
'anchors': self.anchors})([*self.yolo_model.output, *y_true])
self.training_model = models.Model([self.yolo_model.input, *y_true], loss_list)
# Build inference model
yolov4_output = yolov4_head(yolov4_output, self.num_classes, self.anchors, self.xyscale)
# output: [boxes, scores, classes, valid_detections]
self.inference_model = models.Model(input_layer,
nms(yolov4_output, self.img_size, self.num_classes,
iou_threshold=self.config['iou_threshold'],
score_threshold=self.config['score_threshold']))
if load_pretrained and self.weight_path and self.weight_path.endswith('.weights'):
if self.weight_path.endswith('.weights'):
load_weights(self.yolo_model, self.weight_path)
print(f'load from {self.weight_path}')
elif self.weight_path.endswith('.h5'):
self.training_model.load_weights(self.weight_path)
print(f'load from {self.weight_path}')
self.training_model.compile(optimizer=optimizers.Adam(lr=1e-3),
loss={'yolo_loss': lambda y_true, y_pred: y_pred})
def load_model(self, path):
self.yolo_model = models.load_model(path, compile=False)
yolov4_output = yolov4_head(self.yolo_model.output, self.num_classes, self.anchors, self.xyscale)
self.inference_model = models.Model(self.yolo_model.input,
nms(yolov4_output, self.img_size, self.num_classes)) # [boxes, scores, classes, valid_detections]
def save_model(self, path):
self.yolo_model.save(path)
def preprocess_img(self, img):
img = cv2.resize(img, self.img_size[:2])
img = img / 255.
return img
def fit(self, train_data_gen, epochs, val_data_gen=None, initial_epoch=0, callbacks=None):
self.training_model.fit(train_data_gen,
steps_per_epoch=len(train_data_gen),
validation_data=val_data_gen,
validation_steps=len(val_data_gen),
epochs=epochs,
callbacks=callbacks,
initial_epoch=initial_epoch)
# raw_img: RGB
def predict_img(self, raw_img, random_color=True, plot_img=True, figsize=(10, 10), show_text=True, return_output=True):
print('img shape: ', raw_img.shape)
img = self.preprocess_img(raw_img)
imgs = np.expand_dims(img, axis=0)
pred_output = self.inference_model.predict(imgs)
detections = get_detection_data(img=raw_img,
model_outputs=pred_output,
class_names=self.class_names)
output_img = draw_bbox(raw_img, detections, cmap=self.class_color, random_color=random_color, figsize=figsize,
show_text=show_text, show_img=False)
if return_output:
return output_img, detections
else:
return detections
def predict(self, img_path, random_color=True, plot_img=True, figsize=(10, 10), show_text=True):
raw_img = img_path
return self.predict_img(raw_img, random_color, plot_img, figsize, show_text)
def export_gt(self, annotation_path, gt_folder_path):
with open(annotation_path) as file:
for line in file:
line = line.split(' ')
filename = line[0].split(os.sep)[-1].split('.')[0]
objs = line[1:]
# export txt file
with open(os.path.join(gt_folder_path, filename + '.txt'), 'w') as output_file:
for obj in objs:
x_min, y_min, x_max, y_max, class_id = [float(o) for o in obj.strip().split(',')]
output_file.write(f'{self.class_names[int(class_id)]} {x_min} {y_min} {x_max} {y_max}\n')
def export_prediction(self, annotation_path, pred_folder_path, img_folder_path, bs=2):
with open(annotation_path) as file:
img_paths = [os.path.join(img_folder_path, line.split(' ')[0].split(os.sep)[-1]) for line in file]
# print(img_paths[:20])
for batch_idx in tqdm(range(0, len(img_paths), bs)):
# print(len(img_paths), batch_idx, batch_idx*bs, (batch_idx+1)*bs)
paths = img_paths[batch_idx:batch_idx+bs]
# print(paths)
# read and process img
imgs = np.zeros((len(paths), *self.img_size))
raw_img_shapes = []
for j, path in enumerate(paths):
img = cv2.imread(path)
raw_img_shapes.append(img.shape)
img = self.preprocess_img(img)
imgs[j] = img
# process batch output
b_boxes, b_scores, b_classes, b_valid_detections = self.inference_model.predict(imgs)
for k in range(len(paths)):
num_boxes = b_valid_detections[k]
raw_img_shape = raw_img_shapes[k]
boxes = b_boxes[k, :num_boxes]
classes = b_classes[k, :num_boxes]
scores = b_scores[k, :num_boxes]
# print(raw_img_shape)
boxes[:, [0, 2]] = (boxes[:, [0, 2]] * raw_img_shape[1]) # w
boxes[:, [1, 3]] = (boxes[:, [1, 3]] * raw_img_shape[0]) # h
cls_names = [self.class_names[int(c)] for c in classes]
# print(raw_img_shape, boxes.astype(int), cls_names, scores)
img_path = paths[k]
filename = img_path.split(os.sep)[-1].split('.')[0]
# print(filename)
output_path = os.path.join(pred_folder_path, filename+'.txt')
with open(output_path, 'w') as pred_file:
for box_idx in range(num_boxes):
b = boxes[box_idx]
pred_file.write(f'{cls_names[box_idx]} {scores[box_idx]} {b[0]} {b[1]} {b[2]} {b[3]}\n')
def eval_map(self, gt_folder_path, pred_folder_path, temp_json_folder_path, output_files_path):
"""Process Gt"""
ground_truth_files_list = glob(gt_folder_path + '/*.txt')
assert len(ground_truth_files_list) > 0, 'no ground truth file'
ground_truth_files_list.sort()
# dictionary with counter per class
gt_counter_per_class = {}
counter_images_per_class = {}
gt_files = []
for txt_file in ground_truth_files_list:
file_id = txt_file.split(".txt", 1)[0]
file_id = os.path.basename(os.path.normpath(file_id))
# check if there is a correspondent detection-results file
temp_path = os.path.join(pred_folder_path, (file_id + ".txt"))
assert os.path.exists(temp_path), "Error. File not found: {}\n".format(temp_path)
lines_list = read_txt_to_list(txt_file)
# create ground-truth dictionary
bounding_boxes = []
is_difficult = False
already_seen_classes = []
for line in lines_list:
class_name, left, top, right, bottom = line.split()
# check if class is in the ignore list, if yes skip
bbox = left + " " + top + " " + right + " " + bottom
bounding_boxes.append({"class_name": class_name, "bbox": bbox, "used": False})
# count that object
if class_name in gt_counter_per_class:
gt_counter_per_class[class_name] += 1
else:
# if class didn't exist yet
gt_counter_per_class[class_name] = 1
if class_name not in already_seen_classes:
if class_name in counter_images_per_class:
counter_images_per_class[class_name] += 1
else:
# if class didn't exist yet
counter_images_per_class[class_name] = 1
already_seen_classes.append(class_name)
# dump bounding_boxes into a ".json" file
new_temp_file = os.path.join(temp_json_folder_path, file_id+"_ground_truth.json") #TEMP_FILES_PATH + "/" + file_id + "_ground_truth.json"
gt_files.append(new_temp_file)
with open(new_temp_file, 'w') as outfile:
json.dump(bounding_boxes, outfile)
gt_classes = list(gt_counter_per_class.keys())
# let's sort the classes alphabetically
gt_classes = sorted(gt_classes)
n_classes = len(gt_classes)
print(gt_classes, gt_counter_per_class)
"""Process prediction"""
dr_files_list = sorted(glob(os.path.join(pred_folder_path, '*.txt')))
for class_index, class_name in enumerate(gt_classes):
bounding_boxes = []
for txt_file in dr_files_list:
# the first time it checks if all the corresponding ground-truth files exist
file_id = txt_file.split(".txt", 1)[0]
file_id = os.path.basename(os.path.normpath(file_id))
temp_path = os.path.join(gt_folder_path, (file_id + ".txt"))
if class_index == 0:
if not os.path.exists(temp_path):
error_msg = f"Error. File not found: {temp_path}\n"
print(error_msg)
lines = read_txt_to_list(txt_file)
for line in lines:
try:
tmp_class_name, confidence, left, top, right, bottom = line.split()
except ValueError:
error_msg = f"""Error: File {txt_file} in the wrong format.\n
Expected: <class_name> <confidence> <left> <top> <right> <bottom>\n
Received: {line} \n"""
print(error_msg)
if tmp_class_name == class_name:
# print("match")
bbox = left + " " + top + " " + right + " " + bottom
bounding_boxes.append({"confidence": confidence, "file_id": file_id, "bbox": bbox})
# sort detection-results by decreasing confidence
bounding_boxes.sort(key=lambda x: float(x['confidence']), reverse=True)
with open(temp_json_folder_path + "/" + class_name + "_dr.json", 'w') as outfile:
json.dump(bounding_boxes, outfile)
"""
Calculate the AP for each class
"""
sum_AP = 0.0
ap_dictionary = {}
# open file to store the output
with open(output_files_path + "/output.txt", 'w') as output_file:
output_file.write("# AP and precision/recall per class\n")
count_true_positives = {}
for class_index, class_name in enumerate(gt_classes):
count_true_positives[class_name] = 0
"""
Load detection-results of that class
"""
dr_file = temp_json_folder_path + "/" + class_name + "_dr.json"
dr_data = json.load(open(dr_file))
"""
Assign detection-results to ground-truth objects
"""
nd = len(dr_data)
tp = [0] * nd # creates an array of zeros of size nd
fp = [0] * nd
for idx, detection in enumerate(dr_data):
file_id = detection["file_id"]
gt_file = temp_json_folder_path + "/" + file_id + "_ground_truth.json"
ground_truth_data = json.load(open(gt_file))
ovmax = -1
gt_match = -1
# load detected object bounding-box
bb = [float(x) for x in detection["bbox"].split()]
for obj in ground_truth_data:
# look for a class_name match
if obj["class_name"] == class_name:
bbgt = [float(x) for x in obj["bbox"].split()]
bi = [max(bb[0], bbgt[0]), max(bb[1], bbgt[1]), min(bb[2], bbgt[2]), min(bb[3], bbgt[3])]
iw = bi[2] - bi[0] + 1
ih = bi[3] - bi[1] + 1
if iw > 0 and ih > 0:
# compute overlap (IoU) = area of intersection / area of union
ua = (bb[2] - bb[0] + 1) * (bb[3] - bb[1] + 1) + \
(bbgt[2] - bbgt[0]+ 1) * (bbgt[3] - bbgt[1] + 1) - iw * ih
ov = iw * ih / ua
if ov > ovmax:
ovmax = ov
gt_match = obj
min_overlap = 0.5
if ovmax >= min_overlap:
# if "difficult" not in gt_match:
if not bool(gt_match["used"]):
# true positive
tp[idx] = 1
gt_match["used"] = True
count_true_positives[class_name] += 1
# update the ".json" file
with open(gt_file, 'w') as f:
f.write(json.dumps(ground_truth_data))
else:
# false positive (multiple detection)
fp[idx] = 1
else:
fp[idx] = 1
# compute precision/recall
cumsum = 0
for idx, val in enumerate(fp):
fp[idx] += cumsum
cumsum += val
print('fp ', cumsum)
cumsum = 0
for idx, val in enumerate(tp):
tp[idx] += cumsum
cumsum += val
print('tp ', cumsum)
rec = tp[:]
for idx, val in enumerate(tp):
rec[idx] = float(tp[idx]) / gt_counter_per_class[class_name]
print('recall ', cumsum)
prec = tp[:]
for idx, val in enumerate(tp):
prec[idx] = float(tp[idx]) / (fp[idx] + tp[idx])
print('prec ', cumsum)
ap, mrec, mprec = voc_ap(rec[:], prec[:])
sum_AP += ap
text = "{0:.2f}%".format(
ap * 100) + " = " + class_name + " AP " # class_name + " AP = {0:.2f}%".format(ap*100)
print(text)
ap_dictionary[class_name] = ap
n_images = counter_images_per_class[class_name]
# lamr, mr, fppi = log_average_miss_rate(np.array(prec), np.array(rec), n_images)
# lamr_dictionary[class_name] = lamr
"""
Draw plot
"""
if True:
plt.plot(rec, prec, '-o')
# add a new penultimate point to the list (mrec[-2], 0.0)
# since the last line segment (and respective area) do not affect the AP value
area_under_curve_x = mrec[:-1] + [mrec[-2]] + [mrec[-1]]
area_under_curve_y = mprec[:-1] + [0.0] + [mprec[-1]]
plt.fill_between(area_under_curve_x, 0, area_under_curve_y, alpha=0.2, edgecolor='r')
# set window title
fig = plt.gcf() # gcf - get current figure
fig.canvas.set_window_title('AP ' + class_name)
# set plot title
plt.title('class: ' + text)
# plt.suptitle('This is a somewhat long figure title', fontsize=16)
# set axis titles
plt.xlabel('Recall')
plt.ylabel('Precision')
# optional - set axes
axes = plt.gca() # gca - get current axes
axes.set_xlim([0.0, 1.0])
axes.set_ylim([0.0, 1.05]) # .05 to give some extra space
# Alternative option -> wait for button to be pressed
# while not plt.waitforbuttonpress(): pass # wait for key display
# Alternative option -> normal display
plt.show()
# save the plot
# fig.savefig(output_files_path + "/classes/" + class_name + ".png")
# plt.cla() # clear axes for next plot
# if show_animation:
# cv2.destroyAllWindows()
output_file.write("\n# mAP of all classes\n")
mAP = sum_AP / n_classes
text = "mAP = {0:.2f}%".format(mAP * 100)
output_file.write(text + "\n")
print(text)
"""
Count total of detection-results
"""
# iterate through all the files
det_counter_per_class = {}
for txt_file in dr_files_list:
# get lines to list
lines_list = read_txt_to_list(txt_file)
for line in lines_list:
class_name = line.split()[0]
# check if class is in the ignore list, if yes skip
# if class_name in args.ignore:
# continue
# count that object
if class_name in det_counter_per_class:
det_counter_per_class[class_name] += 1
else:
# if class didn't exist yet
det_counter_per_class[class_name] = 1
# print(det_counter_per_class)
dr_classes = list(det_counter_per_class.keys())
"""
Plot the total number of occurences of each class in the ground-truth
"""
if True:
window_title = "ground-truth-info"
plot_title = "ground-truth\n"
plot_title += "(" + str(len(ground_truth_files_list)) + " files and " + str(n_classes) + " classes)"
x_label = "Number of objects per class"
output_path = output_files_path + "/ground-truth-info.png"
to_show = False
plot_color = 'forestgreen'
draw_plot_func(
gt_counter_per_class,
n_classes,
window_title,
plot_title,
x_label,
output_path,
to_show,
plot_color,
'',
)
"""
Finish counting true positives
"""
for class_name in dr_classes:
# if class exists in detection-result but not in ground-truth then there are no true positives in that class
if class_name not in gt_classes:
count_true_positives[class_name] = 0
# print(count_true_positives)
"""
Plot the total number of occurences of each class in the "detection-results" folder
"""
if True:
window_title = "detection-results-info"
# Plot title
plot_title = "detection-results\n"
plot_title += "(" + str(len(dr_files_list)) + " files and "
count_non_zero_values_in_dictionary = sum(int(x) > 0 for x in list(det_counter_per_class.values()))
plot_title += str(count_non_zero_values_in_dictionary) + " detected classes)"
# end Plot title
x_label = "Number of objects per class"
output_path = output_files_path + "/detection-results-info.png"
to_show = False
plot_color = 'forestgreen'
true_p_bar = count_true_positives
draw_plot_func(
det_counter_per_class,
len(det_counter_per_class),
window_title,
plot_title,
x_label,
output_path,
to_show,
plot_color,
true_p_bar
)
"""
Draw mAP plot (Show AP's of all classes in decreasing order)
"""
if True:
window_title = "mAP"
plot_title = "mAP = {0:.2f}%".format(mAP * 100)
x_label = "Average Precision"
output_path = output_files_path + "/mAP.png"
to_show = True
plot_color = 'royalblue'
draw_plot_func(
ap_dictionary,
n_classes,
window_title,
plot_title,
x_label,
output_path,
to_show,
plot_color,
""
)
def predict_raw(self, img_path):
raw_img = cv2.imread(img_path)
print('img shape: ', raw_img.shape)
img = self.preprocess_img(raw_img)
imgs = np.expand_dims(img, axis=0)
return self.yolo_model.predict(imgs)
def predict_nonms(self, img_path, iou_threshold=0.413, score_threshold=0.1):
raw_img = cv2.imread(img_path)
print('img shape: ', raw_img.shape)
img = self.preprocess_img(raw_img)
imgs = np.expand_dims(img, axis=0)
yolov4_output = self.yolo_model.predict(imgs)
output = yolov4_head(yolov4_output, self.num_classes, self.anchors, self.xyscale)
pred_output = nms(output, self.img_size, self.num_classes, iou_threshold, score_threshold)
pred_output = [p.numpy() for p in pred_output]
detections = get_detection_data(img=raw_img,
model_outputs=pred_output,
class_names=self.class_names)
draw_bbox(raw_img, detections, cmap=self.class_color, random_color=True)
return detections