From f111cb002b9c6065fdf6bb274ce5857a9e875e8c Mon Sep 17 00:00:00 2001
From: chenshijun <csj_sky@126.com>
Date: 星期三, 05 六月 2019 15:38:49 +0800
Subject: [PATCH] face rectangle

---
 code/test_on_video.py |  149 +++++++++++++++++++++++++++++++------------------
 1 files changed, 94 insertions(+), 55 deletions(-)

diff --git a/code/test_on_video.py b/code/test_on_video.py
index 4fad440..0193035 100644
--- a/code/test_on_video.py
+++ b/code/test_on_video.py
@@ -1,4 +1,9 @@
+import sys, os, argparse
+
 import numpy as np
+import cv2
+import matplotlib.pyplot as plt
+
 import torch
 import torch.nn as nn
 from torch.autograd import Variable
@@ -8,10 +13,6 @@
 import torchvision
 import torch.nn.functional as F
 from PIL import Image
-
-import cv2
-import matplotlib.pyplot as plt
-import sys, os, argparse
 
 import datasets, hopenet, utils
 
@@ -25,6 +26,8 @@
     parser.add_argument('--video', dest='video_path', help='Path of video')
     parser.add_argument('--bboxes', dest='bboxes', help='Bounding box annotations of frames')
     parser.add_argument('--output_string', dest='output_string', help='String appended to output file')
+    parser.add_argument('--n_frames', dest='n_frames', help='Number of frames', type=int)
+    parser.add_argument('--fps', dest='fps', help='Frames per second of source video', type=float, default=30.)
     args = parser.parse_args()
     return args
 
@@ -45,24 +48,23 @@
     if not os.path.exists(args.video_path):
         sys.exit('Video does not exist')
 
-    # ResNet50 with 3 outputs.
+    # ResNet50 structure
     model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66)
-    # ResNet18
-    # model = hopenet.Hopenet(torchvision.models.resnet.BasicBlock, [2, 2, 2, 2], 66)
 
-    print 'Loading snapshot.'
+    print('Loading snapshot.')
     # Load snapshot
     saved_state_dict = torch.load(snapshot_path)
     model.load_state_dict(saved_state_dict)
 
-    print 'Loading data.'
+    print('Loading data.')
 
     transformations = transforms.Compose([transforms.Scale(224),
-    transforms.RandomCrop(224), transforms.ToTensor()])
+    transforms.CenterCrop(224), transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
 
     model.cuda(gpu)
 
-    print 'Ready to test network.'
+    print('Ready to test network.')
 
     # Test the Model
     model.eval()  # Change model to 'eval' mode (BN uses moving mean/var).
@@ -72,30 +74,42 @@
     idx_tensor = torch.FloatTensor(idx_tensor).cuda(gpu)
 
     video = cv2.VideoCapture(video_path)
+
+    # New cv2
     width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))   # float
     height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT)) # float
 
     # Define the codec and create VideoWriter object
     fourcc = cv2.VideoWriter_fourcc(*'MJPG')
-    out = cv2.VideoWriter('output/video/output-%s.avi' % args.output_string, fourcc, 30.0, (width, height))
+    out = cv2.VideoWriter('output/video/output-%s.avi' % args.output_string, fourcc, args.fps, (width, height))
 
-    bbox_file = open(args.bboxes, 'r')
+    # # Old cv2
+    # width = int(video.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH))   # float
+    # height = int(video.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT)) # float
+    #
+    # # Define the codec and create VideoWriter object
+    # fourcc = cv2.cv.CV_FOURCC(*'MJPG')
+    # out = cv2.VideoWriter('output/video/output-%s.avi' % args.output_string, fourcc, 30.0, (width, height))
+
+    txt_out = open('output/video/output-%s.txt' % args.output_string, 'w')
+
     frame_num = 1
 
-    # TODO: support for several bounding boxes
-    for line in bbox_file:
+    with open(args.bboxes, 'r') as f:
+        bbox_line_list = f.read().splitlines()
+
+    idx = 0
+    while idx < len(bbox_line_list):
+        line = bbox_line_list[idx]
         line = line.strip('\n')
         line = line.split(' ')
         det_frame_num = int(line[0])
 
-        print frame_num
+        print(frame_num)
 
         # Stop at a certain frame number
-        if frame_num > 10000:
-            out.release()
-            video.release()
-            bbox_file.close()
-            sys.exit(0)
+        if frame_num > args.n_frames:
+            break
 
         # Save all frames as they are if they don't have bbox annotation.
         while frame_num < det_frame_num:
@@ -103,50 +117,75 @@
             if ret == False:
                 out.release()
                 video.release()
-                bbox_file.close()
+                txt_out.close()
                 sys.exit(0)
-            out.write(frame)
+            # out.write(frame)
             frame_num += 1
 
+        # Start processing frame with bounding box
         ret,frame = video.read()
         if ret == False:
-            out.release()
-            video.release()
-            bbox_file.close()
-            sys.exit(0)
+            break
+        cv2_frame = cv2.cvtColor(frame,cv2.COLOR_BGR2RGB)
 
-        x_min, y_min, x_max, y_max = int(line[1]), int(line[2]), int(line[3]), int(line[4])
-        # Crop image
-        img = frame[y_min:y_max,x_min:x_max]
-        img = Image.fromarray(img)
+        while True:
+            x_min, y_min, x_max, y_max = int(float(line[1])), int(float(line[2])), int(float(line[3])), int(float(line[4]))
 
-        # Transform
-        img = transformations(img)
-        img_shape = img.size()
-        img = img.view(1, img_shape[0], img_shape[1], img_shape[2])
-        img = Variable(img).cuda(gpu)
-        yaw, pitch, roll = model(img)
+            bbox_width = abs(x_max - x_min)
+            bbox_height = abs(y_max - y_min)
+            # x_min -= 3 * bbox_width / 4
+            # x_max += 3 * bbox_width / 4
+            # y_min -= 3 * bbox_height / 4
+            # y_max += bbox_height / 4
+            x_min -= 50
+            x_max += 50
+            y_min -= 50
+            y_max += 30
+            x_min = max(x_min, 0)
+            y_min = max(y_min, 0)
+            x_max = min(frame.shape[1], x_max)
+            y_max = min(frame.shape[0], y_max)
+            # Crop face loosely
+            img = cv2_frame[y_min:y_max,x_min:x_max]
+            img = Image.fromarray(img)
 
-        yaw_predicted = F.softmax(yaw)
-        pitch_predicted = F.softmax(pitch)
-        roll_predicted = F.softmax(roll)
-        # Get continuous predictions in degrees.
-        yaw_predicted = torch.sum(yaw_predicted.data[0] * idx_tensor) * 3 - 99
-        pitch_predicted = torch.sum(pitch_predicted.data[0] * idx_tensor) * 3 - 99
-        roll_predicted = torch.sum(roll_predicted.data[0] * idx_tensor) * 3 - 99
+            # Transform
+            img = transformations(img)
+            img_shape = img.size()
+            img = img.view(1, img_shape[0], img_shape[1], img_shape[2])
+            img = Variable(img).cuda(gpu)
 
-        # Print new frame with cube and TODO: axis
-        utils.plot_pose_cube(frame, yaw_predicted, pitch_predicted, roll_predicted, (x_min + x_max) / 2, (y_min + y_max) / 2, size = 200)
-        out.write(frame)
+            yaw, pitch, roll = model(img)
 
-        frame_num += 1
+            yaw_predicted = F.softmax(yaw)
+            pitch_predicted = F.softmax(pitch)
+            roll_predicted = F.softmax(roll)
+            # Get continuous predictions in degrees.
+            yaw_predicted = torch.sum(yaw_predicted.data[0] * idx_tensor) * 3 - 99
+            pitch_predicted = torch.sum(pitch_predicted.data[0] * idx_tensor) * 3 - 99
+            roll_predicted = torch.sum(roll_predicted.data[0] * idx_tensor) * 3 - 99
 
-    while True:
-        ret, frame = video.read()
-        if ret == False:
-            out.release()
-            video.release()
-            bbox_file.close()
-            sys.exit(0)
+            # print(new frame with cube and axis
+            txt_out.write(str(frame_num) + ' %f %f %f\n' % (yaw_predicted, pitch_predicted, roll_predicted))
+            # utils.plot_pose_cube(frame, yaw_predicted, pitch_predicted, roll_predicted, (x_min + x_max) / 2, (y_min + y_max) / 2, size = bbox_width)
+            utils.draw_axis(frame, yaw_predicted, pitch_predicted, roll_predicted, tdx = (x_min + x_max) / 2, tdy= (y_min + y_max) / 2, size = bbox_height/2)
+            # Plot expanded bounding box
+            # cv2.rectangle(frame, (x_min, y_min), (x_max, y_max), (0,255,0), 1)
+
+            # Peek next frame detection
+            next_frame_num = int(bbox_line_list[idx+1].strip('\n').split(' ')[0])
+            # print('next_frame_num ', next_frame_num
+            if next_frame_num == det_frame_num:
+                idx += 1
+                line = bbox_line_list[idx].strip('\n').split(' ')
+                det_frame_num = int(line[0])
+            else:
+                break
+
+        idx += 1
         out.write(frame)
         frame_num += 1
+
+    out.release()
+    video.release()
+    txt_out.close()

--
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