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val.py
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val.py
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import argparse
import cv2
import json
import math
import numpy as np
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
import torch
from datasets.coco import CocoValDataset
from models.with_mobilenet import PoseEstimationWithMobileNet
from modules.keypoints import extract_keypoints, group_keypoints
from modules.load_state import load_state
def run_coco_eval(gt_file_path, dt_file_path):
annotation_type = 'keypoints'
print('Running test for {} results.'.format(annotation_type))
coco_gt = COCO(gt_file_path)
coco_dt = coco_gt.loadRes(dt_file_path)
result = COCOeval(coco_gt, coco_dt, annotation_type)
result.evaluate()
result.accumulate()
result.summarize()
def normalize(img, img_mean, img_scale):
img = np.array(img, dtype=np.float32)
img = (img - img_mean) * img_scale
return img
def pad_width(img, stride, pad_value, min_dims):
h, w, _ = img.shape
h = min(min_dims[0], h)
min_dims[0] = math.ceil(min_dims[0] / float(stride)) * stride
min_dims[1] = max(min_dims[1], w)
min_dims[1] = math.ceil(min_dims[1] / float(stride)) * stride
pad = []
pad.append(int(math.floor((min_dims[0] - h) / 2.0)))
pad.append(int(math.floor((min_dims[1] - w) / 2.0)))
pad.append(int(min_dims[0] - h - pad[0]))
pad.append(int(min_dims[1] - w - pad[1]))
padded_img = cv2.copyMakeBorder(img, pad[0], pad[2], pad[1], pad[3],
cv2.BORDER_CONSTANT, value=pad_value)
return padded_img, pad
def convert_to_coco_format(pose_entries, all_keypoints):
coco_keypoints = []
scores = []
for n in range(len(pose_entries)):
if len(pose_entries[n]) == 0:
continue
keypoints = [0] * 17 * 3
to_coco_map = [0, -1, 6, 8, 10, 5, 7, 9, 12, 14, 16, 11, 13, 15, 2, 1, 4, 3]
person_score = pose_entries[n][-2]
position_id = -1
for keypoint_id in pose_entries[n][:-2]:
position_id += 1
if position_id == 1: # no 'neck' in COCO
continue
cx, cy, score, visibility = 0, 0, 0, 0 # keypoint not found
if keypoint_id != -1:
cx, cy, score = all_keypoints[int(keypoint_id), 0:3]
cx = cx + 0.5
cy = cy + 0.5
visibility = 1
keypoints[to_coco_map[position_id] * 3 + 0] = cx
keypoints[to_coco_map[position_id] * 3 + 1] = cy
keypoints[to_coco_map[position_id] * 3 + 2] = visibility
coco_keypoints.append(keypoints)
scores.append(person_score * max(0, (pose_entries[n][-1] - 1))) # -1 for 'neck'
return coco_keypoints, scores
def infer(net, img, scales, base_height, stride, pad_value=(0, 0, 0), img_mean=(128, 128, 128), img_scale=1/256):
normed_img = normalize(img, img_mean, img_scale)
height, width, _ = normed_img.shape
scales_ratios = [scale * base_height / float(height) for scale in scales]
avg_heatmaps = np.zeros((height, width, 19), dtype=np.float32)
avg_pafs = np.zeros((height, width, 38), dtype=np.float32)
for ratio in scales_ratios:
scaled_img = cv2.resize(normed_img, (0, 0), fx=ratio, fy=ratio, interpolation=cv2.INTER_CUBIC)
min_dims = [base_height, max(scaled_img.shape[1], base_height)]
padded_img, pad = pad_width(scaled_img, stride, pad_value, min_dims)
tensor_img = torch.from_numpy(padded_img).permute(2, 0, 1).unsqueeze(0).float().cuda()
stages_output = net(tensor_img)
stage2_heatmaps = stages_output[-2]
heatmaps = np.transpose(stage2_heatmaps.squeeze().cpu().data.numpy(), (1, 2, 0))
heatmaps = cv2.resize(heatmaps, (0, 0), fx=stride, fy=stride, interpolation=cv2.INTER_CUBIC)
heatmaps = heatmaps[pad[0]:heatmaps.shape[0] - pad[2], pad[1]:heatmaps.shape[1] - pad[3]:, :]
heatmaps = cv2.resize(heatmaps, (width, height), interpolation=cv2.INTER_CUBIC)
avg_heatmaps = avg_heatmaps + heatmaps / len(scales_ratios)
stage2_pafs = stages_output[-1]
pafs = np.transpose(stage2_pafs.squeeze().cpu().data.numpy(), (1, 2, 0))
pafs = cv2.resize(pafs, (0, 0), fx=stride, fy=stride, interpolation=cv2.INTER_CUBIC)
pafs = pafs[pad[0]:pafs.shape[0] - pad[2], pad[1]:pafs.shape[1] - pad[3], :]
pafs = cv2.resize(pafs, (width, height), interpolation=cv2.INTER_CUBIC)
avg_pafs = avg_pafs + pafs / len(scales_ratios)
return avg_heatmaps, avg_pafs
def evaluate(labels, output_name, images_folder, net, multiscale=False, visualize=False):
net = net.cuda().eval()
base_height = 368
scales = [1]
if multiscale:
scales = [0.5, 1.0, 1.5, 2.0]
stride = 8
dataset = CocoValDataset(labels, images_folder)
coco_result = []
for sample in dataset:
file_name = sample['file_name']
img = sample['img']
avg_heatmaps, avg_pafs = infer(net, img, scales, base_height, stride)
total_keypoints_num = 0
all_keypoints_by_type = []
for kpt_idx in range(18): # 19th for bg
total_keypoints_num += extract_keypoints(avg_heatmaps[:, :, kpt_idx], all_keypoints_by_type, total_keypoints_num)
pose_entries, all_keypoints = group_keypoints(all_keypoints_by_type, avg_pafs)
coco_keypoints, scores = convert_to_coco_format(pose_entries, all_keypoints)
image_id = int(file_name[0:file_name.rfind('.')])
for idx in range(len(coco_keypoints)):
coco_result.append({
'image_id': image_id,
'category_id': 1, # person
'keypoints': coco_keypoints[idx],
'score': scores[idx]
})
if visualize:
for keypoints in coco_keypoints:
for idx in range(len(keypoints) // 3):
cv2.circle(img, (int(keypoints[idx * 3]), int(keypoints[idx * 3 + 1])),
3, (255, 0, 255), -1)
cv2.imshow('keypoints', img)
key = cv2.waitKey()
if key == 27: # esc
return
with open(output_name, 'w') as f:
json.dump(coco_result, f, indent=4)
run_coco_eval(labels, output_name)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--labels', type=str, required=False, default='D:/code_data/coco/annotations/person_keypoints_val2017.json', help='path to json with keypoints val labels')
parser.add_argument('--output-name', type=str, default='output/detections.json',
help='name of output json file with detected keypoints')
parser.add_argument('--images-folder', type=str, required=False,default='D:/code_data/coco/val2017', help='path to COCO val images folder')
parser.add_argument('--checkpoint-path', type=str, required=False, default='D:/py/openpose_lightweight/weights/checkpoint_iter_370000.pth', help='path to the checkpoint')
parser.add_argument('--multiscale', action='store_true', help='average inference results over multiple scales')
parser.add_argument('--visualize', action='store_true', help='show keypoints')
args = parser.parse_args()
net = PoseEstimationWithMobileNet()
checkpoint = torch.load(args.checkpoint_path)
load_state(net, checkpoint)
evaluate(args.labels, args.output_name, args.images_folder, net, args.multiscale, args.visualize)