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detect_one.py
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detect_one.py
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# -*- coding: UTF-8 -*-
import argparse
import time
from pathlib import Path
import cv2
import torch
import torch.backends.cudnn as cudnn
from numpy import random
import copy
import numpy as np
from models.experimental import attempt_load
from utils.datasets import LoadStreams, LoadImages, letterbox
from utils.general import check_img_size, non_max_suppression_landmark, apply_classifier, scale_coords, xyxy2xywh, \
strip_optimizer, set_logging, increment_path
from utils.plots import plot_one_box
from utils.torch_utils import select_device, load_classifier, time_synchronized
from models.yolo import Model
import yaml
def load_model(weights, cfg_path, device):
model = attempt_load(weights, map_location=device) # load FP32 model
#model = Model(cfg_path, ch=3, nc=1 ).to(device) # create
#ckpt = torch.load(weights, map_location='cpu') # load
#state_dict = ckpt['model'].float().state_dict()
#model.load_state_dict(state_dict)
#model.eval()
return model
def scale_coords_landmarks(img1_shape, coords, img0_shape, ratio_pad=None):
# Rescale coords (xyxy) from img1_shape to img0_shape
if ratio_pad is None: # calculate from img0_shape
gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new
pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding
else:
gain = ratio_pad[0][0]
pad = ratio_pad[1]
coords[:, [0, 2, 4, 6]] -= pad[0] # x padding
coords[:, [1, 3, 5, 7]] -= pad[1] # y padding
coords[:, :8] /= gain
#clip_coords(coords, img0_shape)
coords[:, 0].clamp_(0, img0_shape[1]) # x1
coords[:, 1].clamp_(0, img0_shape[0]) # y1
coords[:, 2].clamp_(0, img0_shape[1]) # x2
coords[:, 3].clamp_(0, img0_shape[0]) # y2
coords[:, 4].clamp_(0, img0_shape[1]) # x3
coords[:, 5].clamp_(0, img0_shape[0]) # y3
coords[:, 6].clamp_(0, img0_shape[1]) # x4
coords[:, 7].clamp_(0, img0_shape[0]) # y4
return coords
def show_results(img, xywh, conf, landmarks, class_num):
h,w,c = img.shape
tl = 1 or round(0.002 * (h + w) / 2) + 1 # line/font thickness
x1 = int(xywh[0] * w - 0.5 * xywh[2] * w)
y1 = int(xywh[1] * h - 0.5 * xywh[3] * h)
x2 = int(xywh[0] * w + 0.5 * xywh[2] * w)
y2 = int(xywh[1] * h + 0.5 * xywh[3] * h)
cv2.rectangle(img, (x1,y1), (x2, y2), (0,255,0), thickness=tl, lineType=cv2.LINE_AA)
clors = [(255,0,0),(0,255,0),(0,0,255),(255,255,0),(0,255,255)]
for i in range(4):
point_x = int(landmarks[2 * i] * w)
point_y = int(landmarks[2 * i + 1] * h)
cv2.circle(img, (point_x, point_y), tl+1, clors[i], -1)
tf = max(tl - 1, 1) # font thickness
label = str(int(class_num)) + ': ' + str(conf)[:5]
cv2.putText(img, label, (x1, y1 - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
return img
def warpimage(img, pts1):
# right_bottom, left_bottom, left up , left_up
#pts1 = np.float32([[56,65],[368,52],[28,387],[389,390]])
pts2 = np.float32([[400,200],[0,200],[0,0],[400,0]])
h, w ,c = img.shape
M = cv2.getPerspectiveTransform(pts1,pts2)
dst = cv2.warpPerspective(img,M,(400,200))
return dst
def detect_one(model, image_path, device):
# Load model
img_size = 640
conf_thres = 0.3
iou_thres = 0.5
orgimg = cv2.imread(image_path) # BGR
img0 = copy.deepcopy(orgimg)
assert orgimg is not None, 'Image Not Found ' + image_path
h0, w0 = orgimg.shape[:2] # orig hw
r = img_size / max(h0, w0) # resize image to img_size
if r != 1: # always resize down, only resize up if training with augmentation
interp = cv2.INTER_AREA if r < 1 else cv2.INTER_LINEAR
img0 = cv2.resize(img0, (int(w0 * r), int(h0 * r)), interpolation=interp)
imgsz = check_img_size(img_size, s=model.stride.max()) # check img_size
img = letterbox(img0, new_shape=imgsz)[0]
# Convert
img = img[:, :, ::-1].transpose(2, 0, 1).copy() # BGR to RGB, to 3x416x416
# Run inference
t0 = time.time()
img = torch.from_numpy(img).to(device)
img = img.float() # uint8 to fp16/32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
# Inference
t1 = time_synchronized()
pred = model(img)[0]
print('pred: ', pred.shape)
# Apply NMS
pred = non_max_suppression_landmark(pred, conf_thres, iou_thres)
print('nms: ', pred)
t2 = time_synchronized()
print('img.shape: ', img.shape)
print('orgimg.shape: ', orgimg.shape)
# Process detections
for i, det in enumerate(pred): # detections per image
gn = torch.tensor(orgimg.shape)[[1, 0, 1, 0]].to(device) # normalization gain whwh
gn_lks = torch.tensor(orgimg.shape)[[1, 0, 1, 0, 1, 0, 1, 0]].to(device) # normalization gain landmarks
if len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], orgimg.shape).round()
# Print results
for c in det[:, -1].unique():
n = (det[:, -1] == c).sum() # detections per class
det[:, 5:13] = scale_coords_landmarks(img.shape[2:], det[:, 5:13], orgimg.shape).round()
for j in range(det.size()[0]):
xywh = (xyxy2xywh(torch.tensor(det[j, :4]).view(1, 4)) / gn).view(-1).tolist()
conf = det[j, 4].cpu().numpy()
landmarks = (det[j, 5:13].view(1, 8) / gn_lks).view(-1).tolist()
class_num = det[j, 13].cpu().numpy()
orgimg = show_results(orgimg, xywh, conf, landmarks, class_num)
#h,w,c = orgimg.shape
#points = np.array(landmarks, dtype=np.float32).reshape(-1, 2)
#points[:, 0] = points[:, 0] * w
#points[:, 1] = points[:, 1] * h
#print('points: ', points)
#dst = warpimage(orgimg, points)
#cv2.imwrite('./result_warp.jpg', dst)
# Stream results
print(f'Done. ({time.time() - t0:.3f}s)')
#cv2.imshow('orgimg', orgimg)
cv2.imwrite('./result.jpg', orgimg)
#if cv2.waitKey(0) == ord('q'): # q to quit
# raise StopIteration
if __name__ == '__main__':
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
weights = './weights/last.pt'
cfg_path = './models/yolov5s.yaml'
model = load_model(weights, cfg_path, device)
root = '/home/xialuxi/work/dukto/data/CCPD2020/CCPD2020/images/test/'
image_path = '/home/xialuxi/work/download/30202039157.jpg'
detect_one(model, image_path, device)
print('over')