update yolo

.gitignore

@@ -18,5 +18,6 @@ ssd/examples
 *.json
 *.h5
 *.zip
+*.weights
 
 coco-minival/

README.md

@@ -28,13 +28,13 @@ To match poses that correspond to the same person across frames, we also provide
   ./install.sh
   ```
 
-1. Download the models manually: **ssd_coco.pth**([Google Drive](https://drive.google.com/open?id=1ifUNnxSDOP7InBtKy5CGnRAMLD4-d4Ct) | [Baidu pan](https://pan.baidu.com/s/1mon7Ht6ObqrS2ZY955swpg)), **pyra_4.pth** ([Google Drive](https://drive.google.com/open?id=1oG1Fxj4oBfKwD1W_2QObxltWybuIk7Y6) | [Baidu pan](https://pan.baidu.com/s/14ONL_T_d1twm9Lxac5x-Ew)). Place them into `./models/ssd` and `./models/sppe` respectively.
+1. Download the models manually: **ssd_coco.pth**([Google Drive](https://drive.google.com/open?id=1ifUNnxSDOP7InBtKy5CGnRAMLD4-d4Ct) | [Baidu pan](https://pan.baidu.com/s/1mon7Ht6ObqrS2ZY955swpg)), **pyra_4.pth** ([Google Drive](https://drive.google.com/open?id=1oG1Fxj4oBfKwD1W_2QObxltWybuIk7Y6) | [Baidu pan](https://pan.baidu.com/s/14ONL_T_d1twm9Lxac5x-Ew)), **yolov3.weights**([Google Drive](https://drive.google.com/open?id=1yjrziA2RzFqWAQG4Qq7XN0vumsMxwSjS) | [Baidu pan](https://pan.baidu.com/s/108SjV-uIJpxnqDMT19v-Aw)). Place them into `./models/ssd`, `./models/sppe` and `./models/yolo` respectively.
 
 
 ## Quick Start
 - **Demo**:  Run AlphaPose for all images in a folder and visualize the results with:
 ```
-python demo_fast.py \
+python demo_yolo.py \
 --inputlist ./list-coco-minival500.txt \
 --imgpath ${img_directory} \
 --outputpath ./coco-minival

SPPE/src/utils/eval.py

@@ -141,7 +141,7 @@ def getPrediction(hms, pt1, pt2, inpH, inpW, resH, resW):
     # preds += 0.5
 
     preds_tf = torch.zeros(preds.size())
-    
+
     preds_tf = transformBoxInvert_batch(preds, pt1, pt2, inpH, inpW, resH, resW)
 
     return preds, preds_tf, maxval

demo_yolo.py

@@ -34,7 +34,7 @@
     # Load YOLO model
     print('Loading YOLO model..')
     det_model = Darknet("yolo/cfg/yolov3.cfg")
-    det_model.load_weights('yolo/yolov3.weights')
+    det_model.load_weights('models/yolo/yolov3.weights')
     det_model.net_info['height'] = args.inp_dim
     det_inp_dim = int(det_model.net_info['height'])
     assert det_inp_dim % 32 == 0

yolo/README.md

@@ -0,0 +1,93 @@
+# A PyTorch implementation of a YOLO v3 Object Detector
+
+[UPDATE] : This repo serves as a driver code for my research. I just graduated college, and am very busy looking for research internship / fellowship roles before eventually applying for a masters. I won't have the time to look into issues for the time being. Thank you.
+
+
+This repository contains code for a object detector based on [YOLOv3: An Incremental Improvement](https://pjreddie.com/media/files/papers/YOLOv3.pdf), implementedin PyTorch. The code is based on the official code of [YOLO v3](https://github.com/pjreddie/darknet), as well as a PyTorch 
+port of the original code, by [marvis](https://github.com/marvis/pytorch-yolo2). One of the goals of this code is to improve
+upon the original port by removing redundant parts of the code (The official code is basically a fully blown deep learning 
+library, and includes stuff like sequence models, which are not used in YOLO). I've also tried to keep the code minimal, and 
+document it as well as I can. 
+
+### Tutorial for building this detector from scratch
+If you want to understand how to implement this detector by yourself from scratch, then you can go through this very detailed 5-part tutorial series I wrote on Paperspace. Perfect for someone who wants to move from beginner to intermediate pytorch skills. 
+
+[Implement YOLO v3 from scratch](https://blog.paperspace.com/how-to-implement-a-yolo-object-detector-in-pytorch/)
+
+As of now, the code only contains the detection module, but you should expect the training module soon. :) 
+
+## Requirements
+1. Python 3.5
+2. OpenCV
+3. PyTorch 0.4
+
+Using PyTorch 0.3 will break the detector.
+
+
+
+## Detection Example
+
+![Detection Example](https://i.imgur.com/m2jwneng.png)
+## Running the detector
+
+### On single or multiple images
+
+Clone, and `cd` into the repo directory. The first thing you need to do is to get the weights file
+This time around, for v3, authors has supplied a weightsfile only for COCO [here](https://pjreddie.com/media/files/yolov3.weights), and place 
+
+the weights file into your repo directory. Or, you could just type (if you're on Linux)
+
+```
+wget https://pjreddie.com/media/files/yolov3.weights 
+python detect.py --images imgs --det det 
+```
+
+
+`--images` flag defines the directory to load images from, or a single image file (it will figure it out), and `--det` is the directory
+to save images to. Other setting such as batch size (using `--bs` flag) , object threshold confidence can be tweaked with flags that can be looked up with. 
+
+```
+python detect.py -h
+```
+
+### Speed Accuracy Tradeoff
+You can change the resolutions of the input image by the `--reso` flag. The default value is 416. Whatever value you chose, rememeber **it should be a multiple of 32 and greater than 32**. Weird things will happen if you don't. You've been warned. 
+
+```
+python detect.py --images imgs --det det --reso 320
+```
+
+### On Video
+For this, you should run the file, video_demo.py with --video flag specifying the video file. The video file should be in .avi format
+since openCV only accepts OpenCV as the input format. 
+
+```
+python video_demo.py --video video.avi
+```
+
+Tweakable settings can be seen with -h flag. 
+
+### Speeding up Video Inference
+
+To speed video inference, you can try using the video_demo_half.py file instead which does all the inference with 16-bit half 
+precision floats instead of 32-bit float. I haven't seen big improvements, but I attribute that to having an older card 
+(Tesla K80, Kepler arch). If you have one of cards with fast float16 support, try it out, and if possible, benchmark it. 
+
+### On a Camera
+Same as video module, but you don't have to specify the video file since feed will be taken from your camera. To be precise, 
+feed will be taken from what the OpenCV, recognises as camera 0. The default image resolution is 160 here, though you can change it with `reso` flag.
+
+```
+python cam_demo.py
+```
+You can easily tweak the code to use different weightsfiles, available at [yolo website](https://pjreddie.com/darknet/yolo/)
+
+NOTE: The scales features has been disabled for better refactoring.
+### Detection across different scales
+YOLO v3 makes detections across different scales, each of which deputise in detecting objects of different sizes depending upon whether they capture coarse features, fine grained features or something between. You can experiment with these scales by the `--scales` flag. 
+
+```
+python detect.py --scales 1,3
+```
+
+

yolo/bbox.py

@@ -0,0 +1,115 @@
+from __future__ import division
+
+import torch 
+import random
+
+import numpy as np
+import cv2
+
+def confidence_filter(result, confidence):
+    conf_mask = (result[:,:,4] > confidence).float().unsqueeze(2)
+    result = result*conf_mask    
+
+    return result
+
+def confidence_filter_cls(result, confidence):
+    max_scores = torch.max(result[:,:,5:25], 2)[0]
+    res = torch.cat((result, max_scores),2)
+    print(res.shape)
+
+
+    cond_1 = (res[:,:,4] > confidence).float()
+    cond_2 = (res[:,:,25] > 0.995).float()
+
+    conf = cond_1 + cond_2
+    conf = torch.clamp(conf, 0.0, 1.0)
+    conf = conf.unsqueeze(2)
+    result = result*conf   
+    return result
+
+
+
+def get_abs_coord(box):
+    box[2], box[3] = abs(box[2]), abs(box[3])
+    x1 = (box[0] - box[2]/2) - 1 
+    y1 = (box[1] - box[3]/2) - 1 
+    x2 = (box[0] + box[2]/2) - 1 
+    y2 = (box[1] + box[3]/2) - 1
+    return x1, y1, x2, y2
+
+
+
+def sanity_fix(box):
+    if (box[0] > box[2]):
+        box[0], box[2] = box[2], box[0]
+
+    if (box[1] >  box[3]):
+        box[1], box[3] = box[3], box[1]
+
+    return box
+
+def bbox_iou(box1, box2):
+    """
+    Returns the IoU of two bounding boxes 
+    
+    
+    """
+    #Get the coordinates of bounding boxes
+    b1_x1, b1_y1, b1_x2, b1_y2 = box1[:,0], box1[:,1], box1[:,2], box1[:,3]
+    b2_x1, b2_y1, b2_x2, b2_y2 = box2[:,0], box2[:,1], box2[:,2], box2[:,3]
+
+    #get the corrdinates of the intersection rectangle
+    inter_rect_x1 =  torch.max(b1_x1, b2_x1)
+    inter_rect_y1 =  torch.max(b1_y1, b2_y1)
+    inter_rect_x2 =  torch.min(b1_x2, b2_x2)
+    inter_rect_y2 =  torch.min(b1_y2, b2_y2)
+
+    #Intersection area
+    if torch.cuda.is_available():
+            inter_area = torch.max(inter_rect_x2 - inter_rect_x1 + 1,torch.zeros(inter_rect_x2.shape).cuda())*torch.max(inter_rect_y2 - inter_rect_y1 + 1, torch.zeros(inter_rect_x2.shape).cuda())
+    else:
+            inter_area = torch.max(inter_rect_x2 - inter_rect_x1 + 1,torch.zeros(inter_rect_x2.shape))*torch.max(inter_rect_y2 - inter_rect_y1 + 1, torch.zeros(inter_rect_x2.shape))
+
+    #Union Area
+    b1_area = (b1_x2 - b1_x1 + 1)*(b1_y2 - b1_y1 + 1)
+    b2_area = (b2_x2 - b2_x1 + 1)*(b2_y2 - b2_y1 + 1)
+
+    iou = inter_area / (b1_area + b2_area - inter_area)
+
+    return iou
+
+
+def pred_corner_coord(prediction):
+    #Get indices of non-zero confidence bboxes
+    ind_nz = torch.nonzero(prediction[:,:,4]).transpose(0,1).contiguous()
+
+    box = prediction[ind_nz[0], ind_nz[1]]
+
+
+    box_a = box.new(box.shape)
+    box_a[:,0] = (box[:,0] - box[:,2]/2)
+    box_a[:,1] = (box[:,1] - box[:,3]/2)
+    box_a[:,2] = (box[:,0] + box[:,2]/2) 
+    box_a[:,3] = (box[:,1] + box[:,3]/2)
+    box[:,:4] = box_a[:,:4]
+
+    prediction[ind_nz[0], ind_nz[1]] = box
+
+    return prediction
+
+
+
+
+def write(x, batches, results, colors, classes):
+    c1 = tuple(x[1:3].int())
+    c2 = tuple(x[3:5].int())
+    img = results[int(x[0])]
+    cls = int(x[-1])
+    label = "{0}".format(classes[cls])
+    color = random.choice(colors)
+    cv2.rectangle(img, c1, c2,color, 1)
+    t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 1 , 1)[0]
+    c2 = c1[0] + t_size[0] + 3, c1[1] + t_size[1] + 4
+    cv2.rectangle(img, c1, c2,color, -1)
+    cv2.putText(img, label, (c1[0], c1[1] + t_size[1] + 4), cv2.FONT_HERSHEY_PLAIN, 1, [225,255,255], 1);
+    return img