adds playground/yolo9 #59

* adds readme and demo python script; * updates for pyVEs/aiVE; * .gitignore yolov9-c.pt #98.37M; adds opencv/examples/python/webcam; decreasing resolution improve FPS

.gitignore

@@ -35,6 +35,10 @@ checkpoints/
 **/VGG18_dropout_earlystopping_training_history.pkl
 **/VGG18_dropout_training_history_lr_scheduler.pkl
 
+## ignores others models
+yolov9-c.pt
+
+
 ##VTK
 **/Frog
 FullHead.mhd

opencv/examples/python/webcam/script.py

@@ -0,0 +1,24 @@
+# https://docs.opencv.org/4.x/dd/d43/tutorial_py_video_display.html
+import numpy as np
+import cv2 as cv
+cap = cv.VideoCapture(0)
+if not cap.isOpened():
+    print("Cannot open camera")
+    exit()
+while True:
+    # Capture frame-by-frame
+    ret, frame = cap.read()
+    # if frame is read correctly ret is True
+    if not ret:
+        print("Can't receive frame (stream end?). Exiting ...")
+        break
+    # Our operations on the frame come here
+    gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
+    # Display the resulting frame
+    cv.imshow('frame', gray)
+    if cv.waitKey(1) == ord('q'):
+        break
+# When everything done, release the capture
+cap.release()
+
+

playground/yolo/README.md

@@ -0,0 +1,12 @@
+# YOLO
+
+## Yolo9
+```
+mamba activate aiVE
+wget https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-c.pt #98.37M  2.76MB/s    in 37s
+python demoyolo9.py
+```
+
+## References 
+https://github.com/kadirnar/yolov9-pip 
+https://github.com/WongKinYiu/yolov9  

playground/yolo/demoyolo9.py

@@ -0,0 +1,82 @@
+import numpy as np
+import yolov9
+import cv2
+import time
+
+def generate_colors(n): 
+  rgb_values = []
+  r,g,b = 0,50,100
+  step = 256 / n 
+  for _ in range(n): 
+    r += step 
+    g += step 
+    b += step 
+    r = int(r) % 256 
+    g = int(g) % 256 
+    b = int(b) % 256 
+    rgb_values.append((r,g,b)) 
+  return rgb_values
+
+model = yolov9.load(
+    "yolov9-c.pt",
+    device="cpu",
+)
+
+# set model parameters
+model.conf = 0.25  # NMS confidence threshold
+model.iou = 0.45  # NMS IoU threshold
+model.classes = None  # (optional list) filter by class
+# print(model.names, model.names[1]) # categories names
+colors = generate_colors(len(model.names))
+
+# set camera capture:
+camera_id = 0
+#frameWidth = 640
+#frameHeight = 480
+frameWidth = 160  #640/4
+frameHeight = 120  #480/4
+#frameWidth = 80  #640/8
+#frameHeight = 60  #480/4
+
+cap = cv2.VideoCapture(camera_id)
+cap.set(3, frameWidth)
+cap.set(4, frameHeight)
+
+while True:
+    ret, frame = cap.read()
+    if not ret:
+        break
+
+    ## perform inference
+    t = time.time()
+    #results = model(frame)
+    results = model(frame, size=frameWidth)
+    t = time.time() - t
+
+    # parse results
+    predictions = results.pred[0]
+    boxes = predictions[:, :4]  # x1, y1, x2, y2
+    scores = predictions[:, 4]
+    categories = predictions[:, 5]
+    bboxes = np.array(boxes, dtype="int")
+    classes = np.array(categories, dtype="int")
+
+    ## show detection bounding boxes on image
+    for cls, bbox in zip(classes, bboxes):
+        (x, y, x2, y2) = bbox
+        cv2.rectangle(frame, (x, y), (x2, y2), colors[cls], 2)
+        cv2.putText(frame, model.names[cls], (x, y - 5), 
+                    cv2.FONT_HERSHEY_PLAIN, 2, colors[cls], 2)
+        cv2.putText(frame, "FPS: "+str(1/t), (10, 30), 
+                    cv2.FONT_HERSHEY_PLAIN, 1.5, (0,0,255), 2)
+
+    # results.show()
+    cv2.imshow("Img", frame)
+
+    key = cv2.waitKey(1)
+    if key == 27:
+        break
+
+cap.release()
+cv2.destroyAllWindows()
+

pyVEs/aiVE.yml

@@ -1,12 +1,11 @@
 ## USAGE
-## cd $HOME/repositories/code/conda/create-virtual-environments
-## conda update --all                           ## UPDATE ALL base PACKAGES
-## conda update -n base -c defaults conda  ## UPDATE CONDA
-## conda list -n aiVE # show list of installed packages
-## conda env create -f aiVE.yml   		    ## INSTALL
-## conda env update -f aiVE.yml --prune  	## UPDATE
-## conda activate aiVE   			            ## ACTIVATE
-## conda remove -n aiVE --all			        ## REMOVE
+## LIST MAMBA ENVS: mamba list -n *VE # show list of installed packages
+## UPDATE MAMBA: mamba update -n base mamba
+## INSTALL MAMBA EV: mamba env create -f *VE.yml
+## UPDATE MAMBA ENV: mamba env update -f *VE.yml --prune
+## ACTIVATE MAMBA ENV: mamba activate *VE
+## REMOVE MAMBA ENV: mamba remove -n *VE --all
+## mamba env update --prune -n *VE -f eVE.yml
 
 ## QUICK TEST OF THE AVAILABILITY OF CUDA
 ## conda activate aiVE
@@ -23,7 +22,8 @@ channels:
   #- huggingface # datasets
   #- anaconda # seaborn; pandas; scikit-learn; jupyter
 dependencies:
-  - python=3.10.*
+  #- python=3.10.*
+  - python=3.11.*
   #- pytorch=1.13.* #=1.11.*
   #- torchvision #=0.12.*
   #- cudatoolkit #=11.*.*
@@ -32,11 +32,14 @@ dependencies:
     # pip install torch torchvision networkx ##<<<<FOR TORCH 1.13 MAKE USE OF THE TERMINAL
     - matplotlib
     - numpy
-    - notebook
-    - jupyter
-    - jupyter_contrib_nbextensions 
-    - pillow
-    - av
+    - yolov9pip
+    ### VERSIONS of opencv-contrib-python-headless  https://pypi.org/project/opencv-contrib-python-headless/#history
+    #- opencv-contrib-python-headless
+    - opencv-python
+    #- notebook
+    #- jupyter
+    #- pillow
+    #- av
     #- opencv-python
     #- einops #https://github.com/arogozhnikov/einops
     #- seaborn